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Professor Alfredo Huete

Biography

My main research interest is in using remote sensing to study and analyse broad scale vegetation health and functioning. I use satellite data to observe land surface responses and interactions with climate, land use activities, and major disturbance events. I also look at vegetation dynamics, landscape phenology processes, and their shifting seasonalities with climate variability. Recently, I used remote sensing and field measurements to understand the phenology patterns of tropical rainforests and savannas in the Amazon and Southeast Asia and my Amazon work was featured in a National Geographic television special entitled "The Big Picture".

Currently my research involves coupling eddy covariance tower flux measurements with ground spectral sensors and satellite observations to study carbon and water cycling across Australian landscapes.

I am actively involved with several international space programs, including the NASA-EOS MODIS Science Team, the Japanese JAXA GCOM-SGLI Science Team, the European PROBA-V User Expert Group, and NPOESS-VIIRS advisory group.

Within the Plant Functional Biology and Climate Change ClusterI (C3) I am the theme leader of the Remote Sensing Research Group

Professional

Member, International Society for Photogrammetry and Remote Sensing
Member, American Geophysical Union
Member, Surveying & Spatial Sciences Institute
Member, The Institution of Electrical and Electronic Engineers
Member, Association for Tropical Biology and Conservation

Image of Alfredo Huete
Professor, Faculty of Science
Core Member, Plant Functional Biology & Climate Change
B.Sc, M.Sc, Ph.D
Member, The Institution of Electrical and Electronic Engineers
Member, International Society for Photogrammetry and Remote Sensing
Member, American Geophysical Union
 
Phone
+61 2 9514 4084
Room
CB04.05.54

Research Interests

  • Ecosystem responses to climate forcings and drought
  • Functional phenology measures and climate-induced biome shifts in seasonality
  • Satellite - flux tower integrations for carbon and water models
  • Tropical savanna and rainforest dynamics
  • Land-ocean interactions and feedback
  • Phenology (pollen) - public health studies

Other projects:
  • Development of an AusCover- Sydney Node, TERN Education Investment Fund (EIF),
  • Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe, COST Action program, ES0903.
  • Scaling photosynthesis in tropical systems: from forest to savanna, from seasons to extreme events, NASA, co-CI,
  • Development of SGLI Time Series Vegetation Indices for Long Term Climate Studies, Collaborative research agreement for the Global Change Observation Mission, Japan Aerospace Exploration Agency (JAXA),
  • Evaluation and Validation of NPP VIIRS Vegetation Index EDR for Earth System and Climate Sciences, NASA, co-I.
  • Integration of Airborne Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems, NASA Applied Sciences- Public Health, co-I.
  • New Approaches for Vegetation Index Time Series Evaluation, NOAA-Univ of Maryland.

Can supervise: Yes

Books

Chuvieco, E. & Huete, A. 2009, Fundamentals of Satellite Remote Sensing, 1st, CRC/Dekker, Taylor & Francis Informa Group, Boca Raton, Florida USA.
Qi, J., Moran, M.S., Huete, A.R., Jackson, R.D. & Chehbouni, A. 1991, View-atmosphere-soil effects on vegetation indices derived from SPOT images, ESA, available ESTEC, Noordwijk; ESA SP-319.
A temporal sequence of 15 SPOT HRV images and aircraft data were obtained over cotton at the Maricopa Agricultural Center, Arizona from April to October 1989. The SPOT data included different view angles (-28 to +24) and the aircraft data were obtained with a nadir-viewing radiometer equipped with SPOT filters. View angle/direction, atmosphere, and soil influences were observed in the SPOT data. The relative magnitude among the 3 influences was dependent on surface conditions, varied with canopy growth, and was different for reflectances vs. vegetation indices (VI). View effects were most pronounced with the red and NIR bands but became secondary with use of VI's. Soil and view variations on VI's were most important in partial canopies while atmospheric influences became dominant with increase in vegetation. -Authors
Escadafal, R. & Huete, A.R. 1991, Influence of the viewing geometry on the spectral properties (high resolution visible and NIR) of selected soils from Arizona, ESA, available ESTEC, Noordwijk; ESA SP-319.
The influence of view angle on the spectral signature of bare soils was investigated during a field campaign over five sites at the Walnut Gulch Experimental Watershed, in semiarid Arizona. Bidirectional reflectance factors (BRF) in the 400 to 900 nm wavelength range and C.I.E. color coefficients were computed from measurements made along the principal plane of the sun with a portable spectroradiometer. -from Authors
Huete, A.R., Qi, J., Chehbouni, A., Leeuwen, W. & Hua, G. 1991, Normalization of multidirectional red and NIR reflectances with the SAVI, ESA, available ESTEC, Noordwijk; ESA SP-319.
Directional reflectance measurements were made over a semi-desert gramma (Bouteloua spp.) grassland at various times of the growing season. View angle measurements from +40 to -40 were made at various solar zenith angles and soil moisture conditions. The sensitivity of the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) to bidirectional measurements was assessed for purposes of improving remote temporal monitoring of vegetation dynamics. -from Authors
Huete, A.R. 1988, Soil spectral filtering for improved biomass assessment in arid ecosystems, Belhaven Press (Pinter.
Remote sensing of agriculture, forest and rangeland frequently involves the measurement of two or more components (plants, soil, atmosphere, etc) in the presence of each other. In this study, factor analysis is used to filter the soil background response and extract the spectra of the vegetation from measurements made over a desert grassland. The extracted vegetation spectra are compared to the original spectra as to their ability to assess green biomass. Green biomass sensitivity is found to be significantly improved with the use of factor analytic separation techniques. -from Author

Chapters

Huete, A., Miura, T., Yoshioka, H., Ratana, P. & Broich, M. 2014, 'Indices of vegetation activity' in Hanes, J.M. (ed), Biophysical Applications of Satellite Remote Sensing, Springer, New York, pp. 1-41.
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Glenn, E.P., Nagler, P.L. & Huete, A. 2014, 'Change detection using vegetation indices and multiplatform satellite imagery at multiple temporal and spatial scales' in Qihao Weng (ed), Scale issues in remote sensing, John Wiley & Sons, Inc., New Jersey, pp. 81-107.
Huete, A. 2012, 'Soil Properties' in Njoku Eni, G. (ed), Encyclopedia of Remote Sensing, Springer, Germany, pp. 1-6.
Soils. Soils are three-dimensional living bodies, with spatially variable biologic, physical, and chemical properties, that form the outer skin of the Earths terrestrial surface. Soil formation. Soils form slowly over time and develop distinguishing properties as a function of climate, geologic and organic parent materials, topography, time, vegetation type, and land use history. Soil profile. The vertical depth of a soil body varies from a few centimeters up to several meters and contain a series of soil horizons. The surface layers are termed the O (organic) or A (mineral) horizons, while a lower zone of clay accumulation is the B horizon, and the lowest zone that interfaces with the parent material is the C horizon.
Thenkabail, P., Lyon, J.G. & Huete, A. 2011, 'Hyperspectral remote sensing of vegetation and agricultural crops: Knowledge gain and knowledge gap after 40 years of research' in Thenkabail, P.S., Lyon, J.G. & Huete, A. (eds), Hyperspectral Remote Sensing of Vegetation, Taylor and Francis Group, USA, pp. 663-688.
Thenkabail, P., Lyon, J.G. & Huete, A. 2011, 'Advances in hyperspectral remote sensing of vegetation and agricultural croplands' in Thenkabail, P.S., Lyon, J.G. & Huete, A. (eds), Hyperspectral Remote Sensing of Vegetation, Taylor and Francis Group, USA, pp. 3-35.
Huete, A. & Glenn, E. 2011, 'Recent advances in remote sensing of ecosystem structure and function' in Weng, Q. (ed), Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, CRC Press, Taylor and Francis Group, Boca Raton, Florida USA, pp. 291-319.
Earth-observing remote sensing technologies are becoming widely adopted within the resource management, ecosystem sciences, and sustainable development communities. Satellite data offer unprecedented capabilities to capture the spatial and temporal detail of ecosystem properties at regional to global scales, and remote sensing tools are now employed in charactertising ecosystem structure and biologic properties and in monitoring ecosystem health, seasonal dynamics, and functional processes.
Huete, A., Didan, K. & van Leeuwen, W. 2011, 'MODIS vegetation indices' in Ramachandran, B., Justice, C.O. & Abrams, M. (eds), Land Remote Sensing and Global Environmental Change: NASA's Earth Observing System and the Science o, Springer-Verlag, New York, pp. 579-602.
Assessments of vegetation condition, cover, change, and processes are major components of global change research programs, and are topics of considerable societal relevance. Spectral vegetation indices are among the most widely used satellite data products, which provide key measurements for climate, hydrologic, and biogeochemical studies; phenology, land cover, and land cover change detection; natural resource management and sustainable development. Vegetation indices (VI) are robust and seamless data products computed similarly across all pixels in time and space, regardless of biome type, land cover condition, and soil type, and thus represent true surface measurements. The simplicity of VIs enables their amalgamation across sensor systems, which facilitates an ensured continuity of critical datasets for long-term land surface modeling and climate change studies. Currently, a more than two decades long NOAA Advanced Very High Resolution Radiometer (AVHRR)-derived consistent global normalized difference vegetation index (NDVI) land record exists, which has contributed significantly to global biome, ecosystem, and agricultural studies.
Huete, A., Didan, K., Leeuwen, W., Miura, T. & Glenn, E. 2011, 'MODIS Vegetation Indices' in Ramachandran, B., Justice, C.O. & Abrams, M. (eds), Land Remote Sensing and Global Environmental Change: NASA's Earth Observing System and the Science of ASTER and MODIS, Springer-Verlag, New York, pp. 579-602.
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Assessments of vegetation condition, cover, change, and processes are major components of global change research prograrns, and are topics of considerable societal relevance. Spectral vegetation indices are among the most widely used satellite data products, which provide key measurements for climate, hydrologic, and biogeochemical studies; phenology, land cover, and land cover change detection; natural resource management and sustainable development.
Huete, A. & Glenn, E.P. 2011, 'Recent advances in remote sensing of ecosystem structure and function' in Weng, Q. (ed), Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, CRC Press, Taylor and Francis Group, Boca Raton, Florida USA, pp. 291-319.
Earth-observing remote sensing technologies are becoming widely adopted within the resource management, ecosystem sciences, and sustainable development communities. Satellite data offer unprecedented capabilities to capture the spatial and temporal detail of ecosystem properties at regional to global scales, and remote sensing tools are now employed in characterising ecosystem structure and biologic properties and in monitoring ecosystem health, seasonal dynamics and functional processes.
Huete, A., Solano-Barajas, R., Glenn, E.P. & Restrepo Coupe, N. 2011, 'Monitoreo de propiedades y procesos ecosistamicos con indices de vegetacion MODIS' in Jean-Francois Mas (ed), Aplicaciones del sensorMODIS para el monitoreo del territorio, Secretaria de Medio Ambiente, Mexico, pp. 195-230.
En este captulo revisamos los importantes avances y las aplicaciones en el uso de ndices de vegetacin (IV) de MODIS como herramientas para el anlisis y monitoreo de propiedades y procesos ecosistmicos, incluyendo aquellos relacionados con la fotosntesis y la transpiracin del dosel. Presentamos las caractersticas bsicas de los productos IV-MODIS estndar, incluidas las series de tiempo, las medidas cualitativas, el mtodo de composicin y las incertidumbres presentes en las series de datos. Discutimos construcciones alternativas de las series de datos temporales de IV, incluyendo aquellos de la recepcin directa de datos. Los perfiles de los IV interanuales y estacionales son presentados y analizados en varios sitios de cobertura terrestre en Mxico y los IV MODIS son comparados con mediciones temporales de productividad bruta y evapotranspiracin de ecosistemas realizadas con torres de medicin de flujo en La Paz, Baja California Sur. Se muestra que los IV-MODIS son herramientas de inestimable valor en el estudio temporal de las dinmicas de los ecosistemas.
Erasmi, S., Ardiansyah, M., Propastin, P. & Huete, A. 2010, 'Spatiotemporal trends of forest cover change in Southeast Asia' in Teja Tscharntke (ed), Tropical Rainforests and Agroforests under Global Change, Ecological and Socio-economic Valuations, Springer, Gottingen, Germany, pp. 269-291.
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The current state of tropical forest cover and its change have been identified as key variables in modelling and measuring the consequences of human action on ecosystems. The conversion of tropical forest cover to any other land cover (deforestation) directly contributes to the two main environmental threats of the recent past: 1) the alteration of the global climate by the emission of carbon to the atmosphere and 2) the decline in tropical biodiversity by land use intensification and habitat conversion. The sub-continent of Southeast Asia exhibits one of the highest rates of forest loss and comprises one of the regions with the highest amount and diversity of flora and fauna species, worldwide.
Huete, A., Kim, Y., Ratana, P., Didan, K., Shimabukuro, Y.E. & Miura, T. 2008, 'Assessment of phenologic variability in Amazon tropical rainforests using hyperspectral and MODIS satellite data' in Kalacska, M. & Sanchez-Azofeifa, A. (eds), Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests, CRC Press, Boca Raton, Florida, USA, pp. 233-259.
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Miura, T., Huete, A., Ferreira, L.G., Sano, E.E. & Yoshioka, H. 2008, 'A technique for reflectance calibration of airborne hyperspectral spectrometer data using a broad, multi-band radiometer' in Kalacska, M. & Sanchez-Azofeifa, A. (eds), Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests, CRC Press, Boca Raton, Florida, USA, pp. 213-231.
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Anyamba, A., Tucker, C.J., Huete, A. & Boken, V.J. 2005, 'Monitoring Drought Using Coarse-Resolution Polar-Orbiting Satellite Data' in Boken, V., Cracknell, A.P. & Heathcote, R.L. (eds), Monitoring and Predicting Agricultural Drought: A Global Study, Oxford University Press, Oxford, UK, pp. 57-78.
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Huete, A. 2005, 'Estimation of Soil Properties Using Hyperspectral VIS/IR Sensors' in Anderson, M.G. (ed), Encyclopedia of Hydrological Sciences, John Wiley & Sons, Ltd, New York, pp. 887-902.
Knowledge of soil properties and processes are crucial to the understanding of the terrestrial hydrologic cycle and the functioning of terrestrial ecosystems. In this paper, we present the current state and potential of hyperspectral remote sensing techniques for quantitative retrieval of soil properties. Remote sensing is used to detect chemical and physical soil properties either (i) directly from the bare soil pixels, (ii) through advanced spectroscopy methods in mixed soil-vegetation-litter pixels, and (iii) by measurements of the overlying vegetated canopy to infer soil properties and moisture status. Optical-geometric properties of soil surfaces reveal information on soil physical features, such as soil structure, crusting, and erosion. We also investigate the use of vegetation water indices to infer soil drying and wetting in the soil root zone. We conclude with a discussion on future needs and directions for remote sensing of soil properties.

Conferences

Watson, C.J., Restrepo Coupe, N. & Huete, A.R. 2013, 'Hyperspectral assesments of condition and species composition of Australian grasslands', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2770-2773.
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Temperate grasslands in Australia show dynamic responses to climate, which renders them difficult to study using conventional remote sensing tools. However, the need to adequately describe native grassland variables is critical in maintaining ecological and agricultural values. We used a spectroradiometer to measure leaf-and canopy-level spectra from grassland plots in a controlled environment and compared results to fractional cover and species type. We found that the target species, Themeda australis and Poa labillardierei were separable at canopy and leaf level for both healthy and senescent foliage. In particular, we found differences in the 470-510 nm and 660-700 nm spectral regions. Comparison of narrow band vegetation indices for different combinations of photosynthetic and non-photosynthetic material showed strong relationships across a range of fractional cover values which was co-linear for both species. This method demonstrates the potential for remote sensing to identify Australian grasslands of different quality and composition. 2013 IEEE.
Rasaiah, B.A., Malthus, T.J., Bellman, C., Chisholm, L., Gamon, J., Hueni, A., Huete, A., Jones, S.D., Ong, C., Phinn, S., Roelfsema, C., Suarez, L., Townsend, P., Trevithick, R. & Wyatt, M. 2013, 'Approaches to establishing a metadata standard for field spectroscopy datasets', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4523-4526.
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There is an urgent need within the international remote sensing community to establish a metadata standard for field spectroscopy that ensures high quality, interoperable metadata sets that can be archived and shared efficiently within Earth observation data sharing systems. Careful examination of all stages of metadata collection and analysis can inform a robust standard that is applicable to a range of field campaigns. This paper presents approaches towards a standard that encompasses in situ metadata collection and initiatives towards sharing metadata within intelligent archiving systems. 2013 IEEE.
Ma, X., Huete, A., Yu, Q., Davies, K.P. & Restrepo Coupe, N. 2012, 'Monitoring spatial patterns of vegetation phenology in an Australian tropical transect using MODIS EVI', International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Society, Australia, pp. 271-276.
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Phenology is receiving increasing interest in the area of climate change and vegetation adaptation to climate. The phenology of a landscape can be used as a key parameter in land surface models and dynamic global vegetation models to more accurately simulate carbon, water and energy exchanges between land cover and atmosphere. However, the characterisation of phenology is lacking in tropical savannas which cover more than 30% of global land area, and are highly vulnerable to climate change. The objective of this study is to investigate the spatial pattern of vegetation phenology along the Northern Australia Tropical Transect (NATT) where the major biomes are wet and dry tropical savannas. For this analysis we used more than 11 years Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) product from 2000 to 2011. Eight phenological metrics were derived: Start of Season (SOS), End of Season (EOS), Length of Season (LOS), Maximum EVI (MaxG), Minimum EVI (MinG), annual amplitude (AMP), large integral (LIG), and small integral (SIG) were generated for each year and each pixel. Our results showed there are significant spatial patterns and considerable interannual variations of vegetation phenology along the NATT study area. Generally speaking, vegetation growing season started and ended earlier in the north, and started and ended later in the south, resulting in a southward decrease of growing season length (LOS). Vegetation productivity, which was represented by annual integral EVI (LIG), showed a significant descending trend from the northern part of NATT to the southern part. Segmented regression analysis showed that there exists a distinguishable breakpoint along the latitudinal gradient, at least in terms of annual minimum EVI (EVI), which is located between 18.84"S to 20.04"S.
Jiang, Z., Huete, A., Wang, Y. & Lyapustin, A. 2011, 'Evaluation of MODIS VI products using the AERONET-based surface reflectance validation network dataset', 34th International Symposium on Remote Sensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring.
MODIS vegetation index (VI) products (MOD13) are widely used in many science applications that aim to monitor and characterize spatial and temporal vegetation dynamics from space. The quality and reliability of the MODIS VI products are vital to these studies, and thus there is a need to assess their quality. In this study, the AERONET-based Surface Reflectance Validation Network (ASRVN) dataset is used to evaluate the quality of the MODIS 1 km, 16-day composite NDVI and EVI products. Our results show a positive bias of red reflectances, which is responsible for bias in the MODIS NDVI and two-band EVI (EVI2). The negative bias of the MODIS blue reflectance nullifies this effect on the standard EVI, resulting in insignificant bias in EVI. EVI and NDVI temporal profiles match ASRVN VI profiles even during higher aerosol optical thickness (AOT) periods, indicating that the VI products are not significantly affected by aerosols.
Ponce, G., Moran, S., Huete, A., Bresloff, C., Huxman, T., Bosch, D., Bradford, J., Buda, A., Gunter, S., McNab, H., McClaran, M., Peters, D., Sadler, J., Seyfried, M., Starks, P., Sutherland Montoya, D. & Heartsill, T. 2011, 'Convergence of dynamic vegetation net productivity responses to precipitation variability from 10 years of MODIS EVI', 34th International Symposium on Remote Sensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring.
According to Global Climate Models (GCMs) the occurrence of extreme events of precipitation will be more frequent in the future. Therefore, important challenges arise regarding climate variability, which are mainly related to the understanding of ecosystem responses to changes in precipitation patterns. Previous studies have found that Above-ground Net Primary Productivity (ANPP) was positively related to increases in annual precipitation and this relation may converge across biomes during dry years. One challenge in studying this ecosystem response at the continental scale is the lack of ANPP field measurements over extended areas. In this study, the MODIS EVI was utilized as a surrogate for ANPP and combined with precipitation datasets from twelve different experimental sites across the United States over a 10-year period. Results from this analysis confirmed that integrated-EVI for different biomes converged toward common precipitation use efficiency during water-limited periods and may be a viable surrogate for ANPP measurements for further ecological research.
Luvall, J.C., Sprigg, W.A., Levetin, E., Huete, A., Nickovic, S., Pejanovic, G.A., Vukovic, A., Van De Water, P.K., Myers, O.B., Budge, A.M., Zelicoff, A.P., Bunderson, L. & Crimmins, T.M. 2011, 'Use of MODIS satellite images and an atmospheric dust transport model to evaluate juniperus spp. Pollen phenology and dispersal', 34th International Symposium on Remote Sensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring.
Pollen can be transported great distances. Van de Water et. al., 2003 reported Juniperus spp. pollen was transported 200-600 km. Hence local observations of plant phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. The DREAM (Dust REgional Atmospheric Model, Nickovic et al. 2001) is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust. We are modifying the DREAM model to incorporate pollen transport. Pollen release will be estimated based on MODIS derived phenology of Juniperus spp. communities. Ground based observational records of pollen release timing and quantities will be used as verification. This information will be used to support the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program and the State of New Mexico environmental public health decision support for asthma and allergies alerts.
Huete, A.R. & Ponce, G. 2010, 'SATELLITE OBSERVED SHIFTS IN SEASONALITY AND VEGETATION -RAINFALL RELATIONSHIPS IN THE SOUTHWEST USA', NETWORKING THE WORLD WITH REMOTE SENSING, pp. 775-777.
Huete, A.R. & Saleska, S.R. 2010, 'Remote Sensing of Tropical Forest Phenology: Issues and Controversies', NETWORKING THE WORLD WITH REMOTE SENSING, pp. 539-541.
Hess, L., Ratana, P., Huete, A., Potter, C. & Melack, J. 2009, 'Use of MODIS enhanced vegetation index to detect seasonal patterns of leaf phenology in central Amazon vrzea forest', International Geoscience and Remote Sensing Symposium (IGARSS).
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MODIS 16-day composite EVI, NDVI, and VI Quality Analysis values for 2000-2005 were extracted for 21 vrzea forest sites along the Solimes-Amazon floodplain west of Manaus, Brazil. VI values were filtered to exclude dates with VI-QA values greater than 3, and time series of median values of the remaining pixels were examined in conjunction with river stage levels recorded at the Manacapuru gauge. All sites showed a regular seasonal variation in EVI, ranging from a mean low for all sites of 0.41 to a mean high of 0.61. The amplitude of variability in NDVI was about 50% that of EVI. Minimum EVI, corresponding to minimum leaf area, occurred in late May, about 40 days preceding maximum river stage; EVI peaked in mid-October, about 30 days before lowest river levels. These temporal patterns are in general agreement with field observations of leaf phenology at vrzea stands near Manaus. 2009 IEEE.
Freitas, R.M., Shimabukuro, Y.E., Rosa, R.R. & Huete, A. 2009, 'Fraction images derived from EO-1 hyperion multitemporal data for dry season green up analysis in Tapajs National Forest, Brazilian Amazonia', International Geoscience and Remote Sensing Symposium (IGARSS).
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In this study, we present an approach for phenology analysis of Amazon green-up using Linear Spectral Mixing Model applied to Hyperion multitemporal data. The study area was selected in the Tapajs National Forest located in Par State, Brazilian Amazonia. The region has well-defined dry and wet seasons with yearly rain about 2,100 mm a dry season occurring from June to October. The study area is primarily covered by dense tropical rain forest ("Floresta Ombrfila Densa") with a high number of emergent tree species. The EO-1 Hyperion data were acquired in July, August and September 2001, corresponding to the dry season in this region. The Linear Spectral Mixing Model was applied on each calibrated surface reflectance data, generating vegetation, soil, and shade fraction images. Then fundamental statistical analyses were carried out to evaluate the differences within the vegetation and shade fraction images derived from medium spatial resolution Hyperion images for rainforest phenology analysis. 2009 IEEE.
Huete, A.R. 2007, 'Satellite and tower flux comparisons of tropical forest functioning in the Mekong Region', 28th Asian Conference on Remote Sensing 2007, ACRS 2007, pp. 316-321.
Recent neotropical rainforest studies with local tower flux measurements and satellite observations have shown a strong correspondence of seasonal ecosystem carbon fluxes with satellite measures of greenness that follow the availability of sunlight. However, in drier tropical forests and in areas of forest disturbance, fluxes were more likely to track seasonal water availability. The tropical forests of the Mekong Region in Southeast Asia are more extensively degraded due to intense land use pressures and thus may be more drought susceptible. In this study, we investigated the seasonal patterns of Terra-MODIS satellite measures of enhanced vegetation index (EVI) greenness across a series of tropical forest types, encompassing intact drought-deciduous and dry evergreen tropical forests. Climate and environmental controls on the resulting phenologic profiles were then evaluated. The satellite time series data were compared with local site tower flux measures of gross primary productivity (GPP) for calibration and possible extension of tower flux measures to regional scales. Our results show large phenologic variability and differences in moisture and light controls on productivity across the different tropical forests with the drier forest sites primarily water-limited, and the more humid evergreen forests showing a positive trend with light availability. Disturbed forest areas were increasingly drought-susceptible. Satellite greenness observations were generally consistent and linearly related with tower flux GPP measurements at the tower sites, providing opportunities for regional scaling of carbon fluxes across the heterogeneous surface states of the Mekong Region.
Kim, Y., Huete, A.R., Jiang, Z. & Miura, T. 2007, 'Multisensor reflectance and vegetation index comparisons of Amazon tropical forest phenology with hyperspectral Hyperion data', Proceedings of SPIE - The International Society for Optical Engineering.
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Current earth observing satellite sensors have different temporal, spectral and spatial characteristics that present problems in the establishment of long term, time series data records. Vegetation indices (VI's) are commonly used in deriving long term measures of vegetation biophysical properties, which have been shown useful in interannual climate studies and phenology studies. While significant improvements have been made with new sensors, and algorithms, and processing methods, backward compatibility of VI's is desired so that the long term record can extend back and utilize the AVHRR record to 1981. Conversely, any reprocessing of the AVHRR record should consider steps to allow forward compatibility with newer sensors and products. In this study we evaluated the use of sensor-specific enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) data sets, using a time sequence of Hyperion images over Tapajos National Forest in Brazil over the 2001 and 2002 dry seasons. We computed NDVI, EVI, and a 2-band version of EVI (EVI2) for different sensor systems (AVHRR, MODIS, VIIRS, SPOT-VGT, and SeaWiFS) and evaluated their differences and continuity in the characterization of tropical forest phenology. We also analyzed the influence of different atmosphere correction scenarios to assess noise in the phenology signal. Our analyses show that EVI2 maintains the desirable properties of increased sensitivity in high biomass forests across all sensor systems evaluated in this study. We further conclude that EVI2 can be extended to the AVHRR time series record and compliment that current NDVI time series record.
Jiang, Z., Huete, A.R., Kim, Y. & Didan, K. 2007, '2-Band enhanced vegetation index without a blue band and its application to AVHRR data', Proceedings of SPIE - The International Society for Optical Engineering.
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The enhanced vegetation index (EVI) has been found useful in improving linearity with biophysical vegetation properties and in reducing saturation effects found in densely vegetated surfaces, commonly encountered in the normalized difference vegetation index (NDVI). However, EVI requires a blue band and is sensitive to variations in blue band reflectance, which limits consistency of EVI across different sensors. The objectives of this study are to develop a 2-band EVI (EVI2) without a blue band that has the best similarity with the 3-band EVI, and to investigate the crosssensor continuity of the EVI2 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). A linearity-adjustment factor () was introduced and coupled with the soil adjustment factor (L) used in the soil-adjusted vegetation index (SAVI) in the development of the EVI2 equation. The similarity between EVI and EVI2 was validated at the global scale. After a linear adjustment, the AVHRR EVI2 was found to be comparable with the MODIS EVI2. The good agreement between the AVHRR and MODIS EVI2 suggests the possibility of extending the current MODIS EVI time series to the historical AVHRR data, providing another longterm vegetation record different from the NDVI counterpart.
Shimabukuro, Y.E., Anderson, L.O., Arago, L.E.O.C. & Huete, A. 2006, 'Using fraction images to study natural land cover changes in the Amazon', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2103-2106.
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Satellite data such as the vegetation indices are a crucial tool for studying vegetation phenology patterns from regional to global scales. In this study, we investigated the relationship of the fraction images, derived from the linear spectral mixture model, with the NDVI and EVI, the most used indices to evaluate the phenological response using remote sensing data from the MODIS sensor. Our objectives were to understand how the vegetation indices are related with the vegetation fraction and to evaluate if the information provided by the shade and soil fraction images can be used to explain the vegetation indices behavior. We used a temporal series data of the MOD13A1 product for the 2002 year, the precipitation data from 125 meteorological stations, and a land cover map generated based on the 2002 images. We studied two different vegetation physiognomies to analyse if the fraction images were landscape dependent Our results showed that for the Open Tropical Forest, the vegetation fraction image presented a significant correlation with the EVI (r 2=0.84) but not with the NDVI. For the Cerrado grassland landscape, the vegetation fraction image presented high correlation with the NDVI (r 2=0.93) and EVI (r2=0.98). Significant correlations were also found for the shade and soil fraction images for the land cover studied, showing that these additional information are a useful source of data to understand the vegetation canopy structural changes and to analyze the responses provided by the vegetation indices correctly.
Ratana, P., Huete, A.R. & Didan, K. 2006, 'MODIS EVI-based variability in amazon phenology across the rainforest-cerrado eeotone', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1942-1944.
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The Amazon region contains one of the world's richest biodiversity and plays a major role in global dynamics of climate and global water and biogeochemical cycles. A better understanding of vegetation phenology and associated seasonal variations in carbon dynamics is necessary to develop reliable biosphere-climate models in the region. In this study, we investigated the interaction of climate and vegetation physiognomy on Amazon vegetation phenology through various eco-climatic transects traversing the Amazon transition ecotone. Our objective was to develop a better understanding of transitional ecotone vegetation dynamics and assess their relationship to rainforest and cerrado phenology patterns. The Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series data were utilized for measuring seasonal variations in "greenness" across a series of eco-climatic transects. Overall, MODIS EVI derived phenology, showed pronounced, moisture induced drywet seasonal contrasts in the cerrado and intermediate seasonal contrast level in the light-limited rainforests. The transitional forests, representing a mixed response of light, water, and other factors, showed the least seasonality, however, the ecotone areas of converted forests depicted strong, moisture-limited, dry-wet seasonal contrasts due to the dominance of shallow rooted plants that cannot exploit water from deeper soil moisture layers in the dry season. The EVI seasonal profiles of tropical rainforests, transition ecotone forests, converted forests, and cerrado were unique. This yielded important phenology information useful in land cover characterization and for parameterization for biosphere-climate models.
Huete, A.R., Running, S. & Myneni, R. 2006, 'Monitoring rainforest dynamics in the Amazon with MODIS land products', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 263-265.
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The metabolism and phenology of Amazon rainforests significantly influence global dynamics of climate, carbon and water, but remain poorly understood. In this study we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial ecosystem variables to analyze Amazon rainforest dynamics utilizing the satellite products; vegetation indices (VI), leaf area index (LAI), fraction of absorbed photosynthetically-active radiation (FPAR), and gross primary production (GPP). We found the MODIS products to greatly facilitate analyses and monitoring of ecosystem metabolism in both intact and disturbed rainforests.
Huete, A.R., Miura, T., Kim, Y., Didan, K. & Privette, J. 2006, 'Assessments of multisensor vegetation index dependencies with hyperspectral and tower flux data', Proceedings of SPIE - The International Society for Optical Engineering.
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Long term data records require the effective integration of new sensor technologies and improved algorithms to better characterize global and climate change impacts on ecosystems, while preserving the fundamental attributes of the existing data record. In this study, we investigated key determinants in the spectral translation and extension of MODIS Vegetation Index products across current sensor systems and to the NPOESS (VIIRS) era. We used simulated sensorspecific data sets derived from hyperspectral data using field spectroroadiometers and Hyperion sensors to investigate inter-sensor translation and continuity issues of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). We also investigated the use of data fusion of satellite VI time series with in-situ flux tower time series measurements of photosynthesis, and the use of data fusion with tower-based continuous measures of broadband/hemispherical VI's as possible reference data sets for the inter-calibration of satellite VI time series from different sensor systems. Preliminary comparisons are presented with actual satellite VI measurements from SPOT-VEGETATION, Terra- and Aqua-MODIS, and AVHRR sensors. We found that with a consistent atmosphere correction scheme and a generalized compositing procedure, translation of multi-sensor datasets can be achieved with certain limitations.
Huete, A., Kim, H.-.J. & Miura, T. 2005, 'Scaling dependencies and uncertainties in vegetation index - Biophysical retrievals in heterogeneous environments', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5029-5032.
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The evaluation and understanding of vegetation indices (VIs) across heterogeneous surfaces is important for time series analyses, field validation and extension of VIs, and in the effective use of VI-biophysical relationships in models. In this study, we evaluated the scaling dependencies in the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) over a range of surface heterogeneous conditions. VI values were derived from fine-grain and aggregated AVIRIS data. VI differences at multiple scales were analyzed in terms of surface reflectance variance and VI-biophysical relationships. The NDVI was highly scale-dependent with significant deviations between coarse resolution values and fine grain averages. Coarse grain NDVI values, as in the case of the MODIS-NDVI, were positively biased when surface NIR reflectance variations exceeded those in the red, a condition prevalent when surface water features and dark soils are present. The EVI was quasi-scale invariant across the heterogeneous conditions analyzed here. EVI values were thus, more easily transportable, facilitating the fusion of multi-sensor and multi-resolution datasets for time series analyses and field-based scaling of biophysical relationships to satellite products. 2005 IEEE.
Ferreira, N.C., Ferreira, L.G., Huete, A., Didan, K. & Miura, T. 2005, 'A GIS based change detection system for the Amazon forest: Advantages and implications for the environmental monitoring and regional sustainable development', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3474-3476.
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In this study, we present the basis of an automated deforestation mapping system for the Brazilian Amazon Forest implemented on the ArcGIS 9.0 platform and based on the comparative analysis of the MOD13Q1 products. This system is the first operational module of the "Integrated Warning Deforestation System for the Amazon" (SIAD), an initiative of the Brazilian government within the scope of the Amazon Protection System (SIPAM), which also comprises a 1) spatial information module, aimed at the assessment of the causes and impacts of the deforested areas; 2) a prospective module indicative of future deforestation risks, and 3) a data and information gateway. 2005 IEEE.
Ratana, P., Huete, A.R., Yin, Y. & Jacobson, A. 2005, 'Interrelationship among among MODIS vegetation products across an Amazon eco-climatic gradient', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3009-3012.
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The Moderate Resolution Imaging Spectroradiometer (MODIS) has been found to be a useful tool in spatial and temporal terrestrial biosphere monitoring. In this study, we investigated the spatial and temporal interrelationships among MODIS vegetation products across a north-south Amazon eco-climatic transect, encompassing tropical forest, forest-savanna transition zone, and cerrado. MODIS vegetation index (VI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), gross primary production (GPP), land surface water indices (i.e. LSWI) and land surface temperature (LST) products were extracted over both natural and converted areas along the climatic gradient. Our results showed strong spatial and temporal variations in vegetation dynamics over the climatic gradient with natural and converted areas responding differently. The relationships between VI and LAI/FPAR were unique with biome type. The VI and LSWI seasonal profiles matched fairly well in the cerrado region and conversion areas but not primary forest areas. A positive relationship was found between VI and LST in tropical forest, however the cerrado showed negative VI-LST relationships. These differences were related to the unique seasonal water and carbon patterns within each ecosystem. The interrelationships between MODIS land data products and land surface water indices yield important information useful in land cover characterization and prediction of vegetation responses to climate change and land cover conversions. 2005 IEEE.
Huete, A.R. 2005, 'Global variability of terrestrial surface properties derived from MODIS visible to thermal-infrared measurements', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4938-4941.
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There are now over five years of MODIS Global Land Data Products that have been applied to global terrestrial land cover, carbon, and water science. There is a suite of land products for Land Cover Characteristics, such as Land Cover, Vegetative Cover Conversion, Continuous Fields, and Fire. The Radiation Budget Variables include Surface Reflectance Products, Land Surface Temperature, Snow and Ice Cover, BRDF and Albedo, and Ecosystem Variables are characterized by Vegetation Indices (NDVI/ EVI), LAI and FPAR, Gross Primary Production and Net Primary Production (GPP/NPP). The land cover products are being used in assessing global change In the biosphere by quantifying land cover type and detecting changes resulting from climatic forcings and human-induced and natural disturbances. The MODIS fire product includes daily mapping of active fires, burned area estimates, and analyses of large-scale wildfire disturbances. The MODIS NDVI and EVI are used for global vegetation analyses at 250 m resolution, adding to the historical AVHRR-NDVI time series data record. There are also advanced biophysical variables generated, such as the leaf area Index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) products which are needed for advanced modeling of climate, carbon cycles, and water balance of the land surface. The daily gross primary production and annual net primary production (GPP/NPP) are components of the land carbon cycle and provide a first step In CO2 source/ sink analysis. Recent results of using this product with the historical AVHRR NDVI data record show a 6% increase in terrestrial NPP over the last 18 years. Two new experimental products are also of utility in land surface science, a canopy water content index and an evaporation index based on surface energy partitioning principles, and have potential value for regional drought analysis, water management and wildfire risk assessments. The radiometric quality of MODIS has proven to be excellent for land science. These high quality, consistent and well-calibrated satellite measurements are becoming invaluable in detection and monitor changes and trends of the biosphere. 200S IEEE.
Huete, A. & Didan, K. 2004, 'MODIS seasonal and inter-annual responses of semiarid ecosystems to drought in the Southwest U.S.A', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1538-1541.
High temporal frequency observations with the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra Earth Observing System platform offer unique opportunities to study climate- and anthropogenicinduced land transformations in the temporal domain. Shifts in vegetation type and physiognomies alter biologic activity and responses to climate patterns in unpredictable ways. Increases in insect populations (e.g. bark beetle) and fire associated with a multi-year drought in the Southwest U.S.A have greatly impacted the health of its native ecosystems from pinyon-juniper, Ponderosa pine, and mixed conifer forests to the savanna, grassland, and desert shrub ecosystems. In this study, MODIS time series data combined with AVIRIS overflights were analyzed for detection and evaluation of the causes, severity, and extent of changes in ecosystem health. We used the 16-day MODIS enhanced vegetation index (EVI) product and a normalized difference water index (NDWI) to analyze the seasonal, inter-annual, and spatial patterns of vegetation activity over a wide range of land cover types across eco-climatic and elevational gradients and through the winter and variable monsoon rainfall 'pulses'. The temporal dynamics of vegetation were found to be highly sensitive to both anthropogenic and climatic forcings found in the semiarid and arid Southwest with seasonal and inter-annual profiles varying markedly with land cover type and land surface disturbance (e.g. drought, insects). All land cover types and eco-climatic gradients from desert shrub to montane forest were significantly affected by the drought, with grasslands most impacted. Tree mortality variability could also be assessed and we found that combined MODIS - AVIRIS data offer the potential of ecosystem health and risk assessment.
Didan, K. & Huete, A. 2004, 'Analysis of the global vegetation dynamic metrics using MODIS vegetation index and land cover', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2058-2061.
Climate change has important implications on the global distribution and dynamics of vegetation that, in turn, impacts the global carbon cycle. Irrespective of the forcings driving these changes, a characterization of global vegetation dynamics and the establishment of accurate metrics that can be linked to forcings are paramount to addressing questions related to climate change and its bearings on the terrestrial biosphere. Terrestrial ecosystems affect climate through a complex system of interactions among water, carbon and energy. An increase, or decrease in these fluxes forces new equilibrium states and feedbacks to the climate system, which in turn impacts the ecosystems. NDVI-based time series analysis of satellite imagery from the NOAA-AVHRR sensor, collected during the last two decades narrates an enhanced vegetation activity over key areas of the Earth (high and mid latitudes). Most of this increase in activities has been indirectly linked to an increase in the Earth's temperature and CO2 concentration. In this study we assessed the relationships between vegetation dynamic metrics and climate-ecosystem parameters: We analyzed 3 years of MODIS Vegetation Index (VI) data augmented by a global land cover map derived from the same sensor, and the GTOPO DEM data. Using a stratified spatial analysis, we assessed the role of the following characteristics on vegetation: Latitude: to isolate temperature regimes and seasonality, Elevation: to isolate land cover and precipitation distribution, Land cover: to isolate phonological characteristics. A combinatorial analysis using the above stratification was applied in successive orders to generate compound results. The results yielded coherent time series profiles depicting vegetation dynamics as it relates to elevation, latitude and land cover.
Ratana, P. & Huete, A. 2004, 'Seasonal dynamics of native and converted cerrado physiognomies with MODIS data', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4336-4339.
The Cerrado or Brazilian savanna represents 23% of the land surface of the country. This important biome, however, has been subjected to rapid rates of land conversion to agriculture and pasture. This has important environmental consequences to local and regional climate change and carbon fluxes. Therefore, a study of seasonal cerrado dynamics, including forest and converted areas, was conducted with four years of data (February 2000 to December 2003) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The 16-day composite vegetation index (VI) data were used to analyze the seasonal patterns of photosynthetic vegetation activity and examine the separability of cerrado formations of varying physiognomies in Brasilia National Park and surrounding areas. The results showed that the cerrado formations exhibited a high seasonality contrast with a pronounced dry season from June through August and wet season from November to March. Discrimination within cerrado formations was difficult due to similarities in their seasonal dynamic behavior. Maximum contrast among all the cerrado formations occurred during dry season, suggesting this as the best time for cerrado physiognomy discrimination. The converted agricultural areas had a higher contrast than the native cerrado, and the forest formation had the lowest seasonal contrast. This enabled an operational method to discriminate the cerrado formation from the converted areas and adjoining forests. Thus, MODIS offers a useful tool to monitor the threatened cerrado biome.
Ferreira, L.G., Ferreira, M.E., Ferreira, N.C., De Jesus, E.T., Sano, E.E. & Huete, A.R. 2004, 'Evaluation of MODIS vegetation indices and change thresholds for the monitoring of the Brazilian Cerrado', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4340-4343.
In this paper we investigated the use of the MODIS vegetation indices and the effect of distinct change thresholds for monitoring land cover change in the Cerrado biome, the largest region of neotropical savanna vegetation in the world and the most threatened biome in Brazil On a preliminary basis, our results suggest the use of change thresholds between 35 and 42% and the use of the enhanced vegetation index (EVI), which, in comparison to the normalized difference vegetation index (NDVI), showed a more stable and predictable behaviour.
Huete, A., Miura, T. & Gao, X. 2002, 'Land cover conversion and degradation analyses through coupled soil-plant biophysical parameters derived from hyperspectral EO-1 hyperion', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 799-801.
Comparing land degradation in arid, semi-arid, and dry sub-humid areas result from various factors, including climate variations and human activity, and can lead to desertification. The process of degradation results in simultaneous and complex variations of many interrelated and coupled soil-vegetation parameters. General information and data regarding the degree and extent of land degradation and the resulting impacts remain poorly understood and remote sensing can play a major role by providing a quantifiable and replicable technique for monitoring and assessing the extent and severity of soil degradation. In this study we investigated various 'optical' indicators and early warning signals of land degradation and desertification with the use of hyperspectral remote sensing data derived from EO-1 Hyperion and AVIRIS sensors. Utilizing spectral mixture analysis, we analyzed simultaneous spatial variations in vegetation cover, species, physiognomy, albedo, and soil properties at various sites representing different stages of land degradation in the Mendoza regions of Argentina and at sites in the Southwest U.S.A. We found that both soil and vegetation parameters were required to characterize the unstable and spatially variable landscape dynamics found in actively degrading environments.
Miura, T., Huete, A., Yoshioka, H. & Kim, H.-.J. 2002, 'An application of airborne hyperspectral and EO-1 Hyperion data for inter-sensor calibration of vegetation indices for regional-scale monitoring', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3118-3120.
In order to use satellite data from multiple sensors for monitoring of the Earth's vegetation, satellite data product continuity and compatibility need to be investigated. In this study, we assessed inter-sensor compatibility of the normalized difference vegetation index (NDVI) by simulating bandpasses of the NOAA-14 AVHRR, Terra-MODIS, and Landsat-7 ETM+ from airborne hyperspectral and EO-1 Hyperion data acquired over a savanna-forest transitional zone in Brazil. Our results showed that the simulated reflectances among the sensors examined here were near-linearly related, but with relationships that were land cover-dependent, e.g., reflectances over green vegetation targets formed separate relationships. The deviations were larger for greener targets. On the other hand, the NDVI crossplots among the sensors formed single, land-cover independent relationships. They were, however, curve-linearly related with the degree of curve-linearity increasing with atmosphere contamination. Also, land cover dependencies appeared in the NDVI cross-plots when contaminated by atmosphere. These results suggest that inter-sensor calibration and continuity of the NDVI are archievable, but require a careful examination of surface and atmospheric conditions and the development of a theoretical basis.
Yoshioka, H., Miura, T., Yamamoto, H. & Huete, A. 2002, 'A technique of inter-sensor VI translations using EO-1 hyperion data to minimize systematic differences in spectral band-pass filters', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2211-2213.
Utilization of satellite date from multiple platforms increases our chances of more frequent and accurate observations of the earth's surface in both global and regional scale. For the purpose of vegetation monitoring, this will be particularly true by combining the data from sensors of various spatial, spectral, and temporal resolutions, e.g. the combinations of data from AVHRR (broad band), MODIS (narrow band) and ETM+ (higher spatial resolution). Even though the same spectral vegetation index can be obtained from these sensors, the two main issues need to be considered; one is the systematic differences caused by the spectral response functions, and the other is the differences in spatial resolutions. This paper investigates the spectral issue and its role in the spectral calibration of NDVI among sensors. Hyperspectral data from Hyperion onboard the EO-1 platform were used to simulate outputs from various sensors by band convolution. The data were initially corrected for Rayleigh scattering and Ozone Absorption to produce the top-of-the-canopy reflectance as a starting point. The technique first designs a sensor-specific vegetation index (VI) and background brightness index (BI) by accounting for the differences in band-pass filters. These VIs and BIs are then used to estimate the common parameters (sensor independent parameters) attributed to vegetation amount and background brightness. Finally, these parameters are used for the translation of VI among sensors. The preliminary results show the high potential and utility of the translation algorithm in comparison to a simple linear fitting of NDVI values of two sensors.
Huete, A., Didan, K., Miura, T., Yoshioka, H., Ferreira, L., Gao, X. & Batchily, K. 2001, 'Validation of the MODIS vegetation indices over a global set of test sites: Preliminary results', Proceedings of SPIE - The International Society for Optical Engineering, pp. 194-203.
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Vegetation indices (VI's) are important tools in the seasonal and inter-annual monitoring of the Earth's vegetation. In this study, the vegetation index products from the Moderate Resolution Imaging Spectroradiometer (MODIS) are evaluated over a preliminary set of validation test sites, including a cerrado and rainforest site in Brazil, and two grass/shrub sites in Arizona and New Mexico, U.S.A. Ground and airborne validation experiments were conducted to assess the performance of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) for vegetation monitoring. Calibrated spectroradiometers were flown for top-of-canopy reflectance retrievals. Vegetation sampling provided the data needed for a biophysical validation of the VI's. Both single-day and 16-day composited MODIS data were processed and corrected for atmosphere at 500m and 1 km resolutions. The MODIS data compared quite well with the validation data with most of the uncertainty associated with the compositing process. Results show the MODIS VI products to offer enhanced sensitivity for land use discrimination and monitoring at both regional and global scales. The EVI was fairly well resistant to residual cloud and aerosol contamination and had a good range of sensitivity over the high biomass, forested areas.
Yoshioka, H., Ferreira, L., Huete, A., Kim, H.J. & Miura, T. 2001, 'A translation algorithm of NDVI to minimize biases caused by differences in spectral band-pass filters', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1835-1837.
This paper addresses the issue of NDVI continuity across sensors by focusing on minimization of NDVI differences caused by sensor spectral differences. Two sets of hyperspectral data were used to simulate a sensor's NDVI value by band convolution with appropriate spectral filters, thus eliminating the spatial Issue related to sensor spatial response curves in this study. One is a set of airborne radiometric data obtained by a spectroradiometer from 330m above ground level over grass dominated Cerrado region in Brasilia, Brasil. The other data set is an AVIRIS image over a semi-arid grass dominated region in Arizona, USA. The translation algorithm is briefly explained and Its performance was demonstrated with the airborne hyperspectral data sets. The values of translated MODIS-equivalent NDVI from simulated ETM+ NDVI were compared to the value of simulated-MODIS NDVI by direct convolution from hyperspectral data with the MODIS bandpass filters. The differences of NDVI values between the translated and simulated MODIS NDVI (?3.0e-4) were reduced by two orders smaller than the original differences of 0.02 represented by (ETM+ - MODIS). The proposed technique showed good performance in minimizing the NDVI differences between the two sensors over the entire NDVI range.
Huete, A., Gao, X., Asner, G., Kim, H.J. & Miura, T. 2001, 'Characterization of vegetation conditions at the acuan and Chancani reserves in Argentina with ground- air- and EO-1 hyperion data', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 308-310.
Hyperspectral data sets were collected over several land cover types in the Crdoba and Mendoza regions of Argentina in support of EO-1 validation activities. Hyperion satellite imagery, airborne AVIRIS imagery and ASD spectroradiometer data were acquired over the protected acuan and Chancani Reserves, composed of floristically diverse vegetation communities, including mesquite shrub and open mesquite forest (algorrobos), savanna, and creosotebush (jarillales). We sampled these vegetation communities in their original, undisturbed state as well as at various stages of degradation with both an optical and biophysical sampling scheme. Ground-based radiometric measurements included 100 m transects through each type of canopy as well as 'pure' spectral signatures over all the major vegetation species, soils, and non-photosynthetic vegetation (NPV) materials. Biophysical measurements included percent cover by component and leaf area index (LAI) measurements. The ground-based data sets were co-registered with the airborne and satellite imagery to evaluate the quality and validate the Hyperion data. Our goal was to assess the capability of hyperspectral data in discrimination the gradient of vegetation types and land cover conversions sampled in this study.
Miura, T., Didan, K., Huete, A.R. & Patricia Rodriguez, E. 2001, 'A performance evaluation of the MODIS vegetation index compositing algorithm', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1812-1814.
The Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) products are aimed at providing consistent, spatial and temporal comparisons of global vegetation conditions, which will be used for operational monitoring of the Earth's photosynthetic vegetation activity in support of land cover classification, change detection, and biophysical interpretations. Standard processing in generating the MODIS VI products include the temporal compositing process in order to minimize cloud and atmosphere contamination, and standardize sun/view angles. In this study, we evaluated the performance of the MODIS VI compositing algorithm as to whether the algorithm is selecting the cloud-free observations and whether it is producing and selecting observations with view zenith angles closest to nadir. The analyses were conducted over a set of globally-distributed sites covering a wide range of biome types over dry and wet seasons. We found the MODIS VI compositing algorithm to work fairly well over arid to semi-arid to temperate zones in both dry and wet seasons, in which nearly all of the composited pixels were cloud-free with view zenith angles closer to nadir than the conventional MVC results. On the other hand, the MODIS algorithm did not improve upon the MVC results well over tropical rain forest areas as cloud covers persisted throughout the period analyzed in these regions.
Justice, C., Townshend, J., Vermote, E., Sohlberg, R., Descloitres, J., Roy, D., Hall, D., Salomonson, V., Riggs, G., Huete, A., Didan, K., Miura, T., Wan, Z.M., Strahler, A., Schaaf, C., Myneni, R., Running, S., Glassy, J., Nemani, R., El Saleous, N. & Wolfe, R. 2000, 'Preliminary land surface products from the NASA moderate resolution imaging spectroradiometer (MODIS)', IGARSS 2000: IEEE 2000 INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOL I - VI, PROCEEDINGS, pp. 1157-1162.
Huete, A., Didan, K., Shimabokuro, Y., Ferreira, L. & Rodriguez, E. 2000, 'Regional Amazon Basin and global analyses of MODIS vegetation indices: Early results and comparisons with AVHRR', IGARSS 2000: IEEE 2000 INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOL I - VI, PROCEEDINGS, pp. 536-538.
Ferreira, L.G., Huete, A., Yoshioka, H. & Sano, E. 2000, 'Preliminary analysis of MODIS vegetation indices over the LBA sites in the Cerrado Region, Brazil', IGARSS 2000: IEEE 2000 INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOL I - VI, PROCEEDINGS, pp. 524-526.
Huete, A., Didan, K., van Leeuwen, W. & Vermote, E. 1999, 'Global-scale analysis of vegetation indices for moderate resolution monitoring of terrestrial vegetation', REMOTE SENSING FOR EARTH SCIENCE, OCEAN, AND SEA ICE APPLICATIONS, pp. 141-151.
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Sano, E.E., Moran, M.S., Huete, A.R. & Miura, T. 1997, 'Ku-band SAR data for bare soil moisture retrieval over agricultural fields', International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 98-100.
This study focused on the analyses of multiangle, Ku-band (14.85 GHz), synthetic aperture radar (SAR) data for bare soil moisture retrieval over agricultural fields. The radar data were acquired at three incidence angles (35, 55, and 75), VV polarization, and 2-m nominal spatial resolution. The fields presented either a small-scale or an intermediate-scale periodic soil roughness components, associated with level-basin and furrow irrigation systems, respectively. Both of these periodic structures were randomly perturbed by the presence of soil clods. Radar backscatter and soil surface moisture data were obtained from 39 `borders' (within-field level basins). The results showed that the bare soil moisture retrieval over agricultural fields with periodic roughness components from Ku-band SAR data is dependent upon the incidence angle and the soil roughness condition. For fields with level-basin irrigation system, a good linear relationship between radar backscattering coefficients and volumetric soil moisture contents was found, especially at 35 incidence angle (r2 = 0.90; for 55 and 75 incidence angles, r2 = 0.74 and 0.41, respectively). However, for fields with the furrow irrigation system, the radar backscattering data were nearly insensitive to soil moisture due to the stronger influence of soil roughness.
van Leeuwen, W.J.D., Huete, A.R., Jia, S. & Walthall, C.L. 1996, 'Comparison of vegetation index compositing scenarios: BRDF versus maximum VI approaches', International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 1423-1425.
Satellite sensors, acquire bidirectional reflectance data under different solar illumination angles. These systems will capture the strong anisotropic properties that vary with relative amounts and types of vegetation and soil within each pixel. Therefore, some knowledge of the bidirectional reflectance distribution function (BRDF) is a requirement for successful interpretation of directional reflectance data and vegetation indices, and derivation of land-cover-specific biophysical parameters. The objectives of this research were: a) to parameterize empirical and semi-empirical BRDF models for different land cover types and MODIS spectral bands, b) utilize the BRDF models to correct off-nadir measurements to nadir-equivalent values for vegetation index (VI) compositing and biophysical interpretation and c) compare different vegetation index compositing scenarios.
Sano, E.E., Huete, A.R., Troufleau, D., Moran, M.S. & Vidal, A. 1996, 'Analysis of ERS-1 SAR data to study soil moisture content in rocky soils', International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 157-159.
The objective of this research was to analyze the potential of ERS-1 SAR data to study the surface soil moisture content of rocky soils in semi-arid regions. The study took place at the Walnut Gulch Experimental Watershed, Tombstone, AZ. Field soil moisture contents and dry bulk densities in the top 5 cm were obtained from 51 different sampling sites in the watershed during a satellite overpass on July 25, 1994. One set of 22 points was characterized by a sandy soil, 3-15% slope, and vegetation dominated by shrubs. Another set of 29 sampling sites was characterized by a clayey soil, 0-3% slope, and covered by grasses and forbes. Soil roughness was measured at eight of these sites. The SAR image was georeferenced in the Universal Transverse Mercator (UTM) coordinate system and calibrated using a digital elevation model (DEM). In general, the correlation between volumetric soil moisture content and the radar backscatter signal was poor, but improved (r2 = 0.78 and 0.41 for the first and second sets, respectively) when limited to sites with volumetric moisture contents higher than 10%. The low correlation was due to the dominant influence of soil roughness which was directly related to the proportion and size of rock fragments. Although roughness seems to explain much of the variance from the radar backscatter signal, it was not clear how best to parameterize the roughness properties. Continued research includes modeling and separating moisture and roughness influences.
Liu, H.Q. & Huete, A. 1994, 'Systems based modification of the NDVI to minimize soil and atmospheric noise', International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 128-130.
The Normalized Difference Vegetation Index (NDVI) equation has a simple, open loop structure. This renders the NDVI susceptible to large sources of error and uncertainty over variable atmospheric and soil background conditions, which is less than satisfactory in meeting the need for accurate, long term vegetation measurements for the Earth Observing System (EOS) program. In this study, a systems analyses approach is used to examine noise sources in existing VI's and to develop a stable, modified NDVI (MNDVI) equation. The MNDVI, a closed-loop version of the NDVI, was constructed by adding: (1) a soil and atmospheric noise feedback loop, and (2) an atmospheric noise compensation forward loop. The coefficients developed for MNDVI can be used with data uncorrected for atmosphere, as well as with Rayleigh corrected and atmospherically corrected data. In field observational and simulated data, as well as satellite imagery, the MNDVI was found to reduce combined soil and atmospheric noise to less than 4% for any complex soil and atmospheric situation. The resulting uncertainty, expressed as vegetation equivalent noise (VEN), was 0.11 LAI units, which was seven times less than encountered with the NDVI (0.8 LAI), and three times less (0.36 LAI), than with the Soil Adjusted and Atmospherically Resistant Vegetation Index (SARVI).
Huete, A.R., Liu, H., de Lira, G.R., Batchily, K. & Escadafal, R. 1994, 'Soil color index to adjust for soil and litter noise in vegetation index imagery of arid regions', International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 1042-1043.
The monitoring of vegetation in arid and semi-arid regions is made difficult by the dominant and variable soil signal. Soil color and brightness differences are believed to be responsible for the large Saharan desert `artifact' areas in A VHRR- normalized difference vegetation index (NDVI) imagery. This problem represents a lower boundary condition of vegetation detection, affecting locust monitoring in the Sahel, and detection of the `onset of greenness' in vegetation index imagery. In this study, a critical analysis is made as to the internal (soil properties) and external (view-sun geometry and atmosphere) conditions that are likely to result in high VI values over non-vegetated surfaces. Observational and simulated, bidirectional soil and litter reflectance data are analyzed and compared with optical data from sparse shrub and grass canopies, with and without atmospheric simulations. The problem is shown to be more related to soil color (hue and chroma) and is unrelated to soil brightness. Soil color problems were found to occur, to varying extent, in the NDVI as well as the `improved' NDVI variants. As a result, additional spectral bands are necessary to develop a separate soil color index, which may involve an empirical equation with the `green' band or a soil-correcting mixture model.
van Leeuwen, W.J.D., Huete, A.R. & Walthall, C.L. 1994, 'Biophysical interpretation of a spectral mixture model based on a radiative transfer model and observational data', International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 1458-1460.
Linear spectral mixture models used to invert mixed spectral responses of a target into proportions of plant and soil components at the Earth's surface lack the biophysical interpretation of the results. The paper aims to relate the decomposed properties of the surface with the known biophysical properties of the surface. A radiative transfer model was used to generate mixture reflectances using the measured optical properties of pure components. The method is applied to subsets of a nadir image of the Advanced Solid State Array Spectroradiometer.
de Lira, G.R., Batchily, K., Hongtao, J. & Huete, A.R. 1994, 'Optical and seasonal variations along the U.S. Mexico border: An analysis with Landsat TM imagery', International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 1044-1045.
A multi-temporal border study was conducted with a series of seven Landsat Thematic images acquired during the 1992 growing season. Spatial and temporal variations across the border were analyzed with reflectance and thermal data. Vegetation index and brightness parameters were computed to study land use differences and their impact on vegetation and soil, temporal and phenological variations. The thermal data, in conjunction with the vegetation index results were used to infer differences in moisture, condition and stress throughout the wet and dry seasons. This was accompanied by a ground-based campaign to analyze the soil and biophysical properties of the surface. Soil color, texture and reflectance were measured on both sides of the border along with plant cover and biomass sampling. Significant differences in biomass cover and dry/green biomass ratios were found. The Mexican side had a more intense grazing use, which resulted in a lack of dry plant material, as well as lower amount of green growth. The normalized vegetation index (NDVI) and brightness clearly showed vegetation differences across the border while thermal differences were more subtle.
Huete, A.R., Liu, H.Y. & Liu, H.Q. 1994, 'Directional vegetation index interactions in ASAS imagery', International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp. 1813-1815.
The use of vegetation indices for monitoring the Earth's vegetation must not only produce composited, cloud-free imagery but must also consider and adjust land surface and atmospheric directional effects. The view angle profiles of various vegetation indices along the principal plane to the sun are investigated with ASAS imagery. The normalized difference vegetation index (NDVI) and several soil- and atmospheric-correcting versions of the NDVI were analyzed to examine land surface anisotropic effects for both land cover discrimination and for the temporal compositing of the vegetation index (VI).
Post, D.F., Bryant, R.B., Batchily, A.K., Huete, A.R., Levine, S.J., Mays, M.D. & Escadafal, R. 1993, 'Correlations between field and laboratory measurements of soil color', SSSA Special Publication (Soil Science Society of America), Publ by Soil Science Soc of America, pp. 35-49.
We prepared sets of <2 mm soil samples, distributed them to soil scientists, and asked them to determine the dry and moist Munsell color of each soil. We observed that soil scientists agreed on the same color chip for a single color component (hue, value, or chroma) 71% of the time, and there was an average of 52% agreement for all three color components. The standard deviation (SD) varied from 0.45 (value-moist) to 0.68 (chroma-moist) with an average SD of 0.57. Regression equations were computed that compared the mean soil color with the nearest color chip noted by an individual soil scientist, and the coefficient of simple determination (r2) ranged from 0.49 (chroma-moist) to 0.79 (value-moist and dry). When `in-between' colors were estimated the r2 improved and ranged from 0.70 (chroma-moist) to 0.95 (value-moist). A detailed evaluation was made of data from a commercial tristimulus colorimeter, and results were compared to colors described by soil scientists. The r2 ranged from 0.88 (chroma-moist) to 0.96 (value-dry); however the slopes and intercepts were different. Commercial colorimeters have great potential as tools for measuring soil colors, but field colors by soil scientists are not identical to instrumental data. They differ because the sensor, light source, and angle of light refraction are different for each color measurement method. The inherent complexity of color identification by humans vs. instrumental measurements is also a contributing factor.
HUETE, A., CHEHBOUNI, A., QI, J. & MICHIGAN, E.R.I. 1992, 'MULTITEMPORAL COMPOSITING OF SATELLITE DATA FOR IMPROVED GLOBAL CHANGE DETECTION', PROCEEDINGS OF THE 24TH INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT, VOLS 1 AND 2, pp. 993-1006.
Qi, J., Chehbouni, A., Huete, A., Kerr, Y. & Sorooshian, S. 1991, 'Scaling of vegetation indices for environmental change studies', Proceedings of the International Symposium on Remote Sensing of Environment, Publ by Environmental Research Inst of Michigan, pp. 579-590.
The spatial integration of physical parameters in remote sensing studies is of critical concern when evaluating the global biophysical processes on the earth's surface. When high resolution physical parameters, such as vegetation indices, are degraded for integration into global scale studies, they differ from lower spatial resolution data due to spatial variability and the method by which these parameters are integrated. In this study, multi-spatial resolution data sets of SPOT and ground based data obtained at Walnut Gulch Experimental Watershed in southern Arizona, U.S.A. during MONSOON '90 were used. These data sets were examined to study the variations of the vegetation index parameters when integrated into coarser resolutions. Different integration methods (conventional mean and Geostatistical mean) were used in simulations of high-to-low resolutions. The sensitivity of the integrated parameters were found to vary with both the spatial variability of the area and the integration methods. Modeled equations describing the scale-dependency of the vegetation index are suggested.
ESCADAFAL, R., HUETE, A., POST, D. & MICHIGAN, E.R.I. 1990, 'ESTIMATING SOIL SPECTRAL PROPERTIES (VISIBLE AND NIR) FROM COLOR AND ROUGHNESS FIELD DATA', PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT, VOLS 1 AND 2, pp. 1263-1273.
Huete, A.R. & Escadafal, R. 1990, 'Assessment of soil-vegetation-senesced materials with spectral mixture modeling: Preliminary analysis', Digest - International Geoscience and Remote Sensing Symposium (IGARSS), Publ by IEEE, pp. 1621-1624.
Continuous spectroradiometric data (0.40 to 1.0 ?m) were obtained for various rangeland sites of varying green grass, senesced grass, perennial shrub, litter, and soil background components using ground- and aircraft-based sensors. Spectral decomposition and mixture modeling were utilized for spectral isolation of subpixel components and the separation of green vegetation from senesced and soil materials. The analysis revealed that the entire reflectance data set was composed of five to six fundamental spectral curves, which when mixed in appropriate proportions would regenerate the entire spectral signature (50 bands) of all pixels. Each basis spectral curve was physically related to distinct ground spectral features, and the mixing loadings depicted the relative amount of each constituent within a pixel.

Journal articles

Haberle, S.G., Bowman, D.M.J.S., Newnham, R.M., Johnston, F.H., Beggs, P.J., Buters, J., Campbell, B., Erbas, B., Godwin, I., Green, B.J., Huete, A., Jaggard, A.K., Medek, D., Murray, F., Newbigin, E., Thibaudon, M., Vicendese, D., Williamson, G.J. & Davies, J.M. 2014, 'The macroecology of airborne pollen in Australian and New Zealand urban areas', PLoS ONE, vol. 9, no. 5.
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The composition and relative abundance of airborne pollen in urban areas of Australia and New Zealand are strongly influenced by geographical location, climate and land use. There is mounting evidence that the diversity and quality of airborne pollen is substantially modified by climate change and land-use yet there are insufficient data to project the future nature of these changes. Our study highlights the need for long-term aerobiological monitoring in Australian and New Zealand urban areas in a systematic, standardised, and sustained way, and provides a framework for targeting the most clinically significant taxa in terms of abundance, allergenic effects and public health burden.
Obata, K. & Huete, A.R. 2014, 'Scaling effects on area-averaged fraction of vegetation cover derived using a linear mixture model with two-band spectral vegetation index constraints', Journal of Applied Remote Sensing, vol. 8, no. 1.
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This study investigated the mechanisms underlying the scaling effects that apply to a fraction of vegetation cover (FVC) estimates derived using two-band spectral vegetation index (VI) isoline-based linear mixture models (VI isoline-based LMM). The VIs included the normalized difference vegetation index, a soil-adjusted vegetation index, and a two-band enhanced vegetation index (EVI2). This study focused in part on the monotonicity of an area-averaged FVC estimate as a function of spatial resolution. The proof of monotonicity yielded measures of the intrinsic area-averaged FVC uncertainties due to scaling effects. The derived results demonstrate that a factor ?, which was defined as a function of "true" and "estimated" endmember spectra of the vegetated and nonvegetated surfaces, was responsible for conveying monotonicity or nonmonotonicity. The monotonic FVC values displayed a uniform increasing or decreasing trend that was independent of the choice of the two-band VI. Conditions under which scaling effects were eliminated from the FVC were identified. Numerical simulations verifying the monotonicity and the practical utility of the scaling theory were evaluated using numerical experiments applied to Landsat7-Enhanced Thematic Mapper Plus (ETM+) data. The findings contribute to developing scale-invariant FVC estimation algorithms for multisensor and data continuity. 2014 Society of Photo-Optical Instrumentation Engineers.
Ashourloo, D., Mobasheri, M.R. & Huete, A. 2014, 'Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina)', Remote Sensing, vol. 6, no. 6, pp. 4723-4740.
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Spectral vegetation indices (SVIs) have been widely used to detect different plant diseases. Wheat leaf rust manifests itself as an early symptom with the leaves turning yellow and orange. The sign of advancing disease is the leaf colour changing to brown while the final symptom is when the leaf becomes dry. The goal of this work is to develop spectral disease indices for the detection of leaf rust. The reflectance spectra of the wheat's infected and non-infected leaves at different disease stages were collected using a spectroradiometer. As ground truth, the ratio of the disease-affected area to the total leaf area and the fractions of the different symptoms were extracted using an RGB digital camera. Fractions of the various disease symptoms extracted by the digital camera and the measured reflectance spectra of the infected leaves were used as input to the spectral mixture analysis (SMA). Then, the spectral reflectance of the different disease symptoms were estimated using SMA and the least squares method. The reflectance of different disease symptoms in the 450~1000 nm were studied carefully using the Fisher function. Two spectral disease indices were developed based on the reflectance at the 605, 695 and 455 nm wavelengths. In both indices, the R2 between the estimated and the observed was as highas 0.94. 2014 by the authors; licensee MDPI, Basel, Switzerland.
Ashourloo, D., Mobasheri, M.R. & Huete, A. 2014, 'Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements', REMOTE SENSING, vol. 6, no. 6, pp. 5107-5123.
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Broich, M., Huete, A., Tulbure, M.G., Ma, X., Xin, Q., Paget, M., Restrepo-Coupe, N., Davies, K.P., Devadas, R. & Held, A. 2014, 'Land surface phenological response to decadal climate variability across Australia using satellite remote sensing', Biogeosciences Discussions, vol. 11, pp. 7685-7719.
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Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually reoccurring patterns. Yet, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e. drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here we focused on Australia, the driest inhabited continent with one of the most variable rainfall climates in the world and vast areas of dryland systems. Detailed and internally consistent studies investigating phenological cycles and their response to climate variability across the entire continent designed specifically for Australian dryland conditions are missing. To fill this knowledge gap and to advance phenological research, we used existing methods more effectively to study geographic and climate-driven variability in phenology over Australia. We linked derived phenological metrics with rainfall and the Southern Oscillation Index (SOI). We based our analysis on Enhanced Vegetation Index (EVI) data from the MODerate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2013, which included extreme drought and wet years. We conducted a continent-wide investigation of the link between phenology and climate variability and a more detailed investigation over the MurrayDarling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles. Phenological cycle peaks occurred not only during the austral summer but at any time of the year, and their timing varied by more than a month in the interior of the continent. The phenological cycle peak magnitude and integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over north-eastern Australia and within the MDB predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of productivity) showed positive anomalies of more than two standard deviations over most of eastern Australia in 20092010, which coincided with the transition between the El Nio induced decadal droughts to flooding caused by La Nia. The quantified spatial-temporal variability in phenology across Australia in response to climate variability presented here provides important information for land management and climate change studies and applications.
Zhang, Y., Guanter, L., Berry, J.A., Joiner, J., van der Tol, C., Huete, A., Gitelson, A., Voigt, M. & Khler, P. 2014, 'Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models', Global Change Biology, vol. 20, no. 12, pp. 3727-3742.
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Photosynthesis simulations by terrestrial biosphere models are usually based on the Farquhar's model, in which the maximum rate of carboxylation (Vcmax) is a key control parameter of photosynthetic capacity. Even though Vcmax is known to vary substantially in space and time in response to environmental controls, it is typically parameterized in models with tabulated values associated to plant functional types. Remote sensing can be used to produce a spatially continuous and temporally resolved view on photosynthetic efficiency, but traditional vegetation observations based on spectral reflectance lack a direct link to plant photochemical processes. Alternatively, recent space-borne measurements of sun-induced chlorophyll fluorescence (SIF) can offer an observational constraint on photosynthesis simulations. Here, we show that top-of-canopy SIF measurements from space are sensitive to Vcmax at the ecosystem level, and present an approach to invert Vcmax from SIF data. We use the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model to derive empirical relationships between seasonal Vcmax and SIF which are used to solve the inverse problem. We evaluate our Vcmax estimation method at six agricultural flux tower sites in the midwestern US using spaced-based SIF retrievals. Our Vcmax estimates agree well with literature values for corn and soybean plants (average values of 37 and 101 ?mol m-2 s-1, respectively) and show plausible seasonal patterns. The effect of the updated seasonally varying Vcmax parameterization on simulated gross primary productivity (GPP) is tested by comparing to simulations with fixed Vcmax values. Validation against flux tower observations demonstrate that simulations of GPP and light use efficiency improve significantly when our time-resolved Vcmax estimates from SIF are used, with R2 for GPP comparisons increasing from 0.85 to 0.93, and for light use efficiency from 0.44 to 0.83. Our results support the use of space-based SIF data as a proxy for photosynthetic capacity and suggest the potential for global, time-resolved estimates of Vcmax.
Susan Moran, M., Ponce-Campos, G.E., Huete, A., McClaran, M.P., Zhang, Y., Hamerlynck, E.P., Augustine, D.J., Gunter, S.A., Kitchen, S.G., Peters, D.P.C., Starks, P.J. & Hernandez, M. 2014, 'Functional response of U.S. grasslands to the early 21st-century drought', Ecology, vol. 95, no. 8, pp. 2121-2133.
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Grasslands across the United States play a key role in regional livelihood and national food security. Yet, it is still unclear how this important resource will respond to the prolonged warm droughts and more intense rainfall events predicted with climate change. The early 21st-century drought in the southwestern United States resulted in hydroclimatic conditions that are similar to those expected with future climate change. We investigated the impact of the early 21st-century drought on aboveground net primary production (ANPP) of six desert and plains grasslands dominated by C4 (warm season) grasses in terms of significant deviations between observed and expected ANPP. In desert grasslands, drought-induced grass mortality led to shifts in the functional response to annual total precipitation (PT), and in some cases, new species assemblages occurred that included invasive species. In contrast, the ANPP in plains grasslands exhibited a strong linear function of the current-year PT and the previous-year ANPP, despite prolonged warm drought. We used these results to disentangle the impacts of interannual total precipitation, intra-annual precipitation patterns, and grassland abundance on ANPP, and thus generalize the functional response of C4 grasslands to predicted climate change. This will allow managers to plan for predictable shifts in resources associated with climate change related to fire risk, loss of forage, and ecosystem services. 2014 by the Ecological Society of America.
Shi, H., Li, L., Eamus, D., Cleverly, J., Huete, A., Beringer, J., Yu, Q., Van Gorsel, E. & Hutley, L. 2014, 'Intrinsic climate dependency of ecosystem light and water-use-efficiencies across Australian biomes', Environmental Research Letters, vol. 9, no. 10.
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The sensitivity of ecosystem gross primary production (GPP) to availability of water and photosynthetically active radiation (PAR) differs among biomes. Here we investigated variations of ecosystem light-use-efficiency (eLUE: GPP/PAR) and water-use-efficiency (eWUE: GPP/evapotranspiration) among seven Australian eddy covariance sites with differing annual precipitation, species composition and temperature. Changes to both eLUE and eWUE were primarily correlated with atmospheric vapor pressure deficit (VPD) at multiple temporal scales across biomes, with minor additional correlations observed with soil moisture and temperature. The effects of leaf area index on eLUE and eWUE were also relatively weak compared to VPD, indicating an intrinsic dependency of eLUE and eWUE on climate. Additionally, eLUE and eWUE were statistically different for biomes between summer and winter, except eWUE for savannas and the grassland. These findings will improve our understanding of how light- and water-use traits in Australian ecosystems may respond to climate change.
Broich, M., Huete, A., Tulbure, M.G., Ma, X., Xin, Q., Paget, M., Restrepo-Coupe, N., Davies, K., Devadas, R. & Held, A. 2014, 'Land surface phenological response to decadal climate variability across Australia using satellite remote sensing', Biogeosciences, vol. 11, no. 18, pp. 5181-5198.
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Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative spatial information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually recurring patterns. However, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e., drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here, we focused on Australia, a continent with one of the most variable rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing. To fill this knowledge gap, we developed an algorithm to characterize phenological cycles, and analyzed geographic and climate-driven variability in phenology from 2000 to 2013, which included extreme drought and wet years. We linked derived phenological metrics to rainfall and the Southern Oscillation Index (SOI). We conducted a continent-wide investigation and a more detailed investigation over the Murray-Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter-and intra-annual variability in phenological cycles across Australia. The peak of phenological cycles occurred not only during the austral summer, but also at any time of the year, and their timing varied by more than a month in the interior of the continent. The magnitude of the phenological cycle peak and the integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over northeastern Australia and within the MDB, predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of vegetation productivity) showed positive anomalies of more than 2 standard deviations over most of eastern Australia in 2009-2010, which coincided with the transition from the El Nio-induced decadal droughts to flooding caused by La Nia.
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J.A., Frankenberg, C., Huete, A.R., Zarco-Tejada, P., Lee, J.-.E., Moran, M.S., Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D., Klumpp, K., Cescatti, A., Baker, J.M. & Griffis, T.J. 2014, 'Reply to Magnani et al.: Linking large-scale chlorophyll fluorescence observations with cropland gross primary production', Proceedings of the National Academy of Sciences of the United States of America, vol. 111, no. 25.
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Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J.A., Frankenberg, C., Huete, A.R., Zarco-Tejada, P., Lee, J.-.E., Moran, M.S., Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D., Klumpp, K., Cescatti, A., Baker, J.M. & Griffis, T.J. 2014, 'Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence', Proceedings of the National Academy of Sciences of the United States of America, vol. 111, no. 14.
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Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-ofthe- art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.
Yebra, M., Van Dijk, A., Leuning, R., Huete, A. & Guerschman, J.P. 2013, 'Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance', Remote Sensing of Environment, vol. 129, pp. 250-261.
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We compared estimates of actual evapotranspiration (ET) produced with six different vegetation measures derived from the MODerate resolution Imaging Spectroradiometer (MODIS) and three contrasting estimation approaches using measurements from eddy covariance flux towers at 16 FLUXNET sites located over six different land cover types. The aim was to assess optimal approaches in using optical remote sensing to estimate ET. The first two approaches directly regressed various MODIS vegetation indices (VIs) and products such as leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) with ET and evaporative fraction (EF). In the third approach, the Penman-Monteith (PM) equation was inverted to obtain surface conductance (Gs), for dry plant canopies. The Gs values were then regressed against the MODIS data products and used to parameterize the PM equation for retrievals of ET. Jack-Knife cross-validation was used to evaluate the various regression models against observed ET. The PM-Gs approach provided the lowest root mean square error (RMSE), and highest determination coefficients (R2) across all sites, with an average RMSE=38Wm-2 and R2=0.72. Direct regressions of observed ET against the VIs resulted in an average RMSE=60Wm-2 and R2=0.22, while the EF regressions an average RMSE=42Wm-2 and R2=0.64. The MODIS LAI and fPAR product produced the poorest estimates of ET (RMSE>44Wm-2 and R2<0.6); while the VIs each performed best for some of the land cover types. The enhanced vegetation index (EVI) produced the best ET estimates for evergreen needleleaf forest (RMSE=28.4Wm-2, R2=0.66). The normalized difference vegetation index (NDVI) best estimated ET in grassland (RMSE=23.8Wm-2 and R2=0.68), cropland (RMSE=29.2Wm-2 and R2=0.86) and woody savannas (RMSE=25.4Wm-2 and R2=0.82), while the VI-based crop coefficient (Kc) yielded the best estimates for evergreen and deciduous broadleaf forests (RMSE=27Wm-2 and R2=0.7 in both cases). Using the ensemble-average of ET as estimated using NDVI, EVI and Kc we computed global grids of dry canopy conductance (Gc) from which annual statistics were extracted to characterise different functional types. The resulting Gc values can be used to parameterize land surface models. 2012 Elsevier Inc.
Ponce Campos, G.E., Moran, M.S., Huete, A., Zhang, Y., Bresloff, C., Huxman, T.E., Eamus, D., Bosch, D.D., Buda, A.R., Gunter, S.A., Scalley, T.H., Kitchen, S.G., Mcclaran, M.P., Mcnab, W.H., Montoya, D.S., Morgan, J.A., Peters, D.P.C., Sadler, E.J., Seyfried, M.S. & Starks, P.J. 2013, 'Ecosystem resilience despite large-scale altered hydroclimatic conditions', Nature, vol. 494, no. 7437, pp. 349-352.
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Climate change is predicted to increase both drought frequency and duration, and when coupled with substantial warming, will establish a new hydroclimatological model for many regions. Large-scale, warm droughts have recently occurred in North America, Africa, Europe, Amazonia and Australia, resulting in major effects on terrestrial ecosystems, carbon balance and food security. Here we compare the functional response of above-ground net primary production to contrasting hydroclimatic periods in the late twentieth century (1975-1998), and drier, warmer conditions in the early twenty-first century (2000-2009) in the Northern and Southern Hemispheres. We find a common ecosystem water-use efficiency (WUE e: Above-ground net primary production/ evapotranspiration) across biomes ranging from grassland to forest that indicates an intrinsic system sensitivity to water availability across rainfall regimes, regardless of hydroclimatic conditions. We found higher WUE e in drier years that increased significantly with drought to a maximum WUE e across all biomes; and a minimum native state in wetter years that was common across hydroclimatic periods. This indicates biome-scale resilience to the interannual variability associated with the early twenty-first century drought - that is, the capacity to tolerate low, annual precipitation and to respond to subsequent periods of favourable water balance. These findings provide a conceptual model of ecosystem properties at the decadal scale applicable to the widespread altered hydroclimatic conditions that are predicted for later this century. Understanding the hydroclimatic threshold that will break down ecosystem resilience and alter maximum WUE e may allow us to predict land-surface consequences as large regions become more arid, starting with water-limited, low-productivity grasslands. 2013 Macmillan Publishers Limited. All rights reserved.
Zhang, Y., Susan Moran, M., Nearing, M.A., Ponce Campos, G.E., Huete, A.R., Buda, A.R., Bosch, D.D., Gunter, S.A., Kitchen, S.G., Henry McNab, W., Morgan, J.A., McClaran, M.P., Montoya, D.S., Peters, D.P.C. & Starks, P.J. 2013, 'Extreme precipitation patterns and reductions of terrestrial ecosystem production across biomes', Journal of Geophysical Research: Biogeosciences, vol. 118, no. 1, pp. 148-157.
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Precipitation regimes are predicted to shift to more extreme patterns that are characterized by more heavy rainfall events and longer dry intervals, yet their ecological impacts on vegetation production remain uncertain across biomes in natural climatic conditions. This in situ study investigated the effects of these climatic conditions on aboveground net primary production (ANPP) by combining a greenness index from satellite measurements and climatic records during 2000-2009 from 11 long-term experimental sites in multiple biomes and climates. Results showed that extreme precipitation patterns decreased the sensitivity of ANPP to total annual precipitation (PT) at the regional and decadal scales, leading to decreased rain use efficiency (RUE; by 20% on average) across biomes. Relative decreases in ANPP were greatest for arid grassland (16%) and Mediterranean forest (20%) and less for mesic grassland and temperate forest (3%). The cooccurrence of heavy rainfall events and longer dry intervals caused greater water stress conditions that resulted in reduced vegetation production. A new generalized model was developed using a function of both PT and an index of precipitation extremes and improved predictions of the sensitivity of ANPP to changes in precipitation patterns. Our results suggest that extreme precipitation patterns have substantially negative effects on vegetation production across biomes and are as important as P T. With predictions of more extreme weather events, forecasts of ecosystem production should consider these nonlinear responses to altered extreme precipitation patterns associated with climate change. Key Points Extreme rainfall events reduced the sensitivity of ANPP to total annual rainfallCo-occurrence of intense rainfall and longer dry interval reduced greater ANPPA new model improved predictions of ANPP by accounting for extreme patterns 2012. American Geophysical Union. All Rights Reserved.
Miura, T., Turner, J.P. & Huete, A.R. 2013, 'Spectral compatibility of the NDVI across VIIRS, MODIS, and AVHRR: An analysis of atmospheric effects using EO-1 hyperion', IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 3, pp. 1349-1359.
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We evaluated the cross-sensor compatibilities of the normalized difference vegetation index (NDVI) across the Visible/Infrared Imager/Radiometer Suite (VIIRS), Moderate Resolution Imaging Spectroradiometer (MODIS), and the National Oceanic and Atmospheric Administration (NOAA)-14 and NOAA-19 Advanced Very High Resolution Radiometer (AVHRR) (AVHRR/2 and AVHRR/3) bandpasses using a global set of Earth Observing One Hyperion hyperspectral data. Five levels of atmospheric correction were simulated to examine the impact of the atmosphere on intersensor NDVI compatibility. These were the uncorrected 'top-of-atmosphere'; Rayleigh (RAY); Rayleigh and ozone (RO); Rayleigh, ozone, and water vapor (ROW); and total atmosphere-corrected 'top-of-canopy (TOC)' reflectances. Among all possible sensor pairs examined, the highest compatibility was observed for VIIRS versus MODIS. Cross-sensor NDVI relationships between the two sensor bandpasses remained nearly the same throughout all levels of atmospheric correction. AVHRR/3-versus-AVHRR/2 NDVI relationships changed very little and also showed an equivalent level of compatibility to VIIRS versus MODIS across all levels of atmospheric correction although they were subject to systematic differences. Intersensor NDVI compatibilities of VIIRS and MODIS to AVHRR/2 and to AVHRR/3 were lower due primarily to the differential sensitivities of these sensors' near-infrared bands to the atmospheric water vapor effects. Comparisons of cross-sensor NDVI compatibilities where operational atmospheric correction schemes were assumed for each of the sensors suggest the need of VIIRS TOC NDVI for long-term continuity with MODIS and AVHRR, which is not currently produced as part of the standard VIIRS Vegetation Index Environmental Data Record. 2012 IEEE.
Monteiro, A.T., Fava, F., Gonalves, J., Huete, A., Gusmeroli, F., Parolo, G., Spano, D. & Bocchi, S. 2013, 'Landscape context determinants to plant diversity in the permanent meadows of Southern European Alps', Biodiversity and Conservation, vol. 22, no. 4, pp. 937-958.
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In the Southern Alps, the role of landscape context on meadows plant diversity was evaluated using a multi-model information theoretic approach and five competing hypotheses of landscape context factors: habitat quality (H1), matrix quality (H2), habitat change (H3), matrix quality change (H4) and topography-environmental conditions (H5)- measured at three spatial scales (125, 250 and 500 m). Shannon diversity index and species richness represented plant diversity obtained in 34 plots (100 m2 size). Landscape context affected plant diversity measures differently. Matrix quality change at larger scale (500 m) was the most supported hypothesis explaining Shannon diversity index, while species richness responded mostly to topography-environmental conditions in the immediate surroundings (125 m). No effects of present-day habitat and matrix quality (H1 and H2) were found. Matrix quality change affected positively Shannon diversity index through an effect of landscape neighbourhood context on farming management practices. Due to the importance of exposure and inclination of slopes, topography-environmental conditions influenced species richness mostly through energy-driven processes and farming management strategies. In terms of scale, matrix quality change was the strongest hypothesis explaining Shannon diversity index at all scales, while the underlying process affecting species richness changed with scale (H5 or H3). Overall, landscape context explained only 25-28 % of the variation in plant diversity, suggesting that landscape management may support biodiversity conservation when comprised in a global strategy including farming practices. In the study area, change in landscape diversity may be a good indicator for Shannon diversity index and south-eastern facing meadows should be preserved. 2013 Springer Science+Business Media Dordrecht.
Ma, X., Huete, A., Yu, Q., Coupe, N.R., Davies, K., Broich, M., Ratana, P., Beringer, J., Hutley, L.B., Cleverly, J., Boulain, N. & Eamus, D. 2013, 'Spatial patterns and temporal dynamics in savanna vegetation phenology across the north australian tropical transect', Remote Sensing of Environment, vol. 139, pp. 97-115.
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The phenology of a landscape is a key parameter in climate and biogeochemical cycle models and its correct representation is central to the accurate simulation of carbon, water and energy exchange between the land surface and the atmosphere. Whereas biogeographic phenological patterns and shifts have received much attention in temperate ecosystems, much less is known about the phenology of savannas, despite their sensitivity to climate change and their coverage of approximately one eighth of the global land surface. Savannas are complex assemblages of multiple tree, shrub, and grass vegetation strata, each with variable phenological responses to seasonal climate and environmental variables. The objectives of this study were to investigate biogeographical and inter-annual patterns in savanna phenology along a 1100. km ecological rainfall gradient, known as North Australian Tropical Transect (NATT), encompassing humid coastal Eucalyptus forests and woodlands to xeric inland Acacia woodlands and shrublands. Key phenology transition dates (start, peak, end, and length of seasonal greening periods) were extracted from 13. years (2000-2012) of Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data using Singular Spectrum Analysis (SSA).Two distinct biogeographical patterns in phenology were observed, controlled by different climate systems. The northern (mesic) portion of the transect, from 12S, to around 17.7S, was influenced by the Inter-Tropical Convergence Zone (ITCZ) seasonal monsoon climate system, resulting in strong latitudinal shifts in phenology patterns, primarily associated with the functional response of the C4 grass layer. Both the start and end of the greening (enhanced vegetation activity) season occurred earlier in the northern tropical savannas and were progressively delayed towards the southern limit of the Eucalyptus-dominated savannas resulting in relatively stable length of greening periods. In contrast, the southern xeric portion of the study area was largely decoupled from monsoonal influences and exhibited highly variable phenology that was largely rainfall pulse driven. The seasonal greening periods were generally shorter but fluctuated widely from no detectable greening during extended drought periods to length of greening seasons that exceeded those in the more mesic northern savannas in some wet years. This was in part due to more extreme rainfall variability, as well as a C3/C4 grass-forb understory that provided the potential for extended greening periods. Phenology of Acacia dominated savannas displayed a much greater overall responsiveness to hydroclimatic variability. The variance in annual precipitation alone could explain 80% of the variances in the length of greening season across the major vegetation groups. We also found that increased variation in the timing of phenology was coupled with a decreasing tree-grass ratio. We further compared the satellite-based phenology results with tower-derived measures of Gross Ecosystem Production (GEP) fluxes at three sites over two contrasting savanna classes. We found good convergence between MODIS EVI and tower GEP, thereby confirming the potential to link these two independent data sources to better understand savanna ecosystem functioning. 2013 Elsevier Inc.
Mariotto, I., Thenkabail, P.S., Huete, A., Slonecker, E.T. & Platonov, A. 2013, 'Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission', Remote Sensing of Environment, vol. 139, pp. 291-305.
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Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM+), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of ?1 (400-2500nm) versus ?2 (400-2500nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742nm and 1175nm (HVI742-1175), (ii) 1296nm and 1054nm (HVI1296-1054), (iii) 1225nm and 697nm (HVI1225-697), and (iv) 702nm and 1104nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051-2331nm spectral range, followed by 10% in the moisture sensitive 970nm, 6% in the red and red-edge (630-752nm), and the remaining 10% distributed between blue (400-500nm), green (501-600nm), and NIR (760-900nm).Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (>. 90%) over MBBs data (varied between 45 and 84%).Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data). 2013 Elsevier Inc.
Peng, D., Jiang, Z., Huete, A.R., Ponce-Campos, G.E., Nguyen, U. & Luvall, J.C. 2013, 'Response of spectral reflectances and vegetation indices on varying juniper cone densities', Remote Sensing, vol. 5, no. 10, pp. 5330-5345.
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Juniper trees are widely distributed throughout the world and are common sources of allergies when microscopic pollen grains are transported by wind and inhaled. In this study, we investigated the spectral influences of pollen-discharging male juniper cones within a juniper canopy. This was done through a controlled outdoor experiment involving ASD FieldSpec Pro Spectroradiometer measurements over juniper canopies of varying cone densities. Broadband and narrowband spectral reflectance and vegetation index (VI) patterns were evaluated as to their sensitivity and their ability to discriminate the presence of cones. The overall aim of this research was to assess remotely sensed phenological capabilities to detect pollen-bearing juniper trees for public health applications. A general decrease in reflectance values with increasing juniper cone density was found, particularly in the Green (545-565 nm) and NIR (750-1,350 nm) regions. In contrast, reflectances in the shortwave-infrared (SWIR, 2,000 nm to 2,350 nm) region decreased from no cone presence to intermediate amounts (90 g/m2) and then increased from intermediate levels tothe highest cone densities (200 g/m2). Reflectance patterns in the Red (620-700 nm) were more complex due to shifting contrast patterns in absorptance between cones and juniper foliage, where juniper foliage is more absorbing than cones only within the intense narrowband region of maximum chlorophyll absorption near 680 nm. Overall, narrowband reflectances were more sensitive to cone density changes than the equivalent MODIS broadbands. In all VIs analyzed, there were significant relationships with cone density levels, particularly with the narrowband versions and the two-band vegetation index (TBVI) based on Green and Red bands, a promising outcome for the use of phenocams in juniper phenology trait studies. These results indicate that spectral indices are sensitive to certain juniper phenologic traits that can potentially be used for juniper cone detection in support of public health applications. 2013 by the authors.
Potgieter, A.B., Lawson, K. & Huete, A.R. 2013, 'Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery', International Journal of Applied Earth Observation and Geoinformation, vol. 23, no. 1, pp. 254-263.
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There are increasing societal and plant industry demands for more accurate, objective and near realtime crop production information to meet both economic and food security concerns. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to monitor large agricultural areas at acceptable pixel scale, cost and accuracy. Fitting parametric profiles to growing season vegetation index time series reduces the volume of data and provides simple quantitative parameters that relates to crop phenology (sowing date, flowering). In this study, we modelled various Gaussian profiles to time sequential MODIS enhanced vegetation index (EVI) images over winter crops in Queensland, Australia. Three simple Gaussian models were evaluated in their effectiveness to identify and classify various winter crop types and coverage at both pixel and regional scales across Queensland's main agricultural areas. Equal to or greater than 93% classification accuracies were obtained in determining crop acreage estimates at pixel scale for each of the Gaussian modelled approaches. Significant high to moderate correlations (log-linear transformation) were also obtained for determining total winter crop (R2 = 0.93) areas as well as specific crop acreage for wheat (R2 = 0.86) and barley (R2 = 0.83). Conversely, it was much more difficult to predict chickpea acreage (R2 ? 0.26), mainly due to very large uncertainties in survey data. The quantitative approach utilised here further had additional benefits of characterising crop phenology in terms of length of growing season and providing regression diagnostics of how well the fitted profiles matched the EVI time series. The Gaussian curve models utilised here are novel in application and therefore will enhance the use and adoption of remote sensing technologies in targeted agricultural application. With innate simplicity and accuracies comparable to other more convoluted multi-temporal approaches it is a good candidate in determining total and specific crop acreage estimates in future national and global food security frameworks. 2012 Published by Elsevier B.V.
Medek, D., Vicendese, D., Jaggard, A., Campbell, B., Johnston, F., Godwin, I., Huete, A., Green, B., Newnham, R., Bowman, D., Newbigin, E., Erbas, B., Beggs, P., Haberle, S. & Davies, J. 2013, 'REGIONAL AND SEASONAL VARIATION IN AIRBORNE GRASS POLLEN LEVELS BETWEEN CITIES OF AUSTRALIA AND NEW ZEALAND', INTERNAL MEDICINE JOURNAL, vol. 43, pp. 7-7.
Obata, K., Miura, T., Yoshioka, H. & Huete, A.R. 2013, 'Derivation of a MODIS-compatible enhanced vegetation index from visible infrared imaging radiometer suite spectral reflectances using vegetation isoline equations', Journal of Applied Remote Sensing, vol. 7, no. 1.
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We developed a unique methodology that spectrally translates the enhanced vegetation index (EVI) across sensors for data continuity based on vegetation isoline equations and derived a moderate resolution imaging spectroradiometer (MODIS)-compatible EVI for the visible/infrared imager/radiometer suite (VIIRS) sensor. The derived equation had four coefficients that were a function of soil, canopy, and atmosphere, e.g., soil line slope, leaf area index (LAI), and aerosol optical thickness (AOT). The PROSAIL canopy reflectance and 6S atmospheric models were employed to numerically characterize the MODIS-compatible VIIRS EVI. MODIS-compatible VIIRS EVI values only differed from those of MODIS EVI by, at most, 0.002 EVI units, whereas VIIRS and MODIS EVI values differed by 0.018 EVI units. The derived coefficients were sensitive mainly to LAI and AOT for the full-and a partial-covered canopy, respectively. The MODIS-compatible EVI resulted in a reasonable level of accuracy when the coefficients were fixed at values found via optimization for model-simulated and actual sensor data (83 and 41% reduction in the root mean square error, respectively), demonstrating the potential practical utility of the derived equation. The developed methodology can be used to obtain a spectrally compatible EVI for any pair of sensors in the data continuity context. The Authors.
Bargain, A., Robin, M., Le Men, E., Huete, A. & Barill, L. 2012, 'Spectral response of the seagrass Zostera noltii with different sediment backgrounds', Aquatic Botany, vol. 98, no. 1, pp. 45-56.
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The efficiency of vegetation indices (VIs) to estimate the above-ground biomass of the seagrass species Zostera noltii Hornem. from remote sensing was tested experimentally on different substrata, since terrestrial vegetation studies have shown that VIs can be adversely influenced by the spectral properties of soils and background surfaces. Leaves placed on medium sand, fine sand and autoclaved fine sand were incrementally removed, and the spectral reflectance was measured in the 400-900nm wavelength range. Several VIs were evaluated: ratios using visible and near infrared wavelengths, narrow-band indices, indices based on derivative analysis and continuum removal. Background spectral reflectance was clearly visible in the leaf reflectance spectra, showing marked brightness and spectral contrast variations for the same amount of vegetation. Paradoxically, indices used to minimize soil effects, such as the Soil-Adjusted Vegetation Index (SAVI) and the Modified second Soil-Adjusted Vegetation Index (MSAVI 2) showed a high sensitivity to background effects. Similar results were found for the widely used Normalized Difference Vegetation Index (NDVI) and for Pigment Specific Simple Ratios (PSSRs). In fact, background effects were most reduced for VIs integrating a blue band correction, namely the modified specific ratio (mSR (705)), the modified Normalized Difference (mND (705)), and two modified NDVIs proposed in this study. However, these indices showed a faster saturation for high seagrass biomass. The background effects were also substantially reduced using Modified Gaussian Model indices at 620 and 675nm. The blue band corrected VIs should now be tested for air-borne or satellite remote sensing applications, but some require sensors with a hyperspectral resolution. Nevertheless, this type of index can be applied to analyse broad band multispectral satellite images with a blue band. 2011 Elsevier B.V.
Huete, A. 2012, 'Vegetation indices, remote sensing and forest monitoring', Geography Compass, vol. 6, no. 9, pp. 513-532.
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With increasing threats and pressure exerted on Earth's forest resources, there are greater demands for more quantitative, timely, and accurate information on their state, functioning, and sustainability. Satellite remote sensing offers an effective way of measuring and monitoring vast forest areas in a consistent and robust manner. This complements ground forest surveys and overcomes the spatial limitations of in situ sampling of forest biophysical properties. Among the various remote sensing tools used in characterizing forests, spectral vegetation indices (VIs) are widely adopted for monitoring forest states and canopy processes. In this article I provide a brief overview on VI applications and advances made in the assessment and monitoring of forest biophysical states, functioning, phenology, and disturbance. I also address current and future challenges, demands, and limitations of VIs for long term forest monitoring and applications in climate science, hydrology, and biogeochemistry.
Huete, A.R. 2012, 'Vegetation Indices, Remote Sensing and Forest Monitoring', Geography Compass, vol. 6, no. 9, pp. 513-532.
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With increasing threats and pressure exerted on Earth's forest resources, there are greater demands for more quantitative, timely, and accurate information on their state, functioning, and sustainability. Satellite remote sensing offers an effective way of measuring and monitoring vast forest areas in a consistent and robust manner. This complements ground forest surveys and overcomes the spatial limitations of in situ sampling of forest biophysical properties. Among the various remote sensing tools used in characterizing forests, spectral vegetation indices (VIs) are widely adopted for monitoring forest states and canopy processes. In this article I provide a brief overview on VI applications and advances made in the assessment and monitoring of forest biophysical states, functioning, phenology, and disturbance. I also address current and future challenges, demands, and limitations of VIs for long term forest monitoring and applications in climate science, hydrology, and biogeochemistry. 2012 The Author. Geography Compass 2012 Blackwell Publishing Ltd.
Peng, D., Huete, A.R., Huang, J., Wang, F. & Sun, H. 2011, 'Detection and estimation of mixed paddy rice cropping patterns with MODIS data', International Journal of Applied Earth Observation and Geoinformation, vol. 13, no. 1, pp. 13-23.
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In this paper, we developed a more sophisticated method for detection and estimation of mixed paddy rice agriculture from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Previous research demonstrated that MODIS data can be used to map paddy rice fields and to distinguish rice from other crops at large, continental scales with combined Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) analysis during the flooding and rice transplanting stage. Our approach improves upon this methodology by incorporating mixed rice cropping patterns that include single-season rice crops, early-season rice, and late-season rice cropping systems. A variable EVI/LSWI threshold function, calibrated to more local rice management practices, was used to recognize rice fields at the flooding stage. We developed our approach with MODIS data in Hunan Province, China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns. We further mapped the aerial coverage and distribution of early, late, and single paddy rice crops for several years from 2000 to 2007 in order to quantify temporal trends in rice crop coverage, growth and management systems. Our results were validated with finer resolution (2.5m) Satellite Pour l'Observation de la Terre 5 High Resolution Geometric (SPOT 5 HRG) data, land-use data at the scale of 1/10,000 and with county-level rice area statistical data. The results showed that all three paddy rice crop patterns could be discriminated and their spatial distribution quantified. We show the area of single crop rice to have increased annually and almost doubling in extent from 2000 to 2007, with simultaneous, but unique declines in the extent of early and late paddy rice. These results were significantly positive correlated and consistent with agricultural statistical data at the county level (P<0.01). 2010 Elsevier B.V.
Anderson, L.O., Arago, L.O.C., Shimabukuro, Y.E., Almeida, S. & Huete, A. 2011, 'Fraction images for monitoring intra-annual phenology of different vegetation physiognomies in Amazonia', International Journal of Remote Sensing, vol. 32, no. 2, pp. 387-408.
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In this study we investigate the potential of fraction images derived from a linear spectral mixture model to detect vegetation phenology in Amazonia, and evaluate their relationships with the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices. Time series ofMODIS 250-m data over three contrasting land cover types in the Amazon were used in conjunction with rainfall data, a land covermap and a forest inventory survey to support the interpretation of our findings. Each vegetation physiognomy was characterized by a particular intra-annual variability detected by a combination of the fraction images. Both vegetation and shade fractions were important to evaluate the seasonality of the open tropical forest (OTF). The association of these results with forest inventory data and the literature suggests that Enhanced Vegetation Index (EVI) and vegetation fraction images are sensitive to structural changes in the canopy of OTF. In cerrado grassland (CG) the phenology was better characterized by combined soil and vegetation fractions. Soybean (SB) areas were characterized by the highest ranges in the vegetation and soil fraction images. Vegetation fraction and vegetation indices for the OTF showed a significant positive relationship with EVI but not with Normalized Difference Vegetation Index (NDVI). Significant relationships for vegetation fraction and vegetation indices were also found for the CG and soybean areas. In contrast to vegetation index approaches to monitoring phenology, fraction images provide additional information that allows amore comprehensive exploration of the spectral and structural changes in vegetation formations. 2011 Taylor & Francis.
Glenn, E.P., Doody, T.M., Guerschman, J.P., Huete, A.R., King, E.A., Mcvicar, T.R., Van Dijk, A.I.J.M., Van Niel, T.G., Yebra, M. & Zhang, Y. 2011, 'Actual evapotranspiration estimation by ground and remote sensing methods: the Australian experience', Hydrological Processes, vol. 25, no. 26, pp. 4103-4116.
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On average, Australia is a dry continent with many competing uses for water. Hence, there is an urgent need to know actual evapotranspiration (ET a) patterns across wide areas of agricultural and natural ecosystems, as opposed to just point measurements of ET a. The Australian Government has tasked the science agencies with operationally developing monthly and annual estimates of ET a and other hydrological variables, and with forecasting water availability over periods of days to decades, as part of its national water assessment programme. To meet these needs, Australian researchers have become leaders in developing large-area methods for estimating ET a at regional and continental scales. Ground methods include meteorological models, eddy covariance towers, sap flow sensors and catchment water balance models. Remote sensing methods use thermal infrared, mid infrared and/or vegetation indices usually combined with meteorological data to estimate ET a. Ground and remote sensing ET a estimates are assimilated into the Australian Water Resource Assessment, which issues annual estimates of the state of the continental water balance for policy and planning purposes. The best ET a models are estimated to have an error or uncertainty of 10% to 20% in Australia. Developments in Australian ET a research over the past 20years are reviewed, and sources of error and uncertainty in current methods and models are discussed. Copyright 2011 John Wiley & Sons, Ltd.
Yang, X.H., Huang, J.F., Wu, Y.P., Wang, J.W., Wang, P., Wang, X.M. & Huete, A.R. 2011, 'Estimating biophysical parameters of rice with remote sensing data using support vector machines', Science China Life Sciences, vol. 54, no. 3, pp. 272-281.
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Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application. Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters, comprising leaf area index (LAI; m2 green leaf area m-2 soil) and green leaf chlorophyll density (GLCD; mg chlorophyll m-2 soil), using stepwise multiple regression (SMR) models and support vector machines (SVMs). Four transformations of the rice canopy data were made, comprising reflectances (R), first-order derivative reflectances (D1), second-order derivative reflectances (D2), and logarithm transformation of reflectances (LOG). The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI, with a root mean square error (RMSE) of 1.0496 LAI units. The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD, with an RMSE of 523.0741 mg m-2. The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters, but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data. 2011 The Author(s).
Jenerette, G.D., Scott, R.L. & Huete, A.R. 2010, 'Functional differences between summer and winter season rain assessed with MODIS-derived phenology in a semi-arid region', Journal of Vegetation Science, vol. 21, no. 1, pp. 16-30.
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Questions: We asked several linked questions about phenology and precipitation relationships at local, landscape, and regional spatial scales within individual seasons, between seasons, and between year temporal scales. (1) How do winter and summer phenological patterns vary in response to total seasonal rainfall? (2) How are phenological rates affected by the previous season rainfall? (3) How does phenological variability differ at landscape and regional spatial scales and at season and interannual temporal scales? Location: Southern Arizona, USA. Methods: We compared satellite-derived phenological variation between 38 distinct 625-km 2 landscapes distributed in the northern Sonoran Desert region from 2000 to 2007. Regression analyses were used to identify relationships between landscape phenology dynamics in response to precipitation variability across multiple spatial and temporal scales. Results:While both summer and winter seasons show increases of peak greenness and peak growth with more precipitation, the timing of peak growth was advanced with more precipitation in winter, while the timing of peak greenness was advanced with more precipitation in summer. Surprisingly, summer maximum growth was negatively affected by winter precipitation. The spatial variations between summer and winter phenology were similar in magnitude and response. Larger-scale spatial and temporal variation showed strong differences in precipitation patterns; however the magnitudes of phenological spatial variability in these two seasons were similar. Conclusions: Vegetation patterns were clearly coupled to precipitation variability, with distinct responses at alternative spatial and temporal scales. Disaggregating vegetation into phenological variation, spanning value, timing, and integrated components revealed substantial complexity in precipitation- phenological relationships. 2009 International Association for Vegetation Science.
Peng, D.-.L., Huang, J.-.F., Huete, A.R., Yang, T.-.M., Gao, P., Chen, Y.-.C., Chen, H., Li, J. & Liu, Z.-.Y. 2010, 'Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data', Journal of Zhejiang University: Science B, vol. 11, no. 4, pp. 275-285.
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We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature, precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area, a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01), and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China, NPP showed 16-d, 48-d, and 96-d lagged correlation with air temperature, precipitation, and sunshine percentage, respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d, while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region, the spatial patterns of vegetation-climate relationship became complicated and diversiform, especially for precipitation influences on NPP. In the northern part of the study area, all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP. 2010 Zhejiang University and Springer-Verlag Berlin Heidelberg.
Glenn, E.P., Nagler, P.L. & Huete, A.R. 2010, 'Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing', Surveys in Geophysics, vol. 31, no. 6, pp. 531-555.
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Evapotranspiration (ET) is the largest term after precipitation in terrestrial water budgets. Accurate estimates of ET are needed for numerous agricultural and natural resource management tasks and to project changes in hydrological cycles due to potential climate change. We explore recent methods that combine vegetation indices (VI) from satellites with ground measurements of actual ET (ETa) and meteorological data to project ETa over a wide range of biome types and scales of measurement, from local to global estimates. The majority of these use time-series imagery from the Moderate Resolution Imaging Spectrometer on the Terra satellite to project ET over seasons and years. The review explores the theoretical basis for the methods, the types of ancillary data needed, and their accuracy and limitations. Coefficients of determination between modeled ETa and measured ETa are in the range of 0.45-0.95, and root mean square errors are in the range of 10-30% of mean ETa values across biomes, similar to methods that use thermal infrared bands to estimate ETa and within the range of accuracy of the ground measurements by which they are calibrated or validated. The advent of frequent-return satellites such as Terra and planed replacement platforms, and the increasing number of moisture and carbon flux tower sites over the globe, have made these methods feasible. Examples of operational algorithms for ET in agricultural and natural ecosystems are presented. The goal of the review is to enable potential end-users from different disciplines to adapt these methods to new applications that require spatially-distributed ET estimates. 2010 Springer Science+Business Media B.V.
Ferreira, N.C., Ferreira, L.G. & Huete, A.R. 2010, 'Assessing the response of the MODIS vegetation indices to landscape disturbance in the forested areas of the legal Brazilian Amazon', International Journal of Remote Sensing, vol. 31, no. 3, pp. 745-759.
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In this study we assessed the impacts of forest fragmentation on the Amazon landscape using remote sensing techniques. Landscape disturbance, obtained for an area of approximately 3.5 106 km2 through simple spatial metrics (i.e. number of fragments, mean fragment area and border size) and principal component transformation were then compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) seasonal responses. As expected, higher disturbance values prevail in the southern border of the Amazon, near the intensively converted deforestation arc, and close to the major roads. NDVI seasonal responses more closely follow human-induced patterns, i.e. forest remnants from areas more intensively converted were associated with higher NDVI seasonal values. The significant correlation between NDVI seasonal responses and landscape disturbances were corroborated through analysis of geographically weighted regression (GWR) parameters and predictions. On the other hand, EVI seasonal responses were more complex with significant variations found over intact, less fragmented forest patches, thus restricting its utility to assess landscape disturbance. Although further research is needed, our results suggest that the degree of fragmentation of the forest remnants can be remotely sensed with MODIS vegetation indices. Thus, it may become possible to upscale field-based data on overall canopy condition and fragmentation status for basin-wide extrapolations. 2010 Taylor & Francis.
Jiang, Z. & Huete, A. 2010, 'Linearization of NDVI based on its relationship with vegetation fraction', Photogrammetry Engineering & Remote Sensing, vol. 76, no. 8, pp. 545-561.
The Normalized Difference Vegetation Index (NDVI) is widely used for global monitoring of land surface vegetation dynamics from space. However, it is well documented that the NDVI approaches saturation asymptotically over highly vegetated areas. In this study, a linearized NDVI (LNDVI) is derived by introducing a linearity-adjustment factor, beta, into the NDVI equation to improve the linearity of the relationship with vegetation fraction and mitigate the saturation problem encountered by NDVI. The linearity of the LNDVI is demonstrated using a ground-observed data set and a model-simulated data set. A functional relationship and consistence of LNDVI with other NDVI adaptations are found, providing independent justification of the value of the NDVI adaptations. Due to its improved linearity with vegetation fraction, this index would provide more accurate monitoring of vegetation dynamics and estimation of biophysical parameters. The LNDVI can be derived from historical NDVI datasets directly without knowledge of input reflectances.
Yoshioka, H., Miura, T., Dematt, J.A.M., Batchily, K. & Huete, A.R. 2010, 'Soil line influences on two-band vegetation indices and vegetation isolines: A numerical study', Remote Sensing, vol. 2, no. 2, pp. 545-561.
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Influences of soil line variations on two-band vegetation indices (VIs) and their vegetation isolines in red and near-infrared (NIR) reflectance space are investigated based on recently derived relationships between the relative variations of VIs with variations of the soil line parameters in the accompanying paper by Yoshioka et al. [1]. The soil line influences are first demonstrated numerically in terms of variations of vegetation isolines and VI values along with the isolines. A hypothetical case is then analyzed by assuming the discrepancies between the general and regional soil lines for a Southern Brazil area reported elsewhere. The results indicate the validity of our analytical approach for the evaluation of soil line influences and the applicability for adjustment of VI errors using external data sources of soil reflectance spectra. 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.
Kim, Y., Huete, A.R., Miura, T. & Jiang, Z. 2010, 'Spectral compatibility of vegetation indices across sensors: Band decomposition analysis with Hyperion data', Journal of Applied Remote Sensing, vol. 4, no. 1.
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Vegetation indices (VIs) are widely used in long-term measurement studies of vegetation changes, including seasonal vegetation activity and interannual vegetation-climate interactions. There is much interest in developing cross-sensor/multi-mission vegetation products that can be extended to future sensors while maintaining continuity with present and past sensors. In this study we investigated multi-sensor spectral bandpass dependencies of the enhanced vegetation index (EVI), a 2-band EVI (EVI2), and the normalized difference vegetation index (NDVI) using spectrally convolved Earth Observing-1 (EO-1) Hyperion satellite images acquired over a range of vegetation conditions. Two types of analysis were carried out, including (1) empirical relationships among sensor reflectances and VIs and (2) decomposition of bandpass contributions to observed cross-sensor VI differences. VI differences were a function of cross-sensor bandpass disparities and the integrative manner in which bandpass differences in red, near-infrared (NIR), and blue reflectances combined to influence a VI. Disparities in blue bandpasses were the primary cause of EVI differences between the Moderate Resolution Imaging Spectroradiometer (MODIS) and other course resolution sensors, including the upcoming Visible Infrared Imager/Radiometer Suite (VIIRS). The highest compatibility was between VIIRS and MODIS EVI2 while AVHRR NDVI and EVI2 were the least compatible to MODIS. 2010 Society of Photo-Optical Instrumentation Engineers.
Jiang, Z. & Huete, A.R. 2010, 'Linearization of NDVI based on its relationship with vegetation fraction', Photogrammetric Engineering and Remote Sensing, vol. 76, no. 8, pp. 965-975.
The Normalized Difference Vegetation Index (NDVI) is widely used for global monitoring of land surface vegetation dynamics from space. However, it is well documented that the NDVI approaches saturation asymptotically over highly vegetated areas. In this study, a linearized NDVI (LNDVI) is derived by introducing a linearity-adjustment factor, b, into the NDVI equation to improve the linearity of the relationship with vegetation fraction and mitigate the saturation problem encountered by NDVI. The linearity of the LNDVI is demonstrated using a ground-observed data set and a modelsimulated data set. A functional relationship and consistence of LNDVI with other NDVI adaptations are found, providing independent justification of the value of the NDVI adaptations. Due to its improved linearity with vegetation fraction, this index would provide more accurate monitoring of vegetation dynamics and estimation of biophysical parameters. The LNDVI can be derived from historical NDVI datasets directly without knowledge of input reflectances. 2010 American Society for Photogrammetry and Remote Sensing.
Miura, T. & Huete, A.R. 2009, 'Performance of three reflectance calibration methods for airborne hyperspectral spectrometer data', Sensors, vol. 9, no. 2, pp. 794-813.
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In this study, the performances and accuracies of three methods for converting airborne hyperspectral spectrometer data to reflectance factors were characterized and compared. The "reflectance mode (RM)" method, which calibrates a spectrometer against a white reference panel prior to mounting on an aircraft, resulted in spectral reflectance retrievals that were biased and distorted. The magnitudes of these bias errors and distortions varied significantly, depending on time of day and length of the flight campaign. The "linear-interpolation (LI)" method, which converts airborne spectrometer data by taking a ratio of linearly-interpolated reference values from the preflight and post- flight reference panel readings, resulted in precise, but inaccurate reflectance retrievals. These reflectance spectra were not distorted, but were subject to bias errors of varying magnitudes dependent on the flight duration length. The "continuous panel (CP)" method uses a multi-band radiometer to obtain continuous measurements over a reference panel throughout the flight campaign, in order to adjust the magnitudes of the linear-interpolated reference values from the preflight and post-flight reference panel readings. Airborne hyperspectral reflectance retrievals obtained using this method were found to be the most accurate and reliable reflectance calibration method. The performances of the CP method in retrieving accuratez reflectance factors were consistent throughout time of day and for various flight durations. Based on the dataset analyzed in this study, the uncertainty of the CP method has been estimated to be 0.0025 0.0005 reflectance units for the wavelength regions not affected by atmospheric absorptions. The RM method can produce reasonable results only for a very short-term flight (e.g., < 15 minutes) conducted around a local solar noon. The flight duration should be kept shorter than 30 minutes for the LI method to produce results with reasonable accuracies. An important advantage of the CP method is that the method can be used for long-duration flight campaigns (e.g., 1-2 hours). Although this study focused on reflectance calibration of airborne spectrometer data, the methods evaluated in this study and the results obtained are directly applicable to ground spectrometer measurements. 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.
Fisher, J.B., Malhi, Y., Bonal, D., Da Rocha, H.R., De Arajo, A.C., Gamo, M., Goulden, M.L., Rano, T.H., Huete, A.R., Kondo, H., Kumagai, T., Loescher, H.W., Miller, S., Nobre, A.D., Nouvellon, Y., Oberbauer, S.F., Panuthai, S., Roupsard, O., Saleska, S., Tanaka, K., Tanaka, N., Tu, K.P. & Von Randow, C. 2009, 'The land-atmosphere water flux in the tropics', Global Change Biology, vol. 15, no. 11, pp. 2694-2714.
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Tropical vegetation is a major source of global land surface evapotranspiration, and can thus play a major role in global hydrological cycles and global atmospheric circulation. Accurate prediction of tropical evapotranspiration is critical to our understanding of these processes under changing climate. We examined the controls on evapotranspiration in tropical vegetation at 21 pan-tropical eddy covariance sites, conducted a comprehensive and systematic evaluation of 13 evapotranspiration models at these sites, and assessed the ability to scale up model estimates of evapotranspiration for the test region of Amazonia. Net radiation was the strongest determinant of evapotranspiration (mean evaporative fraction was 0.72) and explained 87% of the variance in monthly evapotranspiration across the sites. Vapor pressure deficit was the strongest residual predictor (14%), followed by normalized difference vegetation index (9%), precipitation (6%) and wind speed (4%). The radiation-based evapotranspiration models performed best overall for three reasons: (1) the vegetation was largely decoupled from atmospheric turbulent transfer (calculated from ? decoupling factor), especially at the wetter sites; (2) the resistance-based models were hindered by difficulty in consistently characterizing canopy (and stomatal) resistance in the highly diverse vegetation; (3) the temperature-based models inadequately captured the variability in tropical evapotranspiration. We evaluated the potential to predict regional evapotranspiration for one test region: Amazonia. We estimated an Amazonia-wide evapotranspiration of 1370mmyr-1, but this value is dependent on assumptions about energy balance closure for the tropical eddy covariance sites; a lower value (1096mmyr-1) is considered in discussion on the use of flux data to validate and interpolate models. 2009 The Authors Journal compilation 2009 Blackwell Publishing Ltd.
Dematte, J.A., Huete, A., Ferreira, L.G., Nanni, M.R. & Fiorio, P.R. 2009, 'Methodology for bare soil detection and discrimination by Landsat TM image', The Open Remote Sensing Journal, vol. 2, no. 1, pp. 24-35.
The objective of this work was to develop and test a remote sensing technique to determine bare soils with pixel information from satellite images. The methodology was tested and improved on a 2,805 km2 area located in the state of So Paulo, Brazil. The pixel data from a Landsat-5/TM image was transformed into reflectances. 294 pixels were evaluated by five factors simultaneously and included the following: color composition image; vegetation index; soil brightness information (soil line concept), and a comparison between spectral curve of the pixel with spectral patterns of soils. A validation procedure was based on the discriminate analysis for the real soil related with each pixel. For this, a soil map was overlaid onto the image, and the pixels were related to its respective soil class. Soil brightness variations were readily observed in the spectral curves and in red-NIR features and corresponded to differences in texture and particle size as well in iron and organic matter content. Although qualitative, the observation of color composition was useful for pixel identification. The soil line concept was very useful as it presented a high R2 coefficient (0.90). Comparison between ground level soil spectral curves with satellite information could assist on the evaluation of the real format of the curves. Discriminate analysis indicated a 99.3% correct classification of the soils. Field work validation indicated 90% significance. The present method could help researchers acquire valuable information (i.e., soil attributes quantification), when soil data must be acquired from satellite images.
Sun, H.-.S., Huang, J.-.F., Huete, A.R., Peng, D.-.L. & Zhang, F. 2009, 'Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in China', Journal of Zhejiang University: Science A, vol. 10, no. 10, pp. 1509-1522.
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The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderateresolution imaging spectroradiometer (MODIS) data in China. Paddy rice fields were extracted by identifying the unique characteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization. The characteristic could be reflected by the enhanced vegetation index (EVI) and the land surface water index (LSWI) derived from MODIS sensor data. Algorithms for single, early, and late rice identification were obtained from selected typical test sites. The algorithms could not only separate early rice and late rice planted in the same fields, but also reduce the uncertainties. The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics, and the spatial matching was examined by ETM+ (enhanced thematic mapper plus) images in a test region. Major factors that might cause errors, such as the coarse spatial resolution and noises in the MODIS data, were discussed. Although not suitable for monitoring the inter-annual variations due to some inevitable factors, the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale, and they might provide reference for further studies. 2009 Zhejiang University and Springer Berlin Heidelberg.
Yoshioka, H., Miura, T., Dematt, J.A.M., Batchily, K. & Huete, A.R. 2009, 'Derivation of soil line influence on two-band vegetation indices and vegetation Isolines', Remote Sensing, vol. 1, no. 4, pp. 842-857.
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This paper introduces derivations of soil line influences on two-band vegetation indices (VIs) and vegetation isolines in the red and near infrared reflectance space. Soil line variations are described as changes in the soil line parameters (slope and offset) and the red reflectance of the soil surface. A general form of a VI model equation written as a ratio of two linear functions (e.g., NDVI and SAVI) was assumed. It was found that relative VI variations can be approximated by a linear combination of the three soil parameters. The derived expressions imply the possibility of estimating and correcting for soil-induced bias errors in VIs and their derived biophysical parameters, caused by the assumption of a general soil line, through the use of external data sources such as regional soil maps. 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.
Potter, C., Klooster, S., Huete, A., Genovese, V., Bustamante, M., Guimaraes Ferreira, L., De Oliveira Jr, R.C. & Zepp, R. 2009, 'Terrestrial carbon sinks in the brazilian amazon and cerrado region predicted from MODIS satellite data and ecosystem modeling', Biogeosciences, vol. 6, no. 6, pp. 937-945.
A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of Brazilian Amazon and <i>Cerrado</i> regions over the period 2000-2004. Net ecosystem production (NEP) flux for atmospheric CO2 in the region for these years was estimated. Consistently high carbon sink fluxes in terrestrial ecosystems on a yearly basis were found in the western portions of the states of Acre and Rondnia and the northern portions of the state of Par. These areas were not significantly impacted by the 2002-2003 El Nio event in terms of net annual carbon gains. Areas of the region that show periodically high carbon source fluxes from terrestrial ecosystems to the atmosphere on yearly basis were found throughout the state of Maranho and the southern portions of the state of Amazonas. As demonstrated though tower site comparisons, NEP modeled with monthly MODIS Enhanced Vegetation Index (EVI) inputs closely resembles the measured seasonal carbon fluxes at the LBA Tapajos tower site. Modeling results suggest that the capacity for use of MODIS Enhanced Vegetation Index (EVI) data to predict seasonal uptake rates of CO2 in Amazon forests and Cerrado woodlands is strong.
Jiang, Z., Huete, A.R., Didan, K. & Miura, T. 2008, 'Development of a two-band enhanced vegetation index without a blue band', Remote Sensing of Environment, vol. 112, no. 10, pp. 3833-3845.
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The enhanced vegetation index (EVI) was developed as a standard satellite vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a blue band, in addition to the red and near-infrared bands, making it difficult to generate long-term EVI time series as the normalized difference vegetation index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-band EVI (EVI2), without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor ? is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted vegetation index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, ? and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a blue band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied. 2008 Elsevier Inc.
Huete, A.R., Restrepo-Coupe, N., Ratana, P., Didan, K., Saleska, S.R., Ichii, K., Panuthai, S. & Gamo, M. 2008, 'Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in Monsoon Asia', Agricultural and Forest Meteorology, vol. 148, no. 5, pp. 748-760.
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The spatial and temporal dynamics of tropical forest functioning are poorly understood, partly attributed to a weak seasonality and high tree species diversity at the landscape scale. Recent neotropical rainforest studies with local tower flux measurements have revealed strong seasonal carbon fluxes that follow the availability of sunlight in intact forests, while in areas of forest disturbance, carbon fluxes more closely tracked seasonal water availability. These studies also showed a strong seasonal correspondence of satellite measures of greenness, using the Enhanced Vegetation Index (EVI) with ecosystem carbon fluxes in both intact and disturbed forests, which may enable larger scale extension of tower flux measurements. In this study, we investigated the seasonal patterns and relationships of local site tower flux measures of gross primary productivity (Pg) with independent Moderate Resolution Imaging Spectroradiometer (MODIS) satellite greenness measures across three Monsoon Asia tropical forest types, encompassing drought-deciduous, dry evergreen, and humid evergreen secondary tropical forests. In contrast to neotropical forests, the tropical forests of Monsoon Asia are more extensively degraded and heterogeneous due to intense land use pressures, and therefore, may exhibit unique seasonal patterns of ecosystem fluxes that are more likely water-limited and drought-susceptible. Our results show significant phenologic variability and response to moisture and light controls across the three tropical forest sites and at the regional scale. The drier tropical forests were primarily water-limited, while the wet evergreen secondary forest showed a slight positive trend with light availability. Satellite EVI greenness observations were generally synchronized and linearly related with seasonal and inter-annual tower flux Pg measurements at the multiple sites and provided better opportunities for tower extension of carbon fluxes than other satellite products, such as the MODIS Pg product. Satellite EVI-derived Pg images revealed strong seasonal variations in photosynthetic activity throughout the Monsoon Asia tropical region. 2008 Elsevier B.V. All rights reserved.
Glenn, E.P., Huete, A.R., Nagler, P.L. & Nelson, S.G. 2008, 'Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape', Sensors, vol. 8, no. 4, pp. 2136-2160.
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Vegetation indices (VIs) are among the oldest tools in remote sensing studies. Although many variations exist, most of them ratio the reflection of light in the red and NIR sections of the spectrum to separate the landscape into water, soil, and vegetation. Theoretical analyses and field studies have shown that VIs are near-linearly related to photosynthetically active radiation absorbed by a plant canopy, and therefore to lightdependent physiological processes, such as photosynthesis, occurring in the upper canopy. Practical studies have used time-series VIs to measure primary production and vapotranspiration, but these are limited in accuracy to that of the data used in ground truthing or calibrating the models used. VIs are also used to estimate a wide variety of other canopy attributes that are used in Soil-Vegetation-Atmosphere Transfer (SVAT), Surface Energy Balance (SEB), and Global Climate Models (GCM). These attributes include fractional vegetation cover, leaf area index, roughness lengths for turbulent transfer, emissivity and albedo. However, VIs often exhibit only moderate, non-linear relationships to these canopy attributes, compromising the accuracy of the models. We use case studies to illustrate the use and misuse of VIs, and argue for using VIs most simply as a measurement of canopy light absorption rather than as a surrogate for detailed features of canopy architecture. Used this way, VIs are compatible with "Big Leaf SVAT and GCMs that assume that canopy carbon and moisture fluxes have the same relative response to the environment as any single leaf, simplifying the task of modeling complex landscapes. 2008 by MDPI.
Paz-Pellat, F., Bolaos-Gonzlez, M., Palacios-Vlez, E., Palacios-Snchez, L.A., Martnez-Menes, M. & Huete, A. 2008, 'Optimization of the spectral vegetation index NDVIcp', Agrociencia, vol. 42, no. 8, pp. 925-937.
The optimization of the spectral vegetation indices to reduce the effects of soil and atmosphere, requires strong hypotheses when there is no knowledge related to the atmospheric variables and only information contained in the satellite images. The optimization of the index NDVIcp, designed to minimize the effect of soil, is analyzed under a perspective of its viability and of the restrictions associated with this objective. To explain the problematic of the optimization of the spectral vegetation index NDVIcp, an analysis was made of the mathematical structure of the atmospheric effects, and the NDVIcp was formulated as a function of this structure to analyze the problem of minimization of the joint effects of soil and of the atmosphere. The analyses showed that without the knowledge of the type of aerosol (optical thickness of the atmosphere) and atmospheric model, the attempts at reduction or elimination of the atmospheric effects are indetermined (imply multiple solutions to the problem). If the atmosphere and aerosol are known, but not the atmospheric optical thickness, it is possible to minimize the atmospheric effects and of the soil using a scheme of approximation of the constants of the NDVIcp index to the case without atmosphere.
Ferreira, N.C., Ferreira, L.G., Huete, A.R. & Ferreira, M.E. 2007, 'An operational deforestation mapping system using MODIS data and spatial context analysis', International Journal of Remote Sensing, vol. 28, no. 1, pp. 47-62.
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The Brazilian Amazon has the world's highest absolute rate of forest loss, currently averaging nearly 2 million hectares per year. In this study, we present a near-real-time automated deforestation mapping system for the Brazilian Amazon based on the analysis of spatial context information and MODIS Vegetation Index products and implemented on an ArcGIS 9.0 platform. This system, already validated and operational, was developed as part of the "Integrated Warning Deforestation System for the Amazon" (SIAD), an initiative of the Brazilian government within the scope of the Amazon Protection System (SIPAM), which also comprises: (1) a spatial information module, aimed at the assessment of the causes and impacts of the deforested areas; (2) a prediction module, indicative of deforestation trends; and (3) a data and information gateway based on map server technology.
Ichii, K., Hashimoto, H., White, M.A., Potter, C., Hutyra, L.R., Huete, A.R., Myneni, R.B. & Nemani, R.R. 2007, 'Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality', Global Change Biology, vol. 13, no. 1, pp. 67-77.
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Accurate parameterization of rooting depth is difficult but important for capturing the spatio-temporal dynamics of carbon, water and energy cycles in tropical forests. In this study, we adopted a new approach to constrain rooting depth in terrestrial ecosystem models over the Amazon using satellite data [moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI)] and Biome-BGC terrestrial ecosystem model. We simulated seasonal variations in gross primary production (GPP) using different rooting depths (1, 3, 5, and 10m) at point and spatial scales to investigate how rooting depth affects modeled seasonal GPP variations and to determine which rooting depth simulates GPP consistent with satellite-based observations. First, we confirmed that rooting depth strongly controls modeled GPP seasonal variations and that only deep rooting systems can successfully track flux-based GPP seasonality at the Tapajos km67 flux site. Second, spatial analysis showed that the model can reproduce the seasonal variations in satellite-based EVI seasonality, however, with required rooting depths strongly dependent on precipitation and the dry season length. For example, a shallow rooting depth (1-3m) is sufficient in regions with a short dry season (e.g. 0-2 months), and deeper roots are required in regions with a longer dry season (e.g. 3-5 and 5-10m for the regions with 3-4 and 5-6 months dry season, respectively). Our analysis suggests that setting of proper rooting depths is important to simulating GPP seasonality in tropical forests, and the use of satellite data can help to constrain the spatial variability of rooting depth. 2006 Blackwell Publishing Ltd.
Glenn, E.P., Huete, A.R., Nagler, P.L., Hirschboeck, K.K. & Brown, P. 2007, 'Integrating remote sensing and ground methods to estimate evapotranspiration', Critical Reviews in Plant Sciences, vol. 26, no. 3, pp. 139-168.
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Evapotranspiraton (ET) is the second largest term in the terrestrial water budget after precipitation, and ET is expected to increase with global warming. ET studies are relevant to the plant sciences because over 80% of terrestrial ET is due to transpiration by plants. Remote sensing is the only feasible means for projecting ET over large landscape units. In the past decade or so, new ground and remote sensing tools have dramatically increased our ability to measure ET at the plot scale and to scale it over larger regions. Moisture flux towers and micrometeorological stations have been deployed in numerous natural and agricultural biomes and provide continuous measurements of actual ET or potential ET with an accuracy or uncertainty of 10-30%. These measurements can be scaled to larger landscape units using remotely-sensed vegetation indices (VIs), Land Surface Temperature (LST), and other satellite data. Two types of methods have been developed. Empirical methods use time-series VIs and micrometeorological data to project ET measured on the ground to larger landscape units. Physically-based methods use remote sensing data to determine the components of the surface energy balance, including latent heat flux, which determines ET. Errors in predicting ET by both types of methods are within the error bounds of the flux towers by which they are calibrated or validated. However, the error bounds need to be reduced to 10% or less for applications that require precise wide-area ET estimates. The high fidelity between ET and VIs over agricultural fields and natural ecosystems where precise ground estimates of ET are available suggests that this might be an achievable goal if ground methods for measuring ET continue to improve. Copyright Taylor & Francis Group, LLC.
Yang, F., Ichii, K., White, M.A., Hashimoto, H., Michaelis, A.R., Votava, P., Zhu, A.-.X., Huete, A., Running, S.W. & Nemani, R.R. 2007, 'Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach', Remote Sensing of Environment, vol. 110, no. 1, pp. 109-122.
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Remote sensing is a potentially powerful technology with which to extrapolate eddy covariance-based gross primary production (GPP) to continental scales. In support of this concept, we used meteorological and flux data from the AmeriFlux network and Support Vector Machine (SVM), an inductive machine learning technique, to develop and apply a predictive GPP model for the conterminous U.S. In the following four-step process, we first trained the SVM to predict flux-based GPP from 33 AmeriFlux sites between 2000 and 2003 using three remotely-sensed variables (land surface temperature, enhanced vegetation index (EVI), and land cover) and one ground-measured variable (incident shortwave radiation). Second, we evaluated model performance by predicting GPP for 24 available AmeriFlux sites in 2004. In this independent evaluation, the SVM predicted GPP with a root mean squared error (RMSE) of 1.87gC/m 2/day and an R 2 of 0.71. Based on annual total GPP at 15 AmeriFlux sites for which the number of 8-day averages in 2004 was no less than 67% (30 out of a possible 45), annual SVM GPP prediction error was 32.1% for non-forest ecosystems and 22.2% for forest ecosystems, while the standard Moderate Resolution Imaging Spectroradiometer GPP product (MOD17) had an error of 50.3% for non-forest ecosystems and 21.5% for forest ecosystems, suggesting that the regionally tuned SVM performed better than the standard global MOD17 GPP for non-forest ecosystems but had similar performance for forest ecosystems. The most important explanatory factor for GPP prediction was EVI, removal of which increased GPP RMSE by 0.85gC/m 2/day in a cross-validation experiment. Third, using the SVM driven by remote sensing data including incident shortwave radiation, we predicted 2004 conterminous U.S. GPP and found that results were consistent with expected spatial and temporal patterns. Finally, as an illustration of SVM GPP for ecological applications, we estimated maximum light use efficiency (e max), one of the most important factors for standard light use efficiency models, for the conterminous U.S. by integrating the 2004 SVM GPP with the MOD17 GPP algorithm. We found that e max varied from ? 0.86gC/MJ in grasslands to ? 1.56gC/MJ in deciduous forests, while MOD17 e max was 0.68gC/MJ for grasslands and 1.16gC/MJ for deciduous forests, suggesting that refinements of MOD17 e max may be beneficial. 2007 Elsevier Inc. All rights reserved.
Nagler, P.L., Glenn, E.P., Kim, H., Emmerich, W., Scott, R.L., Huxman, T.E. & Huete, A.R. 2007, 'Relationship between evapotranspiration and precipitation pulses in a semiarid rangeland estimated by moisture flux towers and MODIS vegetation indices', Journal of Arid Environments, vol. 70, no. 3, pp. 443-462.
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We used moisture Bowen ratio flux tower data and the enhanced vegetation index (EVI) from the moderate resolution imaging spectrometer (MODIS) on the Terra satellite to measure and scale evapotranspiration (ET) over sparsely vegetated grassland and shrubland sites in a semiarid watershed in southeastern Arizona from 2000 to 2004. The grassland tower site had higher mean annual ET (336 mm yr-1) than the shrubland tower site (266 mm yr-1) (P<0.001). ET measured at the individual tower sites was strongly correlated with EVI (r=0.80-0.94). ET was moderately correlated with precipitation (P), and only weakly correlated with net radiation or air temperature. The strong correlation between ET and EVI, as opposed to the moderate correlation with rainfall, suggests that transpiration (T) is the dominant process controlling ET at these sites. ET could be adequately predicted from EVI and P across seasons and tower sites (r2 = 0.74) by a single multiple regression equation. The regression equation relating ET to EVI and P was used to scale ET over 25 km2 areas of grassland and shrubland around each tower site. Over the study, ratios of T to ET ranged from 0.75 to 1.0. Winter rains stimulated spring ET, and a large rain event in fall, 2000, stimulated ET above T through the following year, indicating that winter rain stored in the soil profile can be an important component of the plants' water budget during the warm season in this ecosystem. We conclude that remotely sensed vegetation indices can be used to scale ground measurements of ET over larger landscape units in semiarid ranglelands, and that the vegetation communities in this landscape effectively harvest the available precipitation over a period of years, even though precipitation patterns are variably seasonally and interannually. 2007 Elsevier Ltd. All rights reserved.
Potter, C., Klooster, S., Huete, A. & Genovese, V. 2007, 'Terrestrial carbon sinks for the United States Predicted from MODIS satellite data and ecosystem modeling', Earth Interactions, vol. 11, no. 13.
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A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of the conterminous United States over the period 2001-04. Predicted net ecosystem production (NEP) flux for atmospheric CO2 in the United States was estimated as annual net sink of about +0.2 Pg C in 2004. Regional climate patterns were reflected in the predicted annual NEP flux from the model, which showed extensive carbon sinks in ecosystems of the southern and eastern regions in 2003-04, and major carbon source fluxes from ecosystems in the Rocky Mountain and Pacific Northwest regions in 2003-04. As demonstrated through tower site comparisons, net primary production (NPP) modeled with monthly MODIS enhanced vegetation index (EVI) inputs closely resembles both the measured high- and low-season carbon fluxes. Modeling results suggest that the capacity of the NASA Carnegie Ames Stanford Approach (CASA) model to use 8-km resolution MODIS EVI data to predict peak growing season uptake rates of CO2 in irrigated croplands and moist temperate forests is strong.
Jiang, Z., Huete, A., Liu, J. & Qi, J. 2007, 'Interpretation of the modified soil-adjusted vegetation index isolines in red-NIR reflectance space', Journal of applied remote sensing, vol. 1, no. 013503, pp. 1-12.
In red-NIR reflectance space, the Modified Soil Adjusted Vegetation Index (MSAVI) isolines, representing similar vegetation biophysical quantities, are neither convergent to a point nor parallel to each other. Consequently, the treatment of the MSAVI isolines is distinctly different from those of other vegetation index isolines, such as the normalized difference vegetation index (NDVI), the perpendicular vegetation index (PVI), and the soil-adjusted vegetation index (SAVI). In this study, the MSAVI isolines are shown to be the tangent lines of the parabola, (NIR-0.5)2+2Red=0, and the values of the MSAVI isolines are equal to the ordinates of their tangent points plus 0.5. These findings provide a graphic interpretation of the MSAVI and are useful in understanding the biophysical characteristics of the MSAVI. The MSAVI isolines are shown to better approximate field data and simulated vegetation biophysical isolines than the other 2-band vegetation index isolines. As the treatment of the MSAVI isolines can be depicted by the parabola curve, the MSAVI can be referred to as a parabola-based vegetation index.
Saleska, S.R., Didan, K., Huete, A.R. & Da Rocha, H.R. 2007, 'Amazon forests green-up during 2005 drought', Science, vol. 318, no. 5850, pp. 612-612.
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Myneni, R.B., Yang, W., Nemani, R.R., Huete, A.R., Dickinson, R.E., Knyazikhin, Y., Didan, K., Fu, R., Negrn Jurez, R.I., Saatchi, S.S., Hashimoto, H., Ichii, K., Shabanov, N.V., Tan, B., Ratana, P., Privette, J.L., Morisette, J.T., Vermote, E.F., Roy, D.P., Wolfe, R.E., Friedl, M.A., Running, S.W., Votava, P., El-Saleous, N., Devadiga, S., Su, Y. & Salomonson, V.V. 2007, 'Large seasonal swings in leaf area of Amazon rainforests', Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 12, pp. 4820-4823.
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Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation-atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of ?25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests. 2007 by The National Academy of Sciences of the USA.
Paz-Pellat, F., Palacios-Vlez, E., Bolaos-Gonzlez, M., Palacios-Snchez, L.A., Martnez-Menes, M., Meja-Saenz, E. & Huete, A. 2007, 'Design of a vegetation spectral index: NDVIcp', Agrociencia, vol. 41, no. 5, pp. 539-554.
There are many vegetation indices (VI) based on relationships of the spectral space of the red and near infrared. In this study, the structure of the most widely used VI is examined, using a formulation to characterize curves of equal leaf area index. In order to solve the inconsistencies found in the VI, a new one (NDVIcp) is proposed, based on the correct structure of the problem, under empirical considerations. The NDVIcp is validated using data from field experiments with maize (Zea mays L.) and cotton (Gossipyum spp.).
Bolaos-Gonzlez, M.A., Paz-Pellat, F., Palacios-Vlez, E., Meja-Senz, E. & Huete, A. 2007, 'Modelation of the sun-sensor geometry effects in the vegetation reflectance', Agrociencia, vol. 41, no. 5, pp. 527-537.
Surface reflectance is not only a function of the spectral properties of incident radiation and surface anisotropy, but also of the direction from which the surface is illuminated and seen; that is, it depends on the illumination-vision geometry (sun-sensor in the specific cases of satellite images) with which it is acquired. The dependence of surface reflectance on sun-sensor geometry is described by the Bidirectional Reflectance Distribution Function (BRDF). For that reason, it is necessary to minimize these effects as the first step in calculating vegetation indexes or any other estimation based on surface reflectance that intends to link bio-physical characteristics of crops or natural vegetation. In this study, a model that estimates reflectance at nadir of the red (R) and near infrared (IRC) bands with a single datum of reflectance obtained with any combination of sun-sensor geometry is developed and evaluated. The proposed model was validated with experimental data taken in a natural grassland, with which adequate results (R2=0.967) were obtained.
Jiang, Z., Huete, A.R., Li, J. & Qi, J. 2007, 'Interpretation of the modified soil-adjusted vegetation index isolines in red-NIR reflectance space', Journal of Applied Remote Sensing, vol. 1, no. 1.
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In red-NIR reflectance space, the Modified Soil Adjusted Vegetation Index (MSAVI) isolines, representing similar vegetation biophysical quantities, are neither convergent to a point nor parallel to each other. Consequently, the treatment of the MSAVI isolines is distinctly different from those of other vegetation index isolines, such as the normalized difference vegetation index (NDVI), the perpendicular vegetation index (PVI), and the soil-adjusted vegetation index (SAVI). In this study, the MSAVI isolines are shown to be the tangent lines of the parabola, (NIR-0.5)2+2Red=0, and the values of the MSAVI isolines are equal to the ordinates of their tangent points plus 0.5. These findings provide a graphic interpretation of the MSAVI and are useful in understanding the biophysical characteristics of the MSAVI. The MSAVI isolines are shown to better approximate field data and simulated vegetation biophysical isolines than the other 2-band vegetation index isolines. As the treatment of the MSAVI isolines can be depicted by the parabola curve, the MSAVI can be referred to as a parabola-based vegetation index. 2007 Society of Photo-Optical Instrumentation Engineers.
Barbosa, H.A., Huete, A.R. & Baethgen, W.E. 2006, 'A 20-year study of NDVI variability over the Northeast Region of Brazil', Journal of Arid Environments, vol. 67, no. 2, pp. 288-307.
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The natural ecosystems of the Northeast Region of Brazil (NEB) have experienced persistent drought episodes and environmental degradation during the past two decades. In this study, we examined the spatial heterogeneity and temporal dynamics of the NEB using a 20-year time series of Normalized Difference Vegetation Index (NDVI) observations, derived from the National Oceanographic and Atmospheric Administration (NOAA)-Advanced Very High Resolution Radiometer (AVHRR) instrument. A set of 12 000 spatially distributed NDVI values was analysed to investigate significant deviations from the mean-monthly values of the base period (1982-2001) in the study area. Various statistical analyses involving minimum, mean and maximum values, coefficient of variation (CV), standardized anomalies (Z-scores), and 36-month running mean were applied to monthly NDVI values to identify spatial and temporal variations in vegetation dynamics. We found strong seasonal oscillations in the vegetation-growing season (February-May) over the NEB study area, with maximum NDVI observed in April-May and seasonal variations, expressed by the CV, ranging from 14% to 32%. In addition, a consistent upward trend in vegetation greenness occurred over the period 1984-1990, and was strongly reversed in the subsequent period 1991-1998. These upward and downward trends in vegetation greenness followed an inter-annual oscillation of ?7-8 years. We also found that dry season peak (September) latitudinal variations in NDVI were 20-25% greater in 1991-1999 than 1982-1990 across the study region. The results of this study suggest that patterns in NEB vegetation variability were a result of the impact of enhanced aridity occurring over the last decade of the 20th century. 2006 Elsevier Ltd. All rights reserved.
Jiang, Z., Huete, A.R., Li, J. & Chen, Y. 2006, 'An analysis of angle-based with ratio-based vegetation indices', IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 9, pp. 2506-2513.
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Remotely sensed, angle-based vegetation indices that measure vegetation amounts by the angle between an approximated soil line and a simulated vegetation isoline in the rednear-infrared reflectance space were developed and evaluated in this paper. nsalan and Boyer previously proposed an angle-based vegetation index, 0 (denoted as 0NDVI in this paper), based on the normalized difference vegetation index (NDVI) with the objective of overcoming the saturation problem in the NDVI. However, ?NDVI did not consider strong soil background influences present in the NDVI. To reduce soil background noise, an angle-based vegetation index, ?SAVI, based on the soil-adjusted vegetation index (SAVI), was derived using trigonometric analysis. The performance of ?NDVI and ?SAVI was evaluated and compared with their corresponding vegetation indices, NDVI and SAVI. The soil background influence on ?NDVI was found to be as significant as that on the NDVI. ?NDVI was found to be more sensitive to vegetation amount than the NDVI at low vegetation density levels, but less sensitive to vegetation fraction at high vegetation density levels. Thus, the saturation effect at high vegetation density levels encountered in the NDVI was not mitigated by ?NDVI. By contrast, ?SAVI exhibited insignificant soil background effects and weaker saturation, as in SAVI, but also improved upon the dynamic range of SAVI. Analyses and evaluation suggest that ?SAVI is an optimal vegetation index to assess and monitor vegetation cover across the entire range of vegetation fraction density levels and over a wide variety of soil backgrounds. 2006 IEEE.
Miura, T., Huete, A. & Yoshioka, H. 2006, 'An empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data', Remote Sensing of Environment, vol. 100, no. 2, pp. 223-236.
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Long term observations of global vegetation from multiple satellites require much effort to ensure continuity and compatibility due to differences in sensor characteristics and product generation algorithms. In this study, we focused on the bandpass filter differences and empirically investigated cross-sensor relationships of the normalized difference vegetation index (NDVI) and reflectance. The specific objectives were: 1) to understand the systematic trends in cross-sensor relationships of the NDVI and reflectance as a function of spectral bandpasses, 2) to examine/identify the relative importance of the spectral features (i.e., the green peak, red edge, and leaf liquid water absorption regions) in and the mechanism(s) of causing the observed systematic trends, and 3) to evaluate the performance of several empirical cross-calibration methods in modeling the observed systematic trends. A Level 1A Hyperion hyperspectral image acquired over a tropical forest-savanna transitional region in Brazil was processed to simulate atmospherically corrected reflectances and NDVI for various bandpasses, including Terra Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA-14 Advanced Very High Resolution Radiometer (AVHRR), and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). Data were extracted from various land cover types typically found in tropical forest and savanna biomes and used for analyses. Both NDVI and reflectance relationships among the sensors were neither linear nor unique and were found to exhibit complex patterns and bandpass dependencies. The reflectance relationships showed strong land cover dependencies. The NDVI relationships, in contrast, did not show land cover dependencies, but resulted in nonlinear forms. From sensitivity analyses, the green peak (?550 nm) and red-NIR transitional (680-780 nm) features were identified as the key factors in producing the observed land cover dependencies and nonlinearity in cross-sensor relationships. In particular, differences in the extents to which the red and/or NIR bandpasses included these features significantly influenced the forms and degrees of nonlinearity in the relationships. Translation of MODIS NDVI to "AVHRR-like" NDVI using a weighted average of MODIS green and red bands performed very poorly, resulting in no reduction of overall discrepancy between MODIS and AVHRR NDVI. Cross-calibration of NDVI and reflectance using NDVI-based quadratic functions performed well, reducing their differences to .025 units for the NDVI and .01 units for the reflectances; however, many of the translation results suffered from bias errors. The present results suggest that distinct translation equations and coefficients need to be developed for every sensor pairs and that land cover-dependency need to be explicitly accounted for to reduce bias errors. 2005 Elsevier Inc. All rights reserved.
Franklin, K.A., Lyons, K., Nagler, P.L., Lampkin, D., Glenn, E.P., Molina-Freaner, F., Markow, T. & Huete, A.R. 2006, 'Buffelgrass (Pennisetum ciliare) land conversion and productivity in the plains of Sonora, Mexico', Biological Conservation, vol. 127, no. 1, pp. 62-71.
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Bufflelgrass (Pennisetum ciliare syn. Cenchrus ciliaris) is an African grass that has been widely introduced in subtropical arid regions of the world to improve rangelands for cattle production. However, it can have a negative effect on the diversity of native plant communities. Buffelgrass was introduced to Sonora, Mexico in the 1970s as a means to bolster the cattle industry. "Desmonte," the process by which native desert vegetation is removed in preparation for buffelgrass seeding, alters the land surface such that buffelgrass plots are easily detectable from aerial and Landsat satellite images. We estimated the extent of conversion to buffelgrass in a 1,850,000 ha area centered on Hermosillo, from MSS and TM images from 1973, 1983, 1990 and 2000. We then compared the relative above-ground productivity of buffelgrass to native vegetation using Normalized Difference Vegetation Index values (NDVI) from Landsat and Moderate Resolution Imaging Spectrometer (MODIS) satellite sensor systems. Buffelgrass pastures have increased from just 7700 ha in 1973 to over 140,000 ha in 2000. Buffelgrass pastures now cover 8% of the land surface in the study area. Buffelgrass pastures have lower net primary productivity, estimated by MODIS NDVI values, than unconverted desert land. The desmonte process removes trees and shrubs, while the buffelgrass plantings are often sparse, leading to an apparent net loss in net primary production from land conversion. We recommend that the desmonte process be discontinued until its efficacy and safety for native ecosystems can be established, and that a comprehensive plan for preserving biodiversity while accomodating economic development be established for this region of the Sonoran Desert in Mexico. 2005 Elsevier Ltd. All rights reserved.
Cheng, Y., Gamon, J.A., Fuentes, D.A., Mao, Z., Sims, D.A., Qiu, H.-.L., Claudio, H., Huete, A. & Rahman, A.F. 2006, 'A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: A comparison of field, AVIRIS and MODIS data', Remote Sensing of Environment, vol. 103, no. 3, pp. 369-378.
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Using field data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) imagery, and Moderate-Resolution Imaging Spectroradiometer (MODIS) data, a multi-scale analysis of ecosystem optical properties was performed for Sky Oaks, a Southern California chaparral ecosystem in the spectral network (SpecNet) and FLUXNET networks. The study covered a 4-year period (2000-2004), which included a severe drought in 2002 and a subsequent wildfire in July 2003, leading to extreme perturbation in ecosystem productivity and optical properties. Two vegetation greenness indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)), and a measure of the fraction of photosynthetically active radiation absorbed by vegetation (fPAR), were compared across sampling platforms, which ranged in pixel size from 1 m (tram system in the field) to 1000 m (MODIS satellite sensor). The three MODIS products closely followed the same seasonal trends as the tram and AVIRIS data, but tended to be higher than the tram and AVIRIS values, particularly for fPAR and NDVI. Following a wildfire that removed all green vegetation, the overestimation in MODIS fPAR values was particularly clear. The MODIS fPAR algorithm (version 4 vs. v.4.1) had a significant effect on the degree of overestimation, with v. 4.1 improving the agreement with the other sensors (AVIRIS and tram) for vegetated conditions, but not for low, post-fire values. The differences between MODIS products and the products from the other platform sensors could not be entirely attributed to differences in sensor spectral responses or sampling scale. These results are consistent with several other recently published studies that indicate that MODIS overestimates fPAR and thus net primary production (NPP) for many terrestrial ecosystems, and demonstrates the need for proper validation of MODIS terrestrial biospheric products by direct comparison against optical signals at other spatial scales, as is now possible at several SpecNet sites. The study also demonstrates the utility of in-situ field sampling (e.g. tram systems) and hyperspectral aircraft imagery for proper interpretation of satellite data taken at coarse spatial scales. 2006 Elsevier Inc. All rights reserved.
Jiang, Z., Huete, A.R., Chen, J., Chen, Y., Li, J., Yan, G. & Zhang, X. 2006, 'Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction', Remote Sensing of Environment, vol. 101, no. 3, pp. 366-378.
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The normalized difference vegetation index (NDVI) is the most widely used vegetation index for retrieval of vegetation canopy biophysical properties. Several studies have investigated the spatial scale dependencies of NDVI and the relationship between NDVI and fractional vegetation cover, but without any consensus on the two issues. The objectives of this paper are to analyze the spatial scale dependencies of NDVI and to analyze the relationship between NDVI and fractional vegetation cover at different resolutions based on linear spectral mixing models. Our results show strong spatial scale dependencies of NDVI over heterogeneous surfaces, indicating that NDVI values at different resolutions may not be comparable. The nonlinearity of NDVI over partially vegetated surfaces becomes prominent with darker soil backgrounds and with presence of shadow. Thus, the NDVI may not be suitable to infer vegetation fraction because of its nonlinearity and scale effects. We found that the scaled difference vegetation index (SDVI), a scale-invariant index based on linear spectral mixing of red and near-infrared reflectances, is a more suitable and robust approach for retrieval of vegetation fraction with remote sensing data, particularly over heterogeneous surfaces. The proposed method was validated with experimental field data, but further validation at the satellite level would be needed. 2006 Elsevier Inc. All rights reserved.
Ferreira, M.E., Ferreira, L.G., Huete, A. & Peccinini, A.A. 2006, 'Analise Comparativa Dos Produtos Modis Ecologia Para O Monitoramento Biofisico Ambiental Do Bioma Cerrado', Brazillian Journal of Geophysics, vol. 24, no. 2, pp. 251-260.
The Brazilian Cerrado is an extensive and complex biome, characterized by rapid and abrupt land cover changes. Due to its dimensions and physiognomic variations, the Cerrado plays an important role regarding the water, energy, and carbon fluxes at both the regional and global scales. Therefore, the correct understanding of the structure and ecological functioning of this biome, particularly in the temporal domain, is of great importance. With this respect, in this study we compared the seasonal response and land cover discrimination of the major MODIS (MODerate resolution Imaging Spectroradiometer) biophysical indices: the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the leaf area index (LAI), and the fraction of absorbed photosynthetically active radiation (fAPAR). In spite of the fact the four indices showed similar temporal trends, the LAI showed the highest sensitivity to the seasonal variations of the natural and converted landscapes. On the other hand, the NDVI showed the best performance regarding land cover discrimination. Our results suggest a synergistic approach concerning the MODIS biophysical / ecological variables for land cover assessments and environmental monitoring of the Cerrado biome.
Huete, A.R., Didan, K., Shimabukuro, Y.E., Ratana, P., Saleska, S.R., Hutyra, L.R., Yang, W., Nemani, R.R. & Myneni, R. 2006, 'Amazon rainforests green-up with sunlight in dry season', Geophysical Research Letters, vol. 33, no. 6.
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Metabolism and phenology of Amazon rainforests significantly influence global dynamics of climate, carbon and water, but remain poorly understood. We analyzed Amazon vegetation phenology at multiple scales with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite measurements from 2000 to 2005. MODIS Enhanced Vegetation Index (EVI, an index of canopy photosynthetic capacity) increased by 25% with sunlight during the dry season across Amazon forests, opposite to ecosystem model predictions that water limitation should cause dry season declines in forest canopy photosynthesis. In contrast to intact forests, areas converted to pasture showed dry-season declines in EVI-derived photosynthetic capacity, presumably because removal of deep-rooted forest trees reduced access to deep soil water. Local canopy photosynthesis measured from eddy flux towers in both a rainforest and forest conversion site confirm our interpretation of satellite data, and suggest that basin-wide carbon fluxes can be constrained by integrating remote sensing and local flux measurements. Copyright 2006 by the American Geophysical Union.
Ferreira, M.E., Ferreira, L.G., Huete, A.R. & Peccinini, A.A. 2006, 'Comparative analysis of the MODIS Ecology products for the biophysical environmental monitoring of the Cerrado biome', Revista Brasileira de Geofisica, vol. 24, no. 2, pp. 251-260.
The Brazilian Cerrado is an extensive and complex biome, characterized by rapid and abrupt land cover changes. Due to its dimensions and physiognomic variations, the Cerrado plays an important role regarding the water, energy, and carbon fluxes at both the regional and global scales, Therefore, the correct understanding of the structure and ecological functioning of this biome, particularly in the temporal domain, is of great importance, With this respect, in this study we compared the seasonal response and land cover discrimination of the major MODIS (MODerate resolution imaging Spectroradiometer) "biophysical" indices: the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the leaf area index (LAI), and the fraction of absorbed photosynthetically active radiation (fAPAR). In spite of the fact the four indices showed similar temporal trends, the LAI showed the highest sensitivity to the seasonal variations of the natural and converted landscapes, On the other hand, the NDVI showed the best performance regarding land cover discrimination, Our results suggest a synergistic approach concerning the MCDIS biophysical / ecological variables for land cover assessments and environmental monitoring of the Cerrado biome. 2006 Sociedade Brasileira de Geofsica.
Nagler, P.L., Cleverly, J., Glenn, E., Lampkin, D., Huete, A. & Wan, Z. 2005, 'Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data', Remote Sensing of Environment, vol. 94, no. 1, pp. 17-30.
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A vegetation index (VI) model for predicting evapotranspiration (ET) from data from the Moderate Resolution Imaging Spectrometer (MODIS) on the EOS-1 Terra satellite and ground meteorological data was developed for riparian vegetation along the Middle Rio Grande River in New Mexico. Ground ET measurements obtained from eddy covariance towers at four riparian sites were correlated with MODIS VIs, MODIS land surface temperatures (LSTs), and ground micrometeorological data over four years. Sites included two saltcedar (Tamarix ramosissima) and two Rio Grande cottonwood (Populus deltoides ssp. Wislizennii) dominated stands. The Enhanced Vegetation Index (EVI) was more closely correlated (r=0.76) with ET than the Normalized Difference Vegetation Index (NDVI; r=0.68) for ET data combined over sites and species. Air temperature (T a) measured over the canopy from towers was the meteorological variable that was most closely correlated with ET (r=0.82). MODIS LST data at 1- and 5-km resolutions were too coarse to accurately measure the radiant surface temperature within the narrow riparian corridor; hence, energy balance methods for estimating ET using MODIS LSTs were not successful. On the other hand, a multivariate regression equation for predicting ET from EVI and T a had an r 2=0.82 across sites, species, and years. The equation was similar to VI-ET models developed for crop species. The finding that ET predictions did not require species-specific equations is significant, inasmuch as these are mixed vegetation zones that cannot be easily mapped at the species level. 2004 Elsevier Inc. All rights reserved.
Nagler, P.L., Scott, R.L., Westenburg, C., Cleverly, J.R., Glenn, E.P. & Huete, A.R. 2005, 'Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers', Remote Sensing of Environment, vol. 97, no. 3, pp. 337-351.
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We combined remote sensing and in-situ measurements to estimate evapotranspiration (ET) from riparian vegetation over large reaches of western U.S. rivers and ET by individual plant types. ET measured from nine flux towers (eddy covariance and Bowen ratio) established in plant communities dominated by five major plant types on the Middle Rio Grande, Upper San Pedro River, and Lower Colorado River was strongly correlated with Enhanced Vegetation Index (EVI) values from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the NASA Terra satellite. The inclusion of maximum daily air temperatures (Ta) measured at the tower sites further improved this relationship. Sixteen-day composite values of EVI and Ta were combined to predict ET across species and tower sites (r2 = 0.74); the regression equation was used to scale ET for 2000-2004 over large river reaches with T a from meteorological stations. Measured and estimated ET values for these river segments were moderate when compared to historical, and often indirect, estimates and ranged from 851-874 mm yr- 1. ET of individual plant communities ranged more widely. Cottonwood (Populus spp.) and willow (Salix spp.) stands generally had the highest annual ET rates (1100-1300 mm yr- 1), while mesquite (Prosopis velutina) (400-1100 mm yr - 1) and saltcedar (Tamarix ramosissima) (300-1300 mm yr - 1) were intermediate, and giant sacaton (Sporobolus wrightii) (500-800 mm yr- 1) and arrowweed (Pluchea sericea) (300-700 mm yr- 1) were the lowest. ET rates estimated from the flux towers and by remote sensing in this study were much lower than values estimated for riparian water budgets using crop coefficient methods for the Middle Rio Grande and Lower Colorado River. 2005 Elsevier Inc. All rights reserved.
Ratana, P., Huete, A.R. & Ferreira, L. 2005, 'Analysis of cerrado physiognomies and conversion in the MODIS seasonal-temporal domain', Earth Interactions, vol. 9, no. 3.
The "cerrado" biome in central Brazil is rapidly being converted into pasture and agricultural crops with important consequences for local and regional climate change and regional carbon fluxes between the atmosphere and land surface. Satellite remote sensing provides an opportunity to monitor the highly diverse and complex cerrado biome, encompassing grassland, shrubland, woodland and gallery forests, and converted areas. In this study, the potential of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data is analyzed to discriminate among these diverse cerrado physiognomies and converted pastures based on their seasonal dynamics and phenology. Four years (2000-03) of MODIS 16-day composited, 250-m resolution vegetation index (VI) data were extracted over a series of biophysically sampled field study sites representing the major cerrado types. The temporal VI profiles over the cerrado formations exhibited high seasonal contrasts with a pronounced dry season from June to August and a wet growing season from November to March. The converted pasture areas showed the highest seasonal contrasts while the gallery forest formation had the lowest contrast. Seasonal VI variations were negatively correlated with woody canopy crown cover and provided a method to discriminate among converted cerrado areas, gallery forests, and the woody and herbaceous cerrado formations. The grassland and shrub cerrado formations, however, were difficult to separate based on their seasonal VI profiles. Maximum discrimination among the cerrado types occurred during the dry season where a positive linear relationship was found between VI and green cover. The annual integrated VI values showed the gallery forests and cerrado woodland as having the highest, and hence most annual productivity, while the more herbaceous shrub and grassland cerrado types were least productive. The cumulative VI profiles of converted cerrado, pasture areas varied distinctly in shape due to their strong dry season inactivity. Furthermore, the annual integrated VI values of the converted pastures differed significantly between the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) MODIS VI products, resulting in large discrepancies in productivity estimates relative to the native cerrado sites. This study shows that the MODIS seasonal-temporal VI profiles are highly useful in monitoring the cerrado biome and conversion-related activities.
Shabanov, N.V., Huang, D., Yang, W., Tan, B., Knyazikhin, Y., Myneni, R.B., Ahl, D.E., Gower, S.T., Huete, A.R., Arago, L.E.O.C. & Shimabukuro, Y.E. 2005, 'Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests', IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 8, pp. 1855-1865.
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Broadleaf forest is a major type of Earth's land cover with the highest observable vegetation density. Retrievals of biophysical parameters, such as leaf area index (LAI), of broadleaf forests at global scale constitute a major challenge to modern remote sensing techniques in view of low sensitivity (saturation) of surface reflectances to such parameters over dense vegetation. The goal of the performed research is to demonstrate physical principles of LAI retrievals over broadleaf forests with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm and to establish a basis for algorithm refinement. To sample natural variability in biophysical parameters of broadleaf forests, we selected MODIS data subsets covering deciduous broadleaf forests of the eastern part of North America and evergreen broadleaf forests of Amazonia. The analysis of an annual course of the Terra MODIS Collection 4 LAI product over broadleaf forests indicated a low portion of best quality main radiative transfer-based algorithm retrievals and dominance of low-reliable backup algorithm retrievals during the growing season. We found that this retrieval anomaly was due to an inconsistency between simulated and MODIS surface reflectances. LAI retrievals over dense vegetation are mostly performed over a compact location in the spectral space of saturated surface reflectances, which need to be accurately modeled. New simulations were performed with the stochastic radiative transfer model, which poses high numerical accuracy at the condition of saturation. Separate sets of parameters of the LAI algorithm were generated for deciduous and evergreen broadleaf forests to account for the differences in the corresponding surface reflectance properties. The optimized algorithm closely captures physics of seasonal variations in surface reflectances and delivers a majority of LAI retrievals during a phonological cycle, consistent with field measurements. The analysis of the optimized retrievals indicates that the precision of MODIS surface reflectances, the natural variability, and mixture of species set a limit to improvements of the accuracy of LAI retrievals over broadleaf forests. 2005 IEEE.
Nagler, P.L., Hinojosa-Huerta, O., Glenn, E.P., Garcia-Hernandez, J., Romo, R., Curtis, C., Huete, A.R. & Nelson, S.G. 2005, 'Regeneration of native trees in the presence of invasive saltcedar in the Colorado River Delta, Mexico', Conservation Biology, vol. 19, no. 6, pp. 1842-1852.
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Many riparian zones in the Sonoran Desert have been altered by elimination of the normal flood regime; such changes to the flow regime have contributed to the spread of saltcedar (Tamarix ramosissma Ledeb.), an exotic, salt-tolerant shrub. It has been proposed that reestablishment of a natural flow regime on these rivers might permit passive restoration of native trees, without the need for aggressive saltcedar clearing programs. We tested this proposition in the Colorado River delta in Mexico, which has received a series of large-volume water releases from U.S. dams over the past 20 years. We mapped the vegetation of the delta riparian corridor through ground and aerial surveys (1999-2002) and satellite imagery (1992-2002) and related vegetation changes to river flood flows and fire events. Although saltcedar is still the dominant plant in the delta, native cottonwood (Populus fremontii S. Wats.) and willow (Salix gooddingii C. Ball) trees have regenerated multiple times because of frequent flood releases from U.S. dams since 1981. Tree populations are young and dynamic (ages 5-10 years). The primary cause of tree mortality between floods is fire. Biomass in the floodplain, as measured by the normalized difference vegetation index on satellite images, responds positively even to low-volume (but long-duration) flood events. Our results support the hypothesis that restoration of a pulse flood regime will regenerate native riparian vegetation despite the presence of a dominant invasive species, but fire management will be necessary to allow mature tree stands to develop. 2005 Society for Conservation Biology.
Sano, E.E., Ferreira, L.G. & Huete, A.R. 2005, 'Synthetic aperture radar (L band) and optical vegetation indices for discriminating the Brazilian savanna physiognomies: A comparative analysis', Earth Interactions, vol. 9, no. 15.
The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere-Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (?) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados' physiognomies plus gallery forest. In contrast to the VIs, ? from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics.
Novo, E.M., Ferreira, L.G., Barbosa, C., Carvalho, C., Sano, E.E., Shimabukuro, Y.E., Huete, A., Potter, C., ROBERTS, D.A., HESS, L.L., MELACK, J.J., Yoshioka, H., Klooster, S., Kumar, V., Myneni, R.B., Ratana, P., Didan, K. & Miura, T. 2005, 'Advanced remote sensing techniques for global changes and Amazon ecosystem functioning studies', Acta Amazonica, vol. 35, no. 2, pp. 259-272.
This paper aims to assess the contribution of remote sensing technology in addressing key questions raised by the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). The answers to these questions foster the knowledge on the climatic, biogechemical and hydrologic functioning of the Amazon, as well as on the impact of human activities at regional and global scales. Remote sensing methods allow integrating information on several processes at different temporal and spatial scales. By doing so, it is possible to perceive hidden relations among processes and structures, enhancing their teleconnections. Key advances in the remote sensing science are summarized in this article, which is particularly focused on information that would not be possible to be retrieved without the concurrence of this technology
Nagler, P., Glenn, E.P., Hursh, K., Curtis, C. & Huete, A. 2005, 'Vegetation mapping for change detection on an arid-zone river', Environmental Monitoring and Assessment, vol. 109, no. 1-3, pp. 255-274.
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A vegetation mapping system for change detection was tested at the Havasu National Wildlife Refuge (HNWR) on the Lower Colorado River. A low-cost, aerial photomosaic of the 4200 ha, study area was constructed utilizing an automated digital camera system, supplemented with oblique photographs to aid in determining species composition and plant heights. Ground-truth plots showed high accuracy in distinguishing native cottonwood (Populus fremontii) and willow (Salix gooddingii) trees from other vegetation on aerial photos. Marsh vegetation (mainly cattails, Typha domengensis) was also easily identified. However, shrubby terrestrial vegetation, consisting of saltcedar (Tamarix ramosissima), arrowweed (Pluchea sericea), and mesquite trees (Prosopis spp.), could not be accurately distinguished from each other and were combined into a single shrub layer on the final vegetation map. The final map took the form of a base, shrub and marsh layer, which was displayed as a Normalized Difference Vegetation Index map from a Landsat Enhanced Thematic Mapper (ETM+) image to show vegetation intensity. Native willow and cottonwood trees were digitized manually on the photomosaic and overlain on the shrub layer in a GIS. By contrast to present, qualitative mapping systems used on the Lower Colorado River, this mapping system provides quantitative information that can be used for accurate change detection. However, better methods to distinguish between saltcedar, mesquite, and arrowweed are needed to map the shrub layer. Springer Science + Business Media, Inc. 2005.
Bauer, M., Carter, G., Dungan, J.L., Foody, G., Gitelson, A.A., Goetz, S., Huete, A.R., Nsset, E., Peuelas, J., Quattrochi, D.A., Stehman, S., Thenkabail, P. & Wigneron, J.P. 2005, 'Appointment of new editorial board members', Remote Sensing of Environment, vol. 95, no. 4, pp. 413-413.
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Ratana, P., Huete, A.R. & Ferreira, L. 2005, 'Analysis of cerrado physiognomies and conversion in the MODIS seasonal-temporal domain', Earth Interactions, vol. 9, no. 1.
The "cerrado" biome in central Brazil is rapidly being converted into pasture and agricultural crops with important consequences for local and regional climate change and regional carbon fluxes between the atmosphere and land surface. Satellite remote sensing provides an opportunity to monitor the highly diverse and complex cerrado biome, encompassing grassland, shrubland, woodland and gallery forests, and converted areas. In this study, the potential of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data is analyzed to discriminate among these diverse cerrado physiognomies and converted pastures based on their seasonal dynamics and phenology. Four years (2000-03) of MODIS 16-day composited, 250-m resolution vegetation index (VI) data were extracted over a series of biophysically sampled field study sites representing the major cerrado types. The temporal VI profiles over the cerrado formations exhibited high seasonal contrasts with a pronounced dry season from June to August and a wet growing season from November to March. The converted pasture areas showed the highest seasonal contrasts while the gallery forest formation had the lowest contrast. Seasonal VI variations were negatively correlated with woody canopy crown cover and provided a method to discriminate among converted cerrado areas, gallery forests, and the woody and herbaceous cerrado formations. The grassland and shrub cerrado formations, however, were difficult to separate based on their seasonal VI profiles. Maximum discrimination among the cerrado types occurred during the dry season where a positive linear relationship was found between VI and green cover. The annual integrated VI values showed the gallery forests and cerrado woodland as having the highest, and hence most annual productivity, while the more herbaceous shrub and grassland cerrado types were least productive. The cumulative VI profiles of converted cerrado, pasture areas varied distinctly in shape due to their strong dry season inactivity. Furthermore, the annual integrated VI values of the converted pastures differed significantly between the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) MODIS VI products, resulting in large discrepancies in productivity estimates relative to the native cerrado sites. This study shows that the MODIS seasonal-temporal VI profiles are highly useful in monitoring the cerrado biome and conversion-related activities.
Sano, E., Ferreira, L. & Huete, A.R. 2005, 'Synthetic aperture radar (l band) and optical vegetation indices for discriminating the Brazilian savanna physiognomies: A comparative analysis', Earth Interactions, vol. 9, no. 1.
The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere-Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (?) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados' physiognomies plus gallery forest. In contrast to the VIs, ? from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics.
Ferreira, L.G. & Huete, A.R. 2004, 'Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices', International Journal of Remote Sensing, vol. 25, no. 10, pp. 1837-1860.
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In this study, the response of vegetation indices (VIs) to the seasonal patterns and spatial distribution of the major vegetation types encountered in the Brazilian Cerrado was investigated. The Cerrado represents the second largest biome in South America and is the most severely threatened biome as a result of rapid land conversions. Our goal was to assess the capability of VIs to effectively monitor the Cerrado and to discriminate among the major types of Cerrado vegetation. A full hydrologic year (1995) of composited AVHRR, local area coverage (LAC) data was converted to Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) values. Temporal extracts were then made over the major Cerrado vegetation communities. Both the NDVI and SAVI temporal profiles corresponded well to the phenological patterns of the natural and converted vegetation formations and depicted three major categories encompassing the savanna formations and pasture sites, the forested areas, and the agricultural crops. Secondary differences in the NDVI and SAVI temporal responses were found to be related to their unique interactions with sun-sensor viewing geometries. An assessment of the functional behaviour of the VIs confirmed SAVI responds primarily to NIR variations, while the NDVI showed a strong dependence on the red reflectance. Based on these results, we expect operational use of the MODIS Enhanced Vegetation Index (EVI) to provide improved discrimination and monitoring capability of the significant Cerrado vegetation types. 2004 Taylor and Francis Ltd.
Nagler, P.L., Glenn, E.P., Lewis Thompson, T. & Huete, A. 2004, 'Leaf area index and normalized difference vegetation index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River', Agricultural and Forest Meteorology, vol. 125, no. 1-2, pp. 1-17.
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Leaf area index (LAI) and normalized difference vegetation index (NDVI) were compared for riparian species along a 350 km stretch of the Lower Colorado River in the United States and Mexico. The species included two native trees, cottonwood (Populus fremontii) and willow (Salix gooddingii), and two salt-tolerant shrubs, saltcedar (Tamarix ramosissima) and arrowweed (Pluchia sericea), exhibiting large differences in leaf type and canopy architecture. LAI was measured with a Licor 2000 plant canopy analyzer calibrated against biomass measurements of LAI, whereas NDVI was measured by low-level aerial photography using a DyCam digital camera with Red (R)-Blue (B)-near infrared (NIR) bands. In addition, reflectance spectra were measured for leaf samples collected from plants in the field. Leaf samples of all species had similar reflectance spectra in the visible (VIS) and NIR, hence similar NDVI values, ranging from 0.62 to 0.72 (P>0.05). LAI values of field plants varied over a relatively narrow range, with mean values of 3.50, 3.28, 2.81 and 3.69 for cottonwood, willow, saltcedar and arrowweed, respectively. However, field plants showed distinct species differences in NDVI, with the following mean values: cottonwood (0.686), willow (0.600), saltcedar (0.473) and arrowweed (0.254) (all significantly different at P<0.05). Differences in NDVI among field plants could be explained by differences in the light extinction coefficient, k, for plant canopies, according to the formula: fIRs=(1-e-kLAI), where fIRs is the fraction of incident light intercepted by the canopy. At one extreme, cottonwood had broad leaves that faced the sun, and a calculated k of 1.25, whereas at the other extreme, arrowweed had linear leaves that were near to vertical, and had a k of 0.15. Ecophysiological implications of the differences among the species are discussed. 2004 Elsevier B.V. All rights reserved.
Ferreira, L.G., Yoshioka, H., Huete, A. & Sano, E.E. 2004, 'Optical characterization of the Brazilian Savanna physiognomies for improved land cover monitoring of the cerrado biome: Preliminary assessments from an airborne campaign over an LBA core site', Journal of Arid Environments, vol. 56, no. 3, pp. 425-447.
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It is estimated that approximately 40% of the Cerrado, the second largest biome in South America, have been already converted. In this study, situated within the scope of the Large Scale Biosphere-Atmosphere Experiment in Amazonia project (LBA), we conducted a wet season ground and airborne campaign over the Brasilia National Park (BNP), the largest LBA core site in the Cerrado biome, to measure the optical and biophysical properties of the major Cerrado land cover types. We investigated land cover discrimination through the analyses of fine resolution spectra, convolved spectra (MODIS bandpasses), and vegetation indices - the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). At these three data levels, three major physiognomic domains (herbaceous, woody, and forested) could be readily identified, and the amount of data correctly classified into the five major land cover types found at BNP were 91% (full spectra), 78% (red and NIR), 75% (NDVI), and 71% (EVI). A synergism between the NDVI and EVI was also evident, and together, these two indices were capable of correctly classifying 82% of the total data set. Our results indicate the possibility of utilizing the MODIS NDVI and EVI images for operational land cover assessments in the Cerrado region. 2003 Elsevier Ltd. All rights reserved.
Yoshioka, H., Miura, T. & Huete, A.R. 2003, 'An isoline-based translation technique of spectral vegetation index using EO-1 Hyperion data', IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 6 PART I, pp. 1363-1372.
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The availability of similar satellite data products from multiple sensors has focused much attention on the issue of continuity across satellite data products from past, current, and future sensors. Hyperspectral datasets acquired over a variety of land cover types are extremely useful in attempting to resolve spectral differences in the global datasets from different sensors. The datasets from the Earth Observing 1 (EO-1) Hyperion sensor are very suitable for this purpose, as is airborne hyperspectral data. In this paper, we examine the possibility of translating vegetation index (VI) data between two sensors by using imagery from the Hyperion sensor and utilizing the vegetation isoline concept. The objectives of this paper are to introduce and test a VI translation technique, focused on the spectral differences associated with sensor spectral bandpass filters. The translation of global VI datasets from one sensor to another requires a methodology applicable over various land cover types and throughout the wide ranges in VI values. To meet these requirements, a technique is proposed that utilizes adjustable translation coefficients, based on an estimation of the leaf area index value relative to a numerical canopy model. The theoretical basis of the proposed translation algorithm is explained in terms of the vegetation isoline concept. Its performance was tested through a numerical experiment with a Hyperion image, focusing on the normalized difference vegetation index (NDVI) as a representative vegetation index. The results indicate the potential of the isoline-based translation technique for stable translation throughout wide ranges of NDVI values.
Wang, Z.X., Liu, C. & Huete, A. 2003, 'From AVHRR-NDVI to MODIS-EVI: Advances in vegetation index research', Acta Ecologica Sinica, vol. 23, no. 5, pp. 979-987.
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Huete, A.R., Miura, T. & Gao, X. 2003, 'Land cover conversion and degradation analyses through coupled soil-plant biophysical parameters derived from hyperspectral EO-1 Hyperion', IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 6 PART I, pp. 1268-1276.
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Land degradation in semiarid areas results from various factors, including climate variations and human activity, and can lead to desertification. The process of degradation results in simultaneous and complex variations of many interrelated soil and vegetation biophysical parameters, rendering it difficult to develop simple and robust remote sensing mapping and monitoring approaches. In this study, we tested the use of Earth Observing 1 (EO-1) Hyperion hyperspectral data to analyze land degradation patterns within the protected acun Biosphere Reserve and surrounding areas in the Monte Desert region of Argentina. The floristically diverse vegetation communities included mesquite forest (algarrobal), creosotebush (jarillal), sand-dune (medanal), and severely degraded (peladal) sites. Various optical measures of land degradation were employed, including vegetation indexes, spectral derivatives, albedo, and spectral mixture analysis. Spectral mixture analysis provided the best characterization of the unstable and spatially variable landscape encountered at the acun Biosphere Reserve. Spectral unmixing provided simultaneous measures of green vegetation, nonphotosynthetic vegetation, and soil, all of which were deemed essential in characterizing land degradation. In conjunction with multitemporal data from the more commonly employed broadband sensors, hyperspectral data can provide a powerful methodology toward understanding environmental degradation.
Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C. & Huete, A. 2003, 'Monitoring vegetation phenology using MODIS', Remote Sensing of Environment, vol. 84, no. 3, pp. 471-475.
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Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate-biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success. 2002 Elsevier Science Inc. All rights reserved.
Gao, X., Huete, A.R. & Didan, K. 2003, 'Multisensor comparisons and validation of MODIS vegetation indices at the semiarid jornada experimental range', IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 10 PART I, pp. 2368-2381.
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Vegetation indices (VIs) are one of the standard science products available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Earth Observing System (EOS) Terra platform, launched in December 1999. An important requirement of MODIS science products is that they be rigorously validated. In this study, we conducted a site-intensive MODIS VI product validation at the semiarid Jornada Experimental Range, New Mexico, an EOS Land Validation Core Site. Our validation approach involved scaling up independent fine-grained datasets, including ground and airborne radiometry, and high spatial resolution imagery [Enhanced Thematic Mapper Plus (ETM+)], to the coarser MODIS spatial resolutions. The MODIS VIs were evaluated with respect to their radiometric performances, the uncertainties of the compositing methodology, and their capabilities to depict seasonal variations in vegetation. The MODIS Quick Airborne Looks (MQUALS) radiometric package was found useful in up-scaling field in situ measurements to coarser spatial resolutions. Both single-day nadir-view and 16-day composited MODIS reflectances and VIs matched well with the nadir-based atmosphere-free MQUALS observations for all the land cover types found at Jornada, with the root mean squared deviations less than 0.03. The MODIS 16-day composited products also performed well with the single-day nadir-view MODIS data, despite some off-nadir view angles and uncertainties with the cloud mask algorithm. The quality assurance (QA)-based constrained view angle-maximum value composite (CV-MVC) algorithm successfully filtered out much of the cloud and aerosol contaminated observations and helped to minimize view angle-related problems. The MODIS seasonal VI profiles also matched quite well with the other multiple sensor datasets obtained at the finer spatial resolutions. The QA information was found to be crucial in achieving consistent spatial and temporal comparisons of global vegetation conditions and for deriving accurate depictions of important phenological features in multitemporal MODIS data. The results of this validation study over the Jornada Experimental Range demonstrated the accuracy, reliability, and science utility of the MODIS VI products in arid and semiarid areas.
Ferreira, L.G., Yoshioka, H., Huete, A. & Sano, E.E. 2003, 'Seasonal landscape and spectral vegetation index dynamics in the Brazilian Cerrado: An analysis within the Large-Scale Biosphere-Atmosphere Experiment in Amaznia (LBA)', Remote Sensing of Environment, vol. 87, no. 4, pp. 534-550.
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The Brazilian Cerrado biome comprises a vertically structured mosaic of grassland, shrubland, and woodland physiognomies with distinct phenology patterns. In this study, we investigated the utility of spectral vegetation indices in differentiating these physiognomies and in monitoring their seasonal dynamics. We obtained high spectral resolution reflectances, during the 2000 wet and dry seasons, over the major Cerrado types at Brasilia National Park (BNP) using the light aircraft-based Modland Quick Airborne Looks (MQUALS) package, consisting of a spectroradiometer and digital camera. Site-intensive biophysical and canopy structural measurements were made simultaneously at each of the Cerrado types including Cerrado grassland, shrub Cerrado, wooded Cerrado, Cerrado woodland, and gallery forest. We analyzed the spectral reflectance signatures, their first derivative analogs, and convolved spectral vegetation indices (VI) over all the Cerrado physiognomies. The high spectral resolution data were convolved to the MODIS, AVHRR, and ETM + bandpasses and converted to the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) to simulate their respective sensors. Dry and wet season comparisons of the measured biophysical attributes were made with the reflectance and VI data for the different Cerrado physiognomies. We found that three major domains of Cerrado could be distinguished with the dry and wet season spectral signatures and vegetation indices. The EVI showed a higher sensitivity to seasonality than the NDVI; however, both indices displayed seasonal variations that were approximately one-half that found with the measured landscape green cover dynamics. Inter-sensor comparisons of seasonal dynamics, based on spectral bandpass properties, revealed the ETM +-simulated VIs had the best seasonal discrimination capability, followed by MODIS and AVHRR. Differences between sensor bandpass-derived VI values, however, varied with Cerrado type and between dry and wet seasons, indicating the need for inter-sensor VI translation equations for effective multi-sensor applications. 2003 Elsevier Inc. All rights reserved.
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Ferreira, L.G. 2002, 'Overview of the radiometric and biophysical performance of the MODIS vegetation indices', Remote Sensing of Environment, vol. 83, no. 1-2, pp. 195-213.
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We evaluated the initial 12 months of vegetation index product availability from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System-Terra platform. Two MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are produced at 1-km and 500-m resolutions and 16-day compositing periods. This paper presents an initial analysis of the MODIS NDVI and EVI performance from both radiometric and biophysical perspectives. We utilize a combination of site-intensive and regionally extensive approaches to demonstrate the performance and validity of the two indices. Our results showed a good correspondence between airborne-measured, top-of-canopy reflectances and VI values with those from the MODIS sensor at four intensively measured test sites representing semi-arid grass/shrub, savanna, and tropical forest biomes. Simultaneously derived field biophysical measures also demonstrated the scientific utility of the MODIS VI. Multitemporal profiles of the MODIS VIs over numerous biome types in North and South America well represented their seasonal phenologies. Comparisons of the MODIS-NDVI with the NOAA-14, 1-km AVHRR-NDVI temporal profiles showed that the MODIS-based index performed with higher fidelity. The dynamic range of the MODIS VIs are presented and their sensitivities in discriminating vegetation differences are evaluated in sparse and dense vegetation areas. We found the NDVI to asymptotically saturate in high biomass regions such as in the Amazon while the EVI remained sensitive to canopy variations. 2002 Elsevier Science Inc. All rights reserved.
Nagler, P.L., Glenn, E.P. & Huete, A.R. 2001, 'Assessment of spectral vegetation indices for riparian vegetation in the Colorado River delta, Mexico', Journal of Arid Environments, vol. 49, no. 1, pp. 91-110.
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This study tested the relationship between three, commonly-used vegetation indices (VIs), percent vegetation cover (% cover) and leaf area index (LAI) over a complex riparian landscape in the Colorado River delta, Mexico. Our objective was to correlate vegetation and soil features with VIs using low-level aerial photography, in preparation for scaling up to analysis of vegetation features using satellite imagery. We used a three-band digital imaging camera (Dycam) to collect data from an aircraft flying at 150 m. A series of 84 images (67100 m) were analysed. Nine of these sites were ground-truthed; the species, % cover, and LAI were determined. Measured LAI (nine sites) from tree, shrub, and groundcover categories were used to determine a global (GLAI) value for 63 images. We conducted both VIs: % cover and VIs: GLAI regression analyses. The normalized difference vegetation index (NDVI) was the VI that best predicted % cover (r2=0.837), but the soil adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) gave nearly equal results (r2=0.807 and 0.796, respectively). Normalized difference vegetation index, SAVI and EVI were less useful in predicting GLAI (r2=0.73, 0.65, 0.64, respectively). Variability in GLAI was due mainly to differences in % cover among images rather than differences in LAI among vegetation types. We also measured reflectance values of the major plant types between 450 and 900 nm, and found small but significant (p<0.05) differences among some of the species. The results support the conclusion that vegetation indices are most simply related to % vegetation cover, rather than species differences in LAI or VIs, even in this mixed riparian biome. There was also a near 1:1 correspondence between the Dycam and Thematic Mapper (TM) NDVI values over a wide range of landcover types (water, bare soil, partial and complete vegetation cover), which indicate that reflectance-based NDVI values can be scaled from low-level aerial Dycam images to satellite images for this ecosystem. 2001 Academic Press.
Miura, T., Huete, A.R., Yoshioka, H. & Holben, B.N. 2001, 'An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction', Remote Sensing of Environment, vol. 78, no. 3, pp. 284-298.
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An error and sensitivity analysis was conducted to investigate the capabilities of the atmospheric resistant vegetation indices (VIs) for minimizing "residual aerosol" effects. The residual aerosol effects result from the assumptions and characteristics of the dark target (DT) approach used to estimate aerosol optical properties in the atmospheric correction scheme (referred to as the dark target-based atmospheric correction, DTAC). The performances of two atmospheric resistant VIs, the atmospherically resistant vegetation index (ARVI) and enhanced vegetation index (EVI), were evaluated and compared with the normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI). The atmospheric resistant Vis successfully minimized the residual aerosol effects, resulting in a 60% reduction of the errors from the NDVI and SAVI when a proper aerosol model was used for the estimation and correction of aerosol effects. The reductions were greater for thicker aerosol atmosphere (larger aerosol optical thickness, AOT). The atmospheric resistant VIs, however, resulted in having larger bias errors than the NDVI and SAVI when an improper aerosol model was used. The application of atmospheric resistant VIs to the DTAC-derived surface reflectances is exactly what is being carried out by the Moderate Resolution Imaging Spectroradiometer (MODIS) VI algorithm. These results raise several issues for the effective, operational use of the DTAC algorithm and atmospheric resistant VIs, which are addressed in this paper. 2001 Elsevier Science Inc. All rights reserved.
Zamora-Arroyo, F., Nagler, P.L., Briggs, M., Radtke, D., Rodriquez, H., Garcia, J., Valdes, C., Huete, A. & Glenn, E.P. 2001, 'Regeneration of native trees in response to flood releases from the United States into the delta of the Colorado River, Mexico', Journal of Arid Environments, vol. 49, no. 1, pp. 49-64.
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Over the past 20 years, discharge of water from the United States to the delta of the Colorado River in Mexico has regenerated native trees that now account for 23% of vegetation in a 100-km, non-perennial, stretch of river below Morelos Dam at the United States-Mexico border. The discharges are associated with the filling of Lake Powell, the last large reservoir to be constructed on the river, and with ENSO cycles that bring extra winter and spring precipitation to the watershed. The discharges below Morelos Dam produce overbank foods that germinate new cohorts of Populus fremontii and Salix gooddingii trees. Relatively little flood water from the United States is required to support a pulse flood regime that can result in regrowth of native vegetation in the delta. Based on analysis of past flows and existing tree populations, we estimate that a February-April flow of 3109 m3 at 80-120 m3 s-1 is sufficient to germinate and establish new cohorts of native trees. However, there was a positive correlation between frequency of flows and total vegetation cover over the years 1992-1999, showing that more frequent flows would further increase vegetation cover. The results support the importance of pulse floods in restoring the ecological integrity of arid-zone rivers. 2001 Academic Press.
Yoshioka, H., Miura, T., Huete, A.R. & Ganapol, B.D. 2000, 'Analysis of vegetation isolines in red-NIR reflectance space', Remote Sensing of Environment, vol. 74, no. 2, pp. 313-326.
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The characteristic behavior of red-near-infrared (NIR) reflectance-based vegetation isolines were analyzed by focusing on its three features: the slope, NIR-intercept, and the intersection between the vegetation isoline and the soil line. These properties are the key factors in understanding variations of vegetation index values with changes of canopy background brightness, known as background noise. The analysis was conducted based on a vegetation isoline equation derived by using the representation of canopy reflectance by the adding method. The isoline parameters, slopes, and NIR-intercepts of vegetation isolines were numerically obtained by the SAIL canopy model. Some of the known behaviors of the vegetation isoline were simulated and analyzed in detail. (C) Elsevier Science Inc., 2000.
Qi, J., Kerr, Y.H., Moran, M.S., Weltz, M., Huete, A.R., Sorooshian, S. & Bryant, R. 2000, 'Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region', Remote Sensing of Environment, vol. 73, no. 1, pp. 18-30.
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The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI-SVI relation between remotely sensed variables, such as SVI and biophysical variables, such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization, procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters, by the model. In this, study, we present a strategy that combines BRDF models and conventional LAI-SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI-SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was, applied to Landset TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements. (C) Elsevier Science Inc., 2000.
Gao, X., Huete, A.R., Ni, W. & Miura, T. 2000, 'Optical-biophysical relationships of vegetation spectra without background contamination', Remote Sensing of Environment, vol. 74, no. 3, pp. 609-620.
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For a better evaluation of the accuracy of VIs in estimating biophysical parameters, a "true" VI value attributed only to the vegetation signal and free of any contamination is needed. In this article, pure vegetation spectra were extracted from a set of open and closed canopies by unmixing the green vegetation signal from the background component. Canopy model-simulation and reflectances derived from graph-based linear extrapolation were used to unmix and derive a "true" vegetation signal, equivalent to a perfect absorber (free boundary) canopy background reflectance condition. Optical-biophysical relationships were then derived for a variety of canopy structures with differences in foliage clumping, horizontal heterogeneity, and leaf type. A 3-dimensional canopy radiative transfer model and a hybrid geometric optical-radiative transfer model (GORT) were used to simulate the directional-hemispherical reflectances from agricultural, grassland, and forested canopies (cereal and broadleaf crop, grass, needleleaf, and broadleaf forest). The relationships of the extracted red and near-intimated reflectances and derived vegetation indices (VIs) to various biophysical parameters (leaf area index, fraction of absorbed photosynthetically active radiation, and percent ground cover) were examined for the pure vegetation spectra. The results showed normalized difference vegetation index (NDVI) relationships with biophysical parameters to become more asymptotic over the pure vegetation condition. The extraction of pure vegetation signals had little effect on the soil-adjusted vegetation index (SAVI), which had values equivalent to those obtained with the presence of a background signal. NDVI values were fairly uniform across the different canopy types, whereas the SAVI values had pronounced differences among canopy types, particularly between the broadleaf and cereal/needleleaf structural types. These results were useful not only in selecting suitable vegetation indices to characterize specific canopy biophysical parameters, but also in understanding a "true" VI behavior, free of background noise. 2000 Elsevier Science Inc.
Yoshioka, H., Huete, A.R. & Miura, T. 2000, 'Derivation of vegetation isoline equations in red-NIR reflectance space', IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 2 I, pp. 838-848.
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A technique to derive vegetation isoline equations in red-NIR reflectance space for homogeneous canopies is proposed and demonstrated. A canopy radiative transfer model, known as the Cooper-Smith-Pitts model, is utilized with truncation of the higher order interaction term between the canopy and soil layers. The technique consists of two model simulations, one with a perfect absorber as canopy background and the other with an arbitrary background to estimate the canopy optical properties necessary for the determination of the isoline parameters. These cases are independent of the soil optical properties of any specific site. Hence, the results can be used for any type or series of soils to construct the vegetation isoline equation. A set of simulations was also conducted using the SAIL model to demonstrate the vegetation isoline derivation by the proposed technique. Reflectances and vegetation indices (VI) estimated from the vegetation isoline generally showed good agreement with those simulated by the SAIL model, especially for relatively darker soil. The isoline equation and derivation were found to be useful for further study of two-band VI's and their variation with canopy background.
Miura, T., Huete, A. & Yoshioka, H. 2000, 'Evaluation of Sensor Calibration Uncertainties on Vegetation Indices for MODIS', IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 3, pp. 1399-1409.
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The impact of reflectance calibration uncertainties on the accuracies of several vegetation indices (VIs) was evaluated for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the TERRA platform. A set of uncertainty propagation equations were designed to model the propagation of calibration uncertainties from top-of-atmosphere (TOA) reflectances to atmospherically-corrected VIs. The soil-adjusted vegetation index (SAVI), the atmospherically-resistant vegetation index (ARVI), and the enhanced vegetation index (EVI) were evaluated along with the normalized difference vegetation index (NDVI). The resultant VI uncertainties associated with calibration ucal (VI) varied with both surface reflectances and atmospheric conditions. Uncertainties in the NDVI and ARVI were highly dependent on pixel brightness, with the largest uncertainties occurring over dark targets with little or no vegetation. The SAVI uncertainties were nearly constant throughout a range of target brightness and vegetation abundance. The EVI uncertainties linearly increased with increasing EVI values. Atmosphere turbidities increased calibration uncertainties in all the VIs through their effect on TOA reflectances. The VI uncertainties were also found to decrease when the calibration errors were positively correlated between bands.
Reynolds, C.A., Yitayew, M., Slack, D.C., Hutchinson, C.F., Huete, A. & Petersen, M.S. 2000, 'Estimating crop yields and production by integrating the FAO Crop specific Water Balance model with real-time satellite data and ground-based ancillary data', INTERNATIONAL JOURNAL OF REMOTE SENSING, vol. 21, no. 18, pp. 3487-3508.
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Accio, L.J. & Huete, A. 2000, 'Resposta espectral de solos em razao do angulo de visada, da umidade e da rugosidade superficial (Soil Spectral Response In Relation To Viewing Angle, Soil Moisture And Surface Roughness)', Pesquisa Agropecuaria Brasileira, vol. 35, no. 12, pp. 2473-2484.
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The objective of this study was to characterize the bi-directional reflectance factor (BRF) of three soil series (McAllister, Stronghold, and Epitaph) located at the Walnut Gulch Experimental Watershed (Arizona, USA) as a function of the viewing angle, soil moisture and surface roughness. Soil spectra were taken in the visible, near and mid-infrared regions, convolved to match the Landsat-TM bands, and the results were nonnalized to the Nadir response and expressed as relative BRE The anisotropic behavior varies from soil to soil and it was higher when the following conditions were taken combined or individually: shorter wavelengths, higher viewing and solar zenith angles, in the backscatteling direction, soil in the dry condition as opposed to wet condition, and in the rough surface as compared to the smooth surface of Epitaph soil series (the only soil tested for the effect of roughness). Rough and smooth surfaces of Epitaph soil, however, were better discriminated in the forward scattering direction. Differences in scale and methods used to obtain the spectral curves were pointed out as responsible for the enhancement of the anisotropic behavior of the soils for lab results as compared to field results.
Fardella, C.E., Mosso, L., Gmez-Snchez, C., Corts, P., Soto, J., Gmez, L., Pinto, M., Huete, A., Oestreicher, E., Foradori, A. & Montero, J. 2000, 'Primary hyperaldosteronism in essential hypertensives: Prevalence, biochemical profile, and molecular biology', Journal of Clinical Endocrinology and Metabolism, vol. 85, no. 5, pp. 1863-1867.
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There is evidence that primary aldosteronism (PA) may be common in patients with essential hypertension (EH) when determinations of serum aldosterone (SA), plasma renin activity (PRA), and the SA/PRA ratio are used as screening. An inherited form of primary hyperaldosteronism is the glucocorticoid-remediable aldosteronism (GRA) caused by an unequal crossing over between the CYB11B1 and CYP11B2 genes that results in a chimeric gene, which has aldosterone synthase activity regulated by ACTH. The aim of this study was to evaluate the prevalence of PA and the GRA in 305 EH patients and 205 normotensive controls. We measured SA (1-16 ng/dL) and PRA (1-2.5 ng/mLh) and calculated the SA/PRA ratio in all patients. A SA/PRA ratio level greater than 25 was defined as being elevated. PA was diagnosed in the presence of high SA levels (> 16 ng/dL), low PRA levels (<0.5 ng/mLh), and very high SA/PRA ratio (>50). Probable PA was diagnosed when the SA/PRA ratio was more than 25 but the other criteria were not present. A Fludrocortisone test was done to confirm the diagnosis. GRA was differentiated from other forms of PA by: the aldosterone suppression test with dexamethasone, the high levels of 18-hydroxycortisol, and the genetic detection of the chimeric gene. In EH patients, 29 of 305 (9.5%) had PA, 13 of 29 met all the criteria for PA, and 16 of 29 were initially diagnosed as having a probable PA and confirmed by the fludrocortisone test. Plasma potassium was normal in all patients. The dexamethasone suppression test was positive for GRA in 10 of 29 and 18-hydroxycortisol levels were high in 2 of 29 patients who had also a chimeric gene. In normotensive subjects, 3 of 205 (1.46%) had PA, and 1 of 205 had a GRA. In summary, we found a high frequency of normokalemic PA in EH patients. A high proportion of PA suppressed SA with dexamethasone, but only a few had a chimeric gene or high levels of 18-hydroxycortisol. These results emphasize the need to further investigate EH patients.
De Oliveira Accioly, L.J. & Huete, A.R. 2000, 'Soil spectral response in relation to viewing angle, soil moisture and surface roughness', Pesquisa Agropecuaria Brasileira, vol. 35, no. 12, pp. 2473-2484.
The objective of this study was to characterize the bi-directional reflectance factor (BRF) of three soil series (McAllister, Stronghold, and Epitaph) located at the Walnut Gulch Experimental Watershed (Arizona, USA) as a function of the viewing angle, soil moisture and surface roughness. Soil spectra were taken in the visible, near and mid-infrared regions, convolved to match the Landsat-TM bands, and the results were normalized to the Nadir response and expressed as relative BRF. The anisotropic behavior varies from soil to soil and it was higher when the following conditions were taken combined or individually: shorter wavelengths, higher viewing and solar zenith angles, in the backscattering direction, soil in the dry condition as opposed to wet condition, and in the rough surface as compared to the smooth surface of Epitaph soil series (the only soil tested for the effect of roughness). Rough and smooth surfaces of Epitaph soil, however, were better discriminated in the forward scattering direction. Differences in scale and methods used to obtain the spectral curves were pointed out as responsible for the enhancement of the anisotropic behavior of the soils for lab results as compared to field results.
Van Leeuwen, W.J.D., Huete, A.R. & Laing, T.W. 1999, 'MODIS vegetation index compositing approach: A prototype with AVHRR data', Remote Sensing of Environment, vol. 69, no. 3, pp. 264-280.
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In this study, the 16-day MODIS (MODerate resolution Imaging Spectroradiometer) vegetation index (VI) compositing algorithm and product were described, evaluated, and compared with the current AVHRR (Advanced Very High Resolution Spectroradiometer) maximum value composite (MVC) approach. The MVC method selects the highest NDVI (normalized difference vegetation index) over a certain time interval. The MODIS VI compositing algorithm emphasizes a global and operational view angle standardization approach: a reflectance-based BRDF (Bidirectional Reflectance Distribution Function) model, succeeded by a back-up MVC algorithm that includes a view angle constraint. A year's worth of daily global AVHRR data was used to prototype the MODIS vegetation index compositing algorithm. The composite scenarios were evaluated with respect to: 1) temporal evolution of the VI for different continents and vegetation types, 2) spatial continuity of the VI, 3) quality flags related to data integrity, cloud cover, and composite method, and 4) view angle distribution of the composited data. On a continental scale, the composited NDVI values from the MODIS algorithm were as much as 30% lower than the mostly, off-nadir NDVI results based on the MVC criterion. The temporal evolution of the NDVI values derived with the MODIS algorithm were similar to the NDVI values derived from the MVC algorithm. A simple BRDF model was adequate to produce nadir equivalent reflectance values from which the NDVI could be computed. Application of the BRDF and 'back-up' components in the MODIS algorithm were dependent on geographic location and season, for example, the BRDF interpolation was most frequently applied in arid and semiarid regions, and during the dry season over humid climate vegetation types. Examples of a MODIS-like global NDVI map and associated quality flags were displayed using a pseudo color bit mapping scheme.
Sano, E.E., Moran, M.S., Huete, A.R. & Miura, T. 1998, 'C- and multiangle Ku-band synthetic aperture radar data for bare soil moisture estimation in agricultural areas', Remote Sensing of Environment, vol. 64, no. 1, pp. 77-90.
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A sensitivity analysis of C-band (5.3 GHz) and Ku-band (14.85 GHz) synthetic aperture radar (SAR) data to the bare soil moisture content of agricultural fields was conducted in this study. The C-band data were obtained with a 23incidence angle, whereas the Ku-band data were obtained with 35, 55, and 75incidence angles. The fields presented either a small-scale or an intermediate-scale periodic soil roughness components, associated with level-basin and furrow irrigation systems, respectively. For fields with a small-scale roughness component, the SAR data were sensitive to soil moisture, particularly at the C-band with a 23incidence angle and Ku-band with a 35incidence angle. For fields with a intermediate-scale roughness component, both C- and Ku-band data were nearly insensitive to soil moisture. By using a theoretical surface scattering model, this study also analyzed the effects of different soil roughness components [root mean square (RMS) height h, correlation length, and periodic row structure] in the SAR data. For fields with RMS height <0.3 cm, a small variation in h (from 0.1 to 0.3 cm) provoked a significant variation in the SAR data (up to 8 dB).
Miura, T., Huete, A.R., Van Leeuwen, W.J.D. & Didan, K. 1998, 'Vegetation detection through smoke-filled AVIRIS images: An assessment using MODIS band passes', Journal of Geophysical Research: Atmospheres, vol. 103, no. D24, pp. 32001-32011.
Radiometrically calibrated, Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images acquired during the Smoke, Clouds and Radiation in Brazil (SCAR-B) experiment were processed to simulate vegetation index (VI) imagery with the Moderate Resolution Imaging Spectroradiometer (MODIS) band passes. Data sets were extracted from tropical forested areas, burned fields, and shrub/grassland areas over both clear and variable smoke conditions with average aerosol optical thickness (AOT) values at 0.67 Jim of 0.14, 1.1, and 1.9, respectively. The atmospheric resistant VIs and various middle-infrared (MIR) derived VIs were then analyzed with respect to their ability to minimize atmospheric "smoke" contamination. The atmospheric resistant VIs utilized the blue band for correction of the red band, while the MIR-derived VIs used the MIR region (1.3 - 2.5 ?m) as a substitute for the red band since it is relatively transparent to smoke, yet remains sensitive to green vegetation. The performance of these indices were assessed and compared with the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI). Over the tropical forests the NDVI and SAVI had high relative errors over all smoke-filled atmospheric conditions (50-80% error), while the atmospheric resistant VIs resulted in a 50-80% relative error only over thick levels of smoke. Over optically thin levels (AOT at 0.67 ?m < 1.1) they performed much better with a 20-40% relative error. The MIR-derived VIs, on the other hand, outperformed all other VIs over forested areas (? 5% error). However, over burned fields with minimal amounts of green biomass the MIR-derived VIs had the highest levels of error due to smoke (> 40%), while all other indices had errors below 20%. In the shrub/grassland site, the atmospheric resistant indices behaved similarly with the MIR-derived indices, with both less sensitive to smoke than the NDVI and SAVI. We conclude that the MIR indices, particularly with MODIS band 7 (2.13 ?m), are useful in vegetation monitoring over forested areas during the burning season. However, they did not perform well in areas outside of forests such as burned areas and shrub/grassland. Copyright 1998 by the American Geophysical Union.
Justice, C.O., Vermote, E., Townshend, J.R.G., Defries, R., Roy, D.P., Hall, D.K., Salomonson, V.V., Privette, J.L., Riggs, G., Strahler, A., Lucht, W., Myneni, R.B., Knyazikhin, Y., Running, S.W., Nemani, R.R., Wan, Z., Huete, A.R., Van Leeuwen, W., Wolfe, R.E., Giglio, L., Muller, J.-.P., Lewis, P. & Barnsley, M.J. 1998, 'The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research', IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 4, pp. 1228-1249.
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The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation. 1998 IEEE.
Sano, E.E., Jiaguo, Q., Huete, A.R. & Moran, M.S. 1998, 'The use of SAR/TM synergy for estimating soil moisture content over a semi-arid rangeland', Second Latino-American seminar on radar remote sensing image processing techniques, pp. 175-183.
The C-band ERS-1 SAR data were combined with the Landsat TM data to improve the soil moisture estimates in a semiarid region. The SAR data were compared with the soil moisture measurements at three conditions: a) without any correction for soil roughness and vegetation effects; b) corrected for soil roughness effects; and c) corrected for both soil roughness and vegetation effects. The soil roughness effects were taken into account by using a dry season SAR image. The vegetation influence was considered by using an empirical relationship between SAR and leaf area index data, the latter being derived from TM images. Results indicated that the contribution of soil roughness and vegetation in the radar backscatter were significant and they must be taken into account to obtain accurate soil moisture estimations.
Sano, E.E., Huete, A.R., Troufleau, D., Moran, M.S. & Vidai, A. 1998, 'Relation between ERS-1 synthetic aperture radar data and measurements of surface roughness and moisture content of rocky soils in a semiarid rangeland', Water Resources Research, vol. 34, no. 6, pp. 1491-1498.
Surface roughness and soil moisture content control the distribution of rainfall into runoff, evapotranspiration, and infiltration. Satellite radar data have the potential to provide spatial and multitemporal estimates of these variables, depending upon the sensor configuration and field condition. The relation between the European Remote Sensing Satellite (ERS-1) synthetic aperture radar (SAR) data and measurements of surface roughness and moisture content of rocky soils in a semiarid rangeland in southeast Arizona was analyzed in this study. A dry and a wet season C band SAR image were acquired and corrected for topographic effects. Field soil roughness and moisture content data were obtained from 47 sampling sites. An intensive soil moisture sampling campaign was also conducted at three sites to determine the number of samples necessary to estimate soil moisture content with 10% accuracy. Dry and wet season SAR data were found to be correlated (r2 = 0.80 and 0.59, respectively) with root-mean-square (RMS) height measurements, while SAR data from the wet season image were poorly correlated with soil moisture. The results indicated that C band SAR data are promising for estimation of surface roughness in semiarid rangelands. However, they are less promising for soil moisture estimation, unless the effects of soil roughness and vegetation are removed. The acquisition of an adequate number of soil moisture samples to obtain representative soil moisture measurements is also a key issue in the validation of soil moisture retrieval from SAR data. In the study area, at least 17 samples per hectare were needed to obtain soil moisture estimates with 10% accuracy.
Huete, A.R., Liu, H.Q., Batchily, K. & Van Leeuwen, W. 1997, 'A comparison of vegetation indices over a global set of TM images for EOS-MODIS', Remote Sensing of Environment, vol. 59, no. 3, pp. 440-451.
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A set of Landsat Thematic Mapper images representing a wide range of vegetation conditions from the NASA Landsat Pathfinder, global land cover test site (GLCTS) initiative were processed to simulate the Moderate Resolution Imaging Spectroradiometer (MODIS), global vegetation index imagery at 250 m pixel size resolution. The sites included boreal forest, temperature coniferous forest, temperate deciduous forest, tropical rainforest, grassland, savanna, and desert biomes. Differences and similarities in sensitivity to vegetation conditions were compared among various spectral vegetation indices (VIs). All VIs showed a qualitative relationship to variations in vegetation. However, there were significant differences among the VIs over desert, grassland, and forested biomes. The normalized difference vegetation index (NDVI) was sensitive to and responded primarily to the highly absorbing red reflectance band, while other indices such as the soil and atmosphere resistant vegetation index (SARVI) were more responsive to variations in the near-infrared (NIR) band. As a result, we found the NDVI to mimic red reflectances and saturate over the forested sites while the SARVI, by contrast, did not saturate and followed variations in NIR reflectances. In the arid and semiarid biomes, the NDVI was much more sensitive to canopy background variations than the SARVI. Maximum differences among vegetation index behavior occurred over the evergreen needleleaf forest sites relative to the deciduous broadleaf forests and drier, grassland, and shrub sites. These differences appear to be useful in complementing the NDVI for improved monitoring of vegetation, with the NDVI sensitive to fraction of absorbed photosynthetic active radiation and the SARVI more sensitive to structural canopy parameters such as leaf area index and leaf morphology.
Santibez, F., Morales, L., De La Fuente, J., Cellier, P. & Huete, A. 1997, 'Topoclimatic modeling for minimum temperature prediction at a regional scale in the Central Valley of Chile', Agronomie, vol. 17, no. 6-7, pp. 307-314.
Spring frost may strongly affect fruit production in the Central Valley of Chile. Minimum temperatures are spatially variable owing to topography and soil conditions. A methodology for forecasting minimum temperature at a regional scale in the Central Valley of Chile, integrating spatial variability of temperature under radiative frost conditions, has been developed. It uses simultaneously a model for forecasting minimum temperatures at a reference station using air temperature and humidity measured at 6 pm, and topoclimatic models, based on satellite infra-red imagery (NOAA/AVHRR) and a digital elevation model, to extend the prediction at a regional scale. The methodological developments were integrated in a geographic information system for georeferencing of a meteorological station with satellite imagery and modeled output. This approach proved to be a useful tool for short range (12 h) minimum temperature prediction by generating thermal images over the Central Valley of Chile. It may also be used as a tool for frost risk assessment, in order to adapt production to local climatological conditions.
Van Leeuwen, W.J.D., Huete, A.R., Walthall, C.L., Prince, S.D., Bgu, A. & Roujean, J.L. 1997, 'Deconvolution of remotely sensed spectral mixtures for retrieval of LAI, fAPAR and soil brightness', Journal of Hydrology, vol. 188-189, no. 1-4, pp. 697-724.
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Linear mixture models have been used to invert spectral reflectances of targets at the Earth's surface into proportions of plant and soil components. However, operational use of mixture models has been limited by a lack of biophysical interpretation of the results. The main objectives of this study were (1) to relate the deconvolved components of mixture model with biophysical properties of vegetation and soil at the surface and (2) to apply the mixture model results to remotely sensed imagery. A radiative transfer model (SAIL: Scattering by Arbitrarily Inclined Leaves) was used to generate reflectance 'mixtures' from leaf and bare soil spectral measurements made at HAPEX-Sahel (Hydrological Atmospheric Pilot EXperiment) study sites. The SAIL model was used to create canopy reflectances and fractions of absorbed photosynthetically active radiation (fAPAR) for a range of mixed targets with varying leaf area index (LAI) and soils. A spectral mixture model was used to deconvolve the simulated reflectance data into component fractions, which were then calibrated to the SAIL-generated LAI, fAPAR and soil brightness. The calibrated relationships were validated with observational ground data (LAI, fAPAR and reflectance) measured at the HAPEX Sahel fallow bush/grassland, fallow grassland and millet sites. Both the vegetation and soil component fractions were found to be dependent upon soil background brightness, such that inclusion of the soil fraction information significantly improved the derivation of vegetation biophysical parameters. Soil brightness was also shown to be a useful parameter to infer soil properties. The deconvolution methodology was then applied to a nadir image of a HAPEX-Sahel site measured by the Advanced Solid State Array Spectroradiometer (ASAS). Site LAI and fAPAR were successfully estimated by combining the fractional estimates of vegetation and soils, obtained through deconvolution of the ASAS image, with the calibrated relationships between vegetation fraction, LAI and fAPAR, obtained from the SAIL data.
Huete, A. 1996, 'Extension of soil spectra to the satellite: Atmosphere, geometric, and sensor considerations', Photo Interpretation: images aeriennes et spatiales, vol. 2, no. 1, pp. 101-118.
Satellite-based remote sensing is an invaluable tool for soil studies related to biogeochemical and carbon cycling, agriculture production, erosion and sediment yield, water balance, and trace gas models. Satellites offer repetitious, synoptic coverage which allow for the extension oflocal (indigenous) knowledge of soils to regional and global scales. Surface processes related to land-use and climate changes can be easily monitored at varying temporal and spatial scales and directional measurements of reflected energy offer clues regarding soil roughness, useful in energy balance fonnulalions as well as in soil erosion and compaction studies.
Bannari, A., Huete, A., Morin, D. & Zagolski, F. 1996, 'Effects de la couleur et de la brillance du sol les indices de vegetation', International journal of remote sensing, vol. 17, no. 10, pp. 1885-1906.
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Variations in the spectral properties of soils related to their colour and brightness considerably influence the detection of sparse vegetation in heterogeneous environments using vegetation indices. During the last decade, a new generation of vegetation indices (NDVI, PVI, SAVI, MSAVI, TSAVI, TSARVI, ARVI, GEMI, and AVI) was developed in order to minimize these effects. To evaluate the sensitivity of these indices to soil colour and brightness and to test their potential for a more precise description of the vegetation cover for different cover rates, a number of simulations were carried out using a first order radiative transfer model. The model was adapted for studying directly the contribution of the optical properties of bare soils on the vegetation index. The results show that the first order radiative transfer model constitutes a valuable tool for analysing and understanding the interactions between the electromagnetic radiation, the vegetation cover and bare soil. It makes it possible to analyse the effect of colour and brightness on the reflectance factor and, consequently, on the vegetation index. The GEMI, AVI, NDVI, ARVI and PVI indices show lower performance for the management of sparse or moderately dense vegetation environments. They are marked by non-negligible errors related to the optical properties of bare soils. The AVI leads to results that do not reflect the theoretical behaviour of vegetation indices. As to the TSAVI, TSARVI, SAVI and MSAVI indices, they are more resistant to changes in the optical properties of soils and permit better discrimination between the vegetation from the bare soil background in an heterogeneous and relatively complex environment.
Van Leeuwen, W.J.D. & Huete, A.R. 1996, 'Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices', Remote Sensing of Environment, vol. 55, no. 2, pp. 123-138.
Litter is frequently present within vegetation canopies and thus contributes to the overall spectral response of a canopy. Consequently, litter will affect spectral indices designed to be sensitive to green vegetation, soil brightness or other features. The main objectives of the current research were to 1) evaluate the spectral properties of green vegetation and litter and 2) quantify the effect of standing litter on the performance of spectral indices. The SAIL (scattering by arbitrarily inclined leaves) model was used to generate canopy reflectance "mixtures" and to estimate fractions of absorbed photosynthetically active radiation (fAPAR) with varying leaf area index (LAI), soil background, combinations of vegetation component spectral properties, and one or two horizontal vegetation layers. Spectral measurements of different bare soils and mature green and senescent leaves of representative plant species at the HAPEX-Sahel (Hydrological Atmospheric Pilot Experiment) study sites were used as input. The normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and the modified NDVI (MNDVI) and mixture model spectral indices were selected to evaluate their performance with respect to standing litter and green vegetation mixtures. Spectral reflectance signatures of leaf litter varied significantly, but strongly resembled soil spectral characteristics. The biophysical parameters (LAI, fAPAR), derived from spectral vegetation indices, tended to be overestimated for randomly distributed, sparse green and litter vegetation cover mixtures, and underestimated for randomly distributed dense green and litter vegetation cover mixtures. All spectral indices and their biophysical interpretation were significantly altered by variability in 1) green leaf, leaf litter, and bark optical properties, 2) the amount and position of standing leaf litter, 3) leaf angle distribution, and 4) soil background. The NDVI response to these variables was inconsistent, and was the most affected by litter. The spectral mixture model indices, designed to be sensitive to litter, were shown to be promising for the identification of litter present among different ecosystems.
Bgu, A., Roujean, J.L., Hanan, N.P., Prince, S.D., Thawley, M., Huete, A. & Tanr, D. 1996, 'Shortwave radiation budget of Sahelian vegetation: 1. Techniques of measurement and results during HAPEX-Sahel', Agricultural and Forest Meteorology, vol. 79, no. 1-2, pp. 79-96.
Shortwave radiative budgets of Sahelian savannas and a millet crop were measured during the 1992 HAPEX-Sahel field experiment, in Niger, West Africa. Measurements were conducted on four land cover types: shrub fallow, grass fallow, degraded shrubland, and a millet field. Each land unit was equipped with sets of sensors to measure the photosynthetically active radiation (PAR) and near-infrared (NIR) radiative fluxes within the canopies, and were operated throughout the entire growing season. Daily fractional PAR and NIR interception by vegetation was rather low (less than 60% and 30% for natural vegetation and crop, respectively). The sparse vegetation and bright sandy soils meant that the PAR absorption and interception were similar (they were equal at a value of approximately 20%). The albedo of the plots varied little diurnally and seasonally, and was strongly affected by the reflection from the soil. The interception and absorption and, to a lesser degree, the albedo exhibited distinct directional effects related to solar zenith angle. 1996 Elsevier Science B.V. All rights reserved.
Bannari, A., Huete, A.R., Morin, D. & Zagolski, F. 1996, 'Effects of the colour and brightness of the Sun on vegetation indices', International Journal of Remote Sensing, vol. 17, no. 10, pp. 1885-1906.
During the last decade, a new generation of vegetation indics (NDVI, PVI, SAVI, MSAVI, TSAVI, TSARVI, ARVI, GEMI, and AVI) was developed. To evaluate the sensitivity of these indices to soil colour and brightness and to test their potential for a more precise description of the vegetation cover for different cover rates, a number of simulations were carried out using a first order radiative transfer model. The model was adapted for studying directly the contribution of the optical properties of bare soils on the vegetation index. The GEMI, AVI, NDVI, ARVI and PVI indices show lower performance for the management of sparse or moderately dense vegetation environments. They are marked by non-negligible errors related to the optical properties of bare soils. The AVI leads to results that do not reflect the theoretical behaviour of vegetation indices. The TSAVI, TSARVI, SAVI and MSAVI indices are more resistant to changes in the optical properties of soils and permit better discrimination between the vegetation from bare soil background.
Huete, A.R. 1996, 'Extension of soil spectra to the satellite: atmosphere, geometric, and sensor considerations', Photo Interpretation: Images Aeriennes et Spatiales, vol. 34, no. 2, pp. 101-118.
Analyzes the relationships between field-based and space-based soil observations taking into account sensor wavebands, atmosphere, and sun-target-sensor geometric effects. Also the use of atmospheric correction and bidirectional reflectance models in the interpretation of the satellite signal received by major space- and airborne sensor systems. Fine and coarse spectra are utilized from such sensors as the Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS); Satellite pour l'Observation de la Terre (SPOT); and the aircraft-based Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Advanced Solid-state Array Spectroradiometer (ASAS). Future sensor systems such as SPOT-VEGETATION, Landsat 7, and the Earth Observing System (EOS) sensors are also discussed. There are abridged French and Spanish versions.
LIU, H. & HUETE, A. 1995, 'A FEEDBACK BASED MODIFICATION OF THE NDVI TO MINIMIZE CANOPY BACKGROUND AND ATMOSPHERIC NOISE', IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 33, no. 2, pp. 457-465.
Epiphanio, J.C. & Huete, A.R. 1995, 'Dependence of NDVI and SAVI on sun/sensor geometry and its effect on fAPAR relationships in alfalfa', Remote Sensing of Environment, vol. 51, no. 3, pp. 351-360.
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Bannari, A., Morin, D., Bonn, F. & Huete, A.R. 1995, 'A review of vegetation indices', Remote Sensing Reviews, vol. 13, no. 1-2, pp. 95-120.
In the field of remote sensing applications, scientists have developed vegetation indices (VI) for qualitatively and quantitatively evaluating vegetative covers using spectral measurements. The spectral response of vegetated areas presents a complex mixture of vegetation, soil brightness, environmental effects, shadow, soil color and moisture. Moreover, the VI is affected by spatial-temporal variations of the atmosphere. Over forty vegetation indices have been developed during the last two decades in order to enhance vegetation response and minimize the effects of the factors described above. This paper summarizes, refers and discusses most of the vegetation indices found in the literature. It presents different existing classifications of indices and proposes to group them in a new classification. -Authors
LIU, H. & HUETE, A. 1995, 'A FEEDBACK BASED MODIFICATION OF THE NDVI TO MINIMIZE CANOPY BACKGROUND AND ATMOSPHERIC NOISE (VOL 33, PG 457, 1995)', IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 33, no. 3, pp. 814-814.
Huete, A. & Liu, H.Q. 1994, 'An Error and Sensitivity Analysis of the Atmospheric- and Soil-Correcting Variants of the NDVI for the MODIS-EOS', Invalid Code, vol. 32, no. 4, pp. 897-905.
This publication focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information. This journal publishes technical papers disclosing new and significant research.
van Leeuwen, W.J.D., Huete, A.R., Duncan, J. & Franklin, J. 1994, 'Radiative transfer in shrub savanna sites in Niger: preliminary results from HAPEX-Sahel. 3. Optical dynamics and vegetation index sensitivity to biomass and plant cover', Agricultural and Forest Meteorology, vol. 69, no. 3-4, pp. 267-288.
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A shrub savannah landscape in Niger was optically characterized utilizing blue, green, red and near-infrared wavelengths. Selected vegetation indices were evaluated for their performance and sensitivity to describe the complex Sahelian soil/vegetation canopies. Bidirectional reflectance factors (BRF) of plants and soils were measured at several view angles, and used as input to various vegetation indices. Both soil and vegetation targets had strong anisotropic reflectance properties, rendering all vegetation index (VI) responses to be a direct function of sun and view geometry. Soil background influences were shown to alter the response of most vegetation indices. N-space greenness had the smallest dynamic range in VI response, but the n-space brightness index provided additional useful information. The global environmental monitoring index (GEMI) showed a large VI dynamic range for bare soils, which was undesirable for a vegetation index. The view angle response of the normalized difference vegetation index (NDVI), atmosphere resistant vegetation index (ARVI) and soil atmosphere resistant vegetation index (SARVI) were asymmetric about nadir for multiple view angles, and were, except for the SARVI, altered seriously by soil moisture and/or soil brightness effects. The soil adjusted vegetation index (SAVI) was least affected by surface soil moisture and was symmetric about nadir for grass vegetation covers. Overall the SAVI, SARVI and the n-space vegetation index performed best under all adverse conditions and were recommended to monitor vegetation growth in the sparsely vegetated Sahelian zone. 1994.
Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. & Sorooshian, S. 1994, 'A modified soil adjusted vegetation index', Remote Sensing of Environment, vol. 48, no. 2, pp. 119-126.
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There is currently a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science and global change. Spectral models and indices are being developed to improve vegetation sensitivity by accounting for atmosphere and soil effects. The soil-adjusted vegetation index (SAVI) was developed to minimize soil influences on canopy spectra by incorporating a soil adjustment factor L into the denominator of the normalized difference vegetation index (NDVI) equation. For optimal adjustment of the soil effect, however, the L factor should vary inversely with the amount of vegetation present. A modified SAVI (MSAVI) that replaces the constant L in the SAVI equation with a variable L function is presented in this article. The L function may be derived by induction or by using the product of the NDVI and weighted difference vegetation index (WDVI). Results based on ground and aircraft-measured cotton canopies are presented. The MSAVI is shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a 'vegetation signal' to 'soil noise' ratio.
Running, S.W., Justice, C.O., Salomonson, V.V., Hall, D., Barker, J., Kaufmann, Y.J., Strahler, A.H., Huete, A., Muller, J.P., Vanderbilt, V., Wan, Z.M., Teillet, P. & Carneggie, D. 1994, 'Terrestrial remote sensing science and algorithms planned for EOS/MODIS', International journal of remote sensing, vol. 15, no. 17, pp. 3587-3620.
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The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0415-14235 ?m wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these variables will allow accurate surface change detection, a fundamental determinant of global change.
Huete, A., Justice, C. & Liu, H. 1994, 'Development of vegetation and soil indices for MODIS-EOS', Remote Sensing of Environment, vol. 49, no. 3, pp. 224-234.
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One of the primary interests of the NASA Earth Observing System (EOS) program is to study the role of vegetation in biospheric processes. Currently, the normalized difference vegetation index (NDVI) is utilized to study and monitor vegetation activity on an operational basis at global scales. Recently developed variants to the NDVI equation, based on improved knowledge of atmosphere, canopy background, and sensor-view geometry, are being considered for use with the EOS Moderate-Resolution Imaging Spectrometer (MODIS) sensor. A set of criteria is established to evaluate their performance with respect to the vegetation signal and atmospheric and soil sources of noise. These include the vegetation signal-to-noise ratio (S/N), % relative error, and vegetation equivalent noise (VEN), which form the basis for the evaluation and comparison of vegetation indices (VIs) across a wide range in vegetation covers. The NDVI variant equations outperformed the NDVI by minimizing atmospheric and/or soil background sources of contamination as well as increasing vegetation signal sensitivity. Preliminary estimates, based on the data sets tested here, indicate an overall level of uncertainty of 0.4 LAI (? 15% cover) with the use of the soil adjusted and atmospherically resistant vegetation index (SARVI), compared with 0.8 LAI (30% cover) for the NDVI. Eventually, the final evaluation and validation of VIs for MODIS will include a field campaign strategy, simulation studies, and a worldwide establishment of test sites that collectively cover a diverse range of biomes. Lastly, the retrieval of canopy biophysical information from these VIs are discussed. 1994.
Franklin, J., Duncan, J., Huete, A.R., van Leeuwen, W.J.D., Li, X. & Bgu, A. 1994, 'Radiative transfer in shrub savanna sites in Niger: preliminary results from HAPEX-Sahel. 1. Modelling surface reflectance using a geometric-optical approach', Agricultural and Forest Meteorology, vol. 69, no. 3-4, pp. 223-245.
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To use optical remote sensing to monitor land surface-climate interactions over large areas, algorithms must be developed to relate multispectral measurements to key variables controlling the exchange of matter (water, carbon dioxide) and energy between the land surface and the atmosphere. The proportion of the ground covered by vegetation and the interception of photosynthetically active radiation (PAR) by vegetation are examples of two variables related to evapotranspiration and primary production, respectively. An areal-proportion model of the multispectral reflectance of shrub savanna, composed of scattered shrubs with a grass, forb or soil understory, predicted the reflectance of two 0.5 km2 sites as the area-weighted average of the shrub and understory or 'background' reflectances. Although the shaded crown and shaded background have darker reflectances, ignoring them in the area-weighted model is not serious when shrub cover is low and solar zenith angle is small. A submodel predicted the reflectance of the shrub crown as a function of the foliage reflectance and amount of plant material within the crown, and the background reflectance scattered or transmitted through canopy gaps (referred to as a soil-plant 'spectral interaction' term). One may be able to combine these two models to estimate both the fraction of vegetation cover and interception of PAR by green vegetation in a shrub savanna. 1994.
Qi, J., Huete, A.R., Cabot, F. & Chehbouni, A. 1994, 'Bidirectional properties and utilizations of high-resolution spectra from a semiarid watershed', Water Resources Research, vol. 30, no. 5, pp. 1271-1279.
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A ground- and air-based high spectral resolution data set was collected during the summer Monsoon '90 experiment at the Walnut Gulch experimental watershed in southeastern Arizona for the purpose of (1) characterizing solar and view angle interactions on dry and wet season canopy spectra, and (2) exploring the use of multidirectional measurements to infer vegetation properties for semiarid watershed studies. Bidirectional reflectance factors were measured up to 40 off nadir with a spectroradiometer over a semidesert grassland site. High-spectral resolution aircraft data were collected over grass and desert shrub sites in order to investigate scaling effects. -from Authors
Pinker, R.T., Kustas, W.P., Laszlo, I., Moran, M.S. & Huete, A.R. 1994, 'Basin-scale solar irradiance estimates in semiarid regions using GOES 7', Water Resources Research, vol. 30, no. 5, pp. 1375-1386.
Because evapotranspiration is strongly controlled by absorbed solar radiation, an attempt has been made here to evaluate the accuracy at which the downward shortwave irradiance (SW ?) can be estimated from satellites on basin scale. It was demonstrated that even under highly variable cloud conditions, satellite estimates of SW ? daily means were within 10% of measured values, while 5-day means were within 3% of measured values. -from Authors
Chehbouni, A., Kerr, Y.H., Qi, J., Huete, A.R. & Sorooshian, S. 1994, 'Toward the development of a multidirectional vegetation index', Water Resources Research, vol. 30, no. 5, pp. 1281-1286.
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Qi, J., Huete, A.R., Moran, M.S., Chehbouni, A. & Jackson, R.D. 1993, 'Interpretation of vegetation indices derived from multi-temporal SPOT images', Remote Sensing of Environment, vol. 44, no. 1, pp. 89-101.
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A temporal sequence of 15 SPOT HRV images and low altitude aircraft reflectance data were obtained over soil and cotton fields at the Maricopa Agricultural Center, Arizona from April to October 1989. The SPOT data included different view angles (- 28 - + 24) while the aircraft data were obtained with a nadir-viewing radiometer equipped with SPOT filters. View angle/direction, atmosphere, and soil influences on individual band and vegetation indices were observed in the SPOT data. The relative magnitude among the three influences was dependent on surface conditions, varied with canopy growth, and was different for red and near-infrared (NIR) reflectances and vegetation indices (VIs). View angle effects were most pronounced in red and NIR reflectance data and were secondary with the use of VIs. View angle variations, and to some extent soil variations, influenced VI responses from partial canopies while relative atmospheric influences became most dominant at higher densities of vegetation. The results suggest that the current compositing routine for temporal vegetation index imagery may not be entirely adequate. 1993.
Huete, A.R., Hua, G., Qi, J., Chehbouni, A. & van Leeuwen, W.J.D. 1992, 'Normalization of multidirectional red and NIR reflectances with the SAVI', Remote Sensing of Environment, vol. 41, no. 2-3, pp. 143-154.
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Directional reflectance measurements were made over a semidesert gramma (Bouteloua spp.) grassland at various times of the growing season. Azimuthal strings of view angle measurements from + 40 to - 40 were made for various solar zenith angles and soil moisture conditions. The sensitivity of the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) to these bidirectional measurements was assessed for purposes of improving remote temporal monitoring of vegetation activity. The NDVI response from the grassland canopy was strongly anisotropic about nadir view angles while the SAVI response was symmetric about nadir. This occurred for all sun angles, soil moisture condition, and grass densities. This enabled variations in SAVI-view angle response to be minimized with a cosine function. It is expected that this study will aid in improving the characterization of vegetation temporal activity from Landsat TM, SPOT, AVHRR, and the Earth Observing System MODIS sensor. 1992.
Huete, A.R. & Escadafal, R. 1991, 'Assessment of biophysical soil properties through spectral decomposition techniques', Remote Sensing of Environment, vol. 35, no. 2-3, pp. 149-159.
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A mixture model was utilized to extract soil biophysical properties from fine resolution soil spectra (400-900 nm) measured outdoors with a portable spectroradiometer. The objective of this study was to fully characterize soil spectral signatures in the visible and near-infrared in terms of underlying "basis" curves, key wavelengths, and dimensionality. Through spectral decomposition and mixture modeling, the reflectance response of a wide, genetic range of soil materials were separated into four independent sources of spectral variability (basis curves), which in linear combination were able to reconstitute the experimental data set. Stepwise spectral reconstruction was then utilized to isolate organic carbon and free iron oxide "basis" curves. This enabled a good "global" measure of soil properties irrespective of soil type or brightness. We anticipate the EOS-MODIS and HIRIS sensors to provide the spectral data needed for inversion of satellite data into soil surface properties and processes. 1991.
Kustas, W.P., Goodrich, D.C., Moran, M.S., Amer, S.A., Bach, L.B., Blanford, J.H., Chehbouni, A., Claassen, H., Clements, W.E., Doraiswamy, P.C., Dubois, P., Clarke, T.R., Daughtry, C.S., Gellman, D.I., Grant, T.A., Hipps, L.E., Huete, A., Humes, K.S., Jackson, T.J., Keefer, T.O., Nichols, W.D., Parry, R., Perry, E.M., Pinker, R.T., Pinter, J.P.J., Qi, J., Riggs, A.C., Schmugge, T.J., Shutko, A.M., Stannard, D.I., Swiatek, E., van, L.J.D., Zyl, J., Vidal, A., Washburne, J. & Weltz, M.A. 1991, 'An Interdisciplinary field study of the energy and water fluxes in the atmosphere-biosphere system over semiarid rangelands: Description and some preliminary results', Bulletin American Meteorological society, vol. 72, no. 11, pp. 1683-1705.
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Arid and semiarid rangelands comprise a significant portion of the earth's land surface. Yet little is known about the effects of temporal and spatial changes in surface soil moisture on the hydrologic cycle, energy balance, and the feedbacks to the atmosphere via thermal forcing over such environments. Understanding this interrelationship is crucial for evaluating the role of the hydrologic cycle in surface-atmosphere interactions. This study focuses on the utility of remote sensing to provide measurements of surface soil moisture, surface albedo, vegetation biomass, and temperature at different spatial and temporal scales. Remote-sensing measurements may provide the only practical means of estimating some of the more important factors controlling land surface processes over large areas. Consequently, the use of remotely sensed information in biophysical and geophysical models greatly enhances their ability to compute fluxes at catchment and regional scales on a routine basis. However, model calculations for different climates and ecosystems need verification. This requires that the remotely sensed data and model computations be evaluated with ground-truth data collected at the same areas scales. The present study (MONSOON 90) attempts to address this issue for semiarid rangelands. The experimental plan included remotely sensed data in the visible, near-infrared, thermal, and microwave wavelengths from ground and aircraft platforms and, when available, from satellites. Collected concurrently were ground measurements of soil moisture and temperature, energy and water fluxes, and profile data in the atmospheric boundary layer in a hydrologically instrumented semiarid rangeland watershed. Field experiments were conducted in 1990 during the dry and wet or monsoon season for the southwestern United States. A detailed description of the field campaigns, including measurements and some preliminary results are given.
Huete, A.R. & Tucker, C.J. 1991, 'Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery', International Journal of Remote Sensing, vol. 12, no. 6, pp. 1223-1242.
The effects of soil optical properties on vegetation index imagery are analysed with ground-based spectral measurements and both simulated and actual AVHRR data from the NOAA satellites. Soil effects on vegetation indices were divided into primary variations associated with the brightness of bare soils, secondary variations attributed to "colour' differences among bare soils, and soil-vegetation spectral mixing. Primary variations were attributed to shifts in the soil line owing to atmosphere or soil composition. Secondary soil variance was responsible for the Saharan desert "artefact' areas of increased vegetation index response in AVHRR imagery. The impact of soil effects is discussed with a transect of vegetation index data derived from NOAA data from desert to equatorial forest. -Authors
Jackson, R.D. & Huete, A.R. 1991, 'Interpreting vegetation indices', Preventive Veterinary Medicine, vol. 11, no. 3-4, pp. 185-200.
Remotely sensed spectral vegetation indices are widely used and have benefited numerous disciplines interested in the assessment of biomass, water use, plant stress, plant health and crop production. The successful use of these indices requires knowledge of the units of the input variables used to form the indices, and an understanding of the manner in which the external environment and the architectural aspects of a vegetation canopy influence and alter the computed index values. Although vegetation indices were developed to extract the plant signal only, the soil background, moisture condition, solar zenith angle, view angle, as well as the atmosphere, alter the index values in complex ways. The nature of these problems are explored both in an empirical and in a theoretical sense, and suggestions are offered for the effective use and interpretation of vegetation indices. 1991 Elsevier Science Publishers B.V. All rights reserved.
Escadafal, R. & Huete, A. 1991, 'Improvement in remote sensing of low vegetation cover in arid regions by correcting vegetation indices for soil "noise'', Comptes Rendus - Academie des Sciences, Serie II, vol. 312, no. 11, pp. 1385-1391.
The variations of near-infrared/red reflectance ratios of ten arid soil samples were correlated with a "redness index' computed from red and green spectral bands. These variations have been shown to limit the performances of vegetation indices (NDVI and soil adjusted VI) in discriminating low vegetation covers. The redness index is used to adjust for this "soil noise'. The "noise-corrected' SAVI was able to assess vegetation amounts with an error four times smaller than the uncorrected NDVI. These is an abridged English version. -from English summary
Huete, A.R. & Warrick, A.W. 1990, 'Assessment of vegetation and soil water regimes in partial canopies with optical remotely sensed data', Remote Sensing of Environment, vol. 32, no. 2-3, pp. 155-167.
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The optical properties of a partially vegetated cotton field with spatially and temporally dynamic soil water conditions were analyzed with coincident aircraft and satellite data. The study was conducted at the Maricopa Agricultural Center in Arizona during June 1988. The dynamics of soil surface drying made it difficult to evaluate plant cover and assess soil condition with combined thermal, brightness, and normalized difference vegetation index (NDVI) spectral parameters as all three parameters were sensitive to the three stages of soil drying. A soil-adjusted vegetation index (SAVI) minimized both spatial and temporal variations in soil spectral behavior and was found useful in vegetation analysis and in further qualitative assessment of soil condition. Due to the complex dynamics of soil surface drying and variability in soil properties, soil water content at the surface (0 - 5 cm) could not be determined with the Thematic Mapper moisture bands, or with the various "wetness" indicators. 1990.
Levitt, D.G., Simpson, J.R. & Huete, A.R. 1990, 'Estimates of surface soil water content using linear combinations of spectral wavebands', Theoretical and Applied Climatology, vol. 42, no. 4, pp. 245-252.
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Surface reflectance factors from bare field soil were measured to determine the relationship between surface soil water content and spectral reflectance. Reflectance in the six reflective Thematic Mapper (TM) bands plus a 1.15 to 1.30 ?m bandpass (referred to as MMR 5) was measured using a groundbased radiometer across a soil water gradient provided by a line source sprinkler system. A spectral index of soil brightness (Brightness) derived using the Gram-Schmidt process and utilizing reflectance information was calculated for each band and for combinations of bands. The results of this study show that TM band 7 (2.05-2.30 ?m) provided improved estimates of surface soil water content (0-0.5 cm depth) over estimates using reflectance information from all seven bands. Good correlations were also found between band ratio spectral indices of TM 5 (1.55-1.75 ?m) / TM 7, MMR 5/TM 7, and MMR 5/TM 5 and surface soil water content. Results indicate that surface reflectance factors within bandpasses that partially overlap water absorption regions, such as TM 7, are most highly correlated with surface soil moisture. Band ratios utilizing a bandpass partially overlapping a water absorption region such as TM 7 and a non-water-absorbing bandpass such as MMR 5 yield close correlations with surface soil water content. 1990 Springer-Verlag.
Huete, A.R. 1988, 'A soil-adjusted vegetation index (SAVI)', Remote Sensing of Environment, vol. 25, no. 3, pp. 295-309.
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A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton (Gossypium hirsutum L. var DPI-70) and range grass (Eragrostics lehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple lobal" that can describe dynamic soil-vegetation systems from remotely sensed data. 1988.
Huete, A.R. & Jackson, R.D. 1988, 'Soil and atmosphere influences on the spectra of partial canopies', Remote Sensing of Environment, vol. 25, no. 1, pp. 89-105.
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An atmospheric radiant transfer model was used to compare ground-measured radiances over partially vegetated canopies with their simulated responses at the top of a clear (100 km meteorological range) and a turbid (10 km) atmosphere. Radiance measurements in the first four bands of the Thematic Mapper were taken over incomplete cotton (Gossypium hirsutum L.) and Lehmann lovegrass (Eragrostis lehmanniana Nees) canopies with different soil backgrounds separately inserted underneath. Atmospheric influences on the spectra of partial canopies were found to be significantly dependent on the "brightness" of the underlying soil. The change in canopy red and near-infrared radiance between the ground and the top of the atmosphere was such that an increase, decrease, or no change could be observed, depending on the magnitude of the canopy substrate contribution. Both increasing soil "brightness" and atmospheric turbidity lowered the ratio (RVI) and normalized difference vegetation index values (NDVI). Consequently, atmospheric-induced RVI and NDVI degradation were greatest over canopies with darker soils and were not detectable over canopies with light-colored soils. In contrast, soil and atmospheric effects on the perpendicular vegetation index were independent with atmosphere degradation being similar across all soil backgrounds. Soil influences on vegetation indices from partial canopies were found to be of similar magnitude to those attributed to the atmosphere for the range of soil and atmosphere conditions examined here. 1988.
Huete, A. 1987, 'Soil-dependent spectral response in a developing plant canopy', Agronomy Journal, vol. 79, no. 1, pp. 31-68.
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Amajor problem in the use of remote sensing techniques to assess plant biomass and condition over incomplete canopies concerns the soil background contribution toward measured spectral response. An understanding of this soil signal is essential 10 better relate canopy spectra with plant properties. An interactive, plant-soil radiant flux model was developed to separate spectral variations associated with soil background from those attrlbutable to vegetation. Field measured spectra taken over It developing cotton (Gossypinm hirsutum L.) canopy with four soil types (Cumnlic Cryoboroll, Typic TorriHuvent, Ustollic Haplargid, and Typic Calciorthid) alternately inserted underneath were decomposed into soil and vegetation spectra by utilizing the model in It principal component analysis. The soil component included all radiation penetrating the canopy and inter~ acting with the underlying soil. The vegetation compOnent repre seoted all radiation reflected directly from the plant tover with no soil interaction. The soil component was found to resemble the spectral response of green vegetation due to the scattering and trans-mittance properties of the overlying plant canopy. Results show how the soil signal mixes into various vegetation indices inhibiting reliable vegetation discrimination. The potential improvements in veg etation analysis that can result from filtering soil background response from planHanopy spectra are also discussed.
Huete, A.R. & Jackson, R.D. 1987, 'Suitability of spectral indices for evaluating vegetation characteristics on arid rangelands', Remote Sensing of Environment, vol. 23, no. 2.
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The spectral behavior of an arid, Lehmann lovegrass (Eragrostis lehmanniana), range canopy with varying quantities of live, green grass, senesced, yellow grass, weathered, gray litter, and different soil backgrounds was analyzed with a ground based radiometer. The analysis included rangeland field plots and artificial mixtures of live and dead grass. Senesced grass and weathered litter were found to significantly alter the spectral response of the range canopy in the first four Thematic Mapper wavebands (0.45-0.52; 0.52-0.60; 0.63-0.69; 0.76-0.90 ?m). These influences seriously hampered the utility of spectral vegetation indices in assessing green phytomass levels. Gray litter lowered the response of the green vegetation index (GVI) and perpendicular vegetation index (PVI) while minimally influencing the ratio vegetation index (RVI) and the normalized difference vegetation index (NDVI). Yellow, senesced grass increased the greenness response of plots without green vegetation and decreased the greenness response of plots with green vegetation. Higher reflecting soils increased the GVI and PVI response and decreased the RVI and NDVI response of comparable range canopy mixtures. Small amounts of 30 cm tall, green grass (750 kg/ha) could not be detected within a 75 cm tall, senesced grass stand (5000 kg/ha). The results of this study show spectral vegetation indices to be unreliable measures of green phytomass in arid rangelands. A mixture model employing principal component analysis was used to extract a green vegetation signal, but green phytomass detection was not improved. Apparently, the green vegetation signal emerging from range canopies is diminished by the scattering influences of the vertically oriented elements of the senesced grass phytomass. 1987.
Huete, A.R. 1987, 'Soil and sun angle interactions on partial canopy spectra.', International Journal of Remote Sensing, vol. 8, no. 9, pp. 1307-1317.
The spectral behaviour of an incomplete cotton canopy was analysed in relation to solar zenith angle and soil background variations. Soil and vegetation spectral contributions towards canopy response were separated using a first-order interactive model and consequently used to compare the relative sensitivity of canopy spectra to soil background and solar angle differences. Canopy reflectance behaviour with solar angle increased, decreased or remained invariant depending on the reflectance properties of the underlying soil. Sunlit and shaded soil contributions were found to alter vegetation index behaviour significantly over different Sun angles. -Author
Post, D.F., Huete, A.R. & Pease, D.S. 1986, 'A comparison of soil scientist estimations and laboratory determinations of some Arizona soil properties.', Journal of Soil & Water Conservation, vol. 41, no. 6, pp. 421-424.
Samples of representative Arizona soils were sent to 36 soil scientists who were asked to estimate the sand, silt, and clay percentage; organic matter percentage; cation exchange capacity; carbonate content; and wilting point of each soil. These estimates were then correlated with laboratory analyses of these soils. The mean correlation coefficients for the individual estimations were highest for sand and clay (.88 and .86) and lowest for percent carbonates and cation exchange capacity (.56 and .65). -from Authors
Huete, A.R. 1986, 'Separation of soil-plant spectral mixtures by factor analysis', Remote Sensing of Environment, vol. 19, no. 3, pp. 237-251.
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A factor-analytic inversion model is presented which enables a data set of spectral mixtures to be decomposed into the sum of unique reflecting components weighted by their corresponding amounts. Spectral mixtures are decomposed into abstract eigenspectra and eigenvector matrices. The eigenspectra are then transformed into pure component spectral signatures through a target testing procedure that allows one to individually search for the presence of ground reflecting features. Soil-plant mixtures with variable soil moisture and plant densities were successfully decomposed into dry soil, wet soil, and vegetation components and their respective amounts in all spectral mixtures were determined. Potential uses of factor analysis in soil identification and biomass assessment are discussed. 1986.
Huete, A.R., Jackson, R.D. & Post, D.F. 1985, 'Spectral response of a plant canopy with different soil backgrounds', Remote Sensing of Environment, vol. 17, no. 1, pp. 37-53.
The spectral behavior of a cotton canopy with four soil types alternately inserted underneath was examined at various levels of vegetation density. Measured composite spectra, representing various mixtures of vegetation with different soil backgrounds, were compared with existing measures of greenness, including the NIR-red band ratios, the perpendicular vegetation index (PVI), and the greenness vegetation index (GVI). Observed spectral patterns involving constant vegetation amounts with different soil backgrounds could not be explained nor predicted by either the ratio or the orthogonal greenness measures. All greenness measures were found to be strongly dependent on soil brightness. Furthermore, soil-induced greenness changes became greater with increasing amounts of vegetation up to 60% green cover. The results presented suggests that soil and plant spectra interactively mix in a nonadditive, partly correlated manner to produce composite canopy spectra. 1985.
Huete, A.R. & McColl, J.G. 1984, 'Soil cation leaching by 'acid rain' with varying nitrate-to-sulfate ratios', Journal of Environmental Quality, vol. 13, no. 3, pp. 366-371.
The influence of the anion composition of simulated 'acid rain' on cation leaching of three soils with different surface-charge properties was examined. Four acid mixtures of H2SO4 and HNO3 all with pH 3.5, but with varying NO3-/SO42- mole ratios of 1.00:0.00, 0.75:0.25, 0.55:0.45, and 0.00:1.00, were used to leach an Ultic-Alfisol, an Oxisol, and an Entisol. The taxonomic names of these three soils are (1) Cornutt series: fine, mixed, mesic Ultic Haploxeralfs, (2) unnamed Rhodustox, and (3) Hanford series: coarse-loamy, mixed, nonacid, thermic Typic Xerorthents. The Alfisol had a high SO42- adsorption capacity because of its high Fe2O3 content of 12 g kg-1 and high point-of-zero charge (PZC) of 6.0. The Oxisol, although strongly weathered, had a lower Fe2O3 content of 5 g kg-1 and PZC of 4.5. The Entisol was relatively unweathered soil derived from silicaceous alluvium, with even less Fe2O3 of 3 g kg-1 and a lower PZC of 3.5, and represented a soil of fixed charge. Cation leaching of the Alfisol varied directly with the NO3- content of the leaching input due to the higher mobility of NO3- compared with SO42- that was adsorbed. The relative NO3-/SO42- contents of inputs had no effects on cation leaching of the Entisol. Effects on leaching of the Oxisol were intermediate between those of the Alfisol and Entisol. It was clearly demonstrated that the anion composition of 'acid rain' plays a significant role in the cation leaching of soils with amphoteric-charge properties, which are able to adsorb SO42-. Some practical implications are also dicussed.
Miller, T.E., Wing, J.S. & Huete, A. 1984, 'The agricultural potential of selected C4 plants in arid environments', Journal Of Arid Environments, vol. 7, no. 1, pp. 275-286.
Due to the increasing amount of dry lands in the world, C4 plants which are highly efficient in water use need to be investigated as potential crop species. This study examined the arid-land agricultural potential ofseveral specific C4 species: Amaramhus hypochondriacus, Amaranthus retrOjlexus, and Portulaca oleracea. Germination responses of each species to both temperature and moisture were determined in growth chambers and contrasted with the response of two C3 species, Beta vulgaris var. cida and Chenopodium album. The growth response of each species [Q temperature, water availability, and soil salinity was investigated in a series of greenhouse experiments in Tucson, Arizona. The nutritional qualities of both seeds and vegetative matter (per cent moisture, fat, protein, fiber, ash, and amino acid composition) were detennined for the experimental species.
Huete, A.R., Post, D.F. & Jackson, R.D. 1984, 'Soil spectral effects on 4-space vegetation discrimination', Remote Sensing of Environment, vol. 15, no. 2, pp. 155-165.
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The influence of soil background on vegetation discrimination in four-band reflectance space was examined. Dry and wet reflectance data were obtained for 20 soils covering a wide range in spectral properties with a hand-held radiometer. Principal components analysis was used to study the distribution of soil spectra in 4-space and to define a mean soil line. Soil-specific background lines were similarly derived and used to examine the overall cloud of soil spectra in individual soil form. Reflectance data from a full-canopy wheat plot were used to compute unit vector coefficients in the greenness direction from the mean soil line and from the individual soil lines. Analysis of the mean soil line showed that it was not possible to discriminate bare soil from low vegetation densities. Greenness measurements were shown to be sensitive to both soil type and soil moisture condition. In contrast, the use of individual soil lines as a base to measure greenness minimized soil background influence and improved vegetation assessment, particularly at low green plant canopy covers. 1984.
Miller, T.E., Soho Wing, J. & Huete, A.R. 1984, 'The agricultural potential of selected C4 plants in arid environments.', Journal of Arid Environments, vol. 7, no. 3, pp. 275-286.
Germination responses of Amaranthus hypochondriacus, A. retroflexus and Portulaca oleracea to both temperature and moisture were determined in growth chambers and contrasted with the response of 2 C3 species, Beta vulgaris var. cicla and Chenopodium album. The C4 species had higher germination percentages over the entire range of temperatures studied. Differences in photosynthetic pathway had no effect on moisture requirements for germination. Dry weight yields increased linearly with soil moisture for both the C4 and the C3 species, but the water-use efficiencies of the C4 species were higher at all watering levels investigated. The photosynthetic pathway of each species had no apparent effect on its productivity in relation to soil salinity. The nutritional standing qualities except for consistently high lysine contents. No advantage was found for C4 plants grown in saline soil. -from Authors

Reports

Solano, R., Didan, K., Jacobson, A. & Huete, A. NASA report 2010, MODIS vegetation indices (MOD13) C5 user's guide, pp. 1-42, University of Arizona.
A public user manual on how to use the MODIS Vegetation Index satellite product data
Didan, K., Solano, R., Jacobson, A. & Huete, A. NASA report 2010, MODIS Vegetation Indices (MOD13) C5 User's Guide, pp. 1-42, Arizona, USA.
TOne of the primary interests of the Earth Observing System (EOS) program is to study the role of terrestrial vegetation in large-scale global processes with the goal of understanding how the Earth functions as a system. This requires an understanding of the global distribution of vegetation types as well as their biophysical and structural properties and spatial/temporal variations. Vegetation Indices (VI) are robust, empirical measures of vegetation activity at the land surface. They are designed to enhance the vegetation re?ected signal from measured spectral responses by combining two (or more) wavebands, often in the red (0.6 - 0.7 m) and NIR wavelengths (0.7-1.1 m) region