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, Plant Functional Biology & Climate Change
Core Member, Plant Functional Biology & Climate Change
B.Sc, M.Sc, Ph.D
Member, The Institution of Electrical and Electronic Engineers
Member, American Geophysical Union
Member, International Society for Photogrammetry and Remote Sensing
 
Phone
+61 2 9514 4084
Fax
+61 2 9514 4079
Room
CB04.5.50H

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

Book Chapters

Huete, A., Miura, T., Yoshioka, H., Ratana, P. & Broich, M. 2014, 'Indices of vegetation activity' in Jonathan M. Hanes (ed), Biophysical Applications of Satellite Remote Sensing, Springer, New York, pp. 1-41.
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Huete, A. 2012, 'Soil Properties' in Njoku, Eni G. (eds), 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 Earth+s 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.
Huete, A. 2012, 'Vegetation Indices' in Njoku, Eni G. (eds), Encyclopedia of Remote Sensing, Springer, Germany, pp. ---.
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Huete, A. & Glenn, E. 2011, 'Recent advances in remote sensing of ecosystem structure and function' in Weng, Q (eds), Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, CRC Press, Taylor and Francis Group, Boca Raton, Florida USA, pp. 291-319.
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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., Leeuwen, W.v., Miura, T. & Glenn, E. 2011, 'MODIS Vegetation Indices' in Ramachandran, B., Justice, C.O., and 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 (eds), Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, CRC Press, Taylor and Francis Group, Boca Raton, Florida USA, pp. 291-319.
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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., Didan, K. & van Leeuwen, W. 2011, 'MODIS vegetation indices' in Ramachandran, B., Justice, C.O., and 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.
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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., 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 cap+ tulo revisamos los importantes avances y las aplicaciones en el uso de + ndices de vegetaci+n (IV) de MODIS como herramientas para el an+lisis y monitoreo de propiedades y procesos ecosist+micos, incluyendo aquellos relacionados con la fotos+ ntesis y la transpiraci+n del dosel. Presentamos las caracter+ sticas b+sicas de los productos IV-MODIS est+ndar, incluidas las series de tiempo, las medidas cualitativas, el m+todo de composici+n y las incertidumbres presentes en las series de datos. Discutimos construcciones alternativas de las series de datos temporales de IV, incluyendo aquellos de la recepci+n directa de datos. Los perfiles de los IV interanuales y estacionales son presentados y analizados en varios sitios de cobertura terrestre en M+xico y los IV MODIS son comparados con mediciones temporales de productividad bruta y evapotranspiraci+n de ecosistemas realizadas con torres de medici+n 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 din+micas de los ecosistemas.
Thenkabail, P., Lyon, J.G. & Huete, A. 2011, 'Advances in hyperspectral remote sensing of vegetation and agricultural croplands' in Prasad S. Thenkabail, John G. Lyon and Alfredo Huete (eds), Hyperspectral Remote Sensing of Vegetation, Taylor and Francis Group, USA, pp. 3-35.
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 Prasad S. Thenkabail, John G. Lyon and Alfredo Huete (eds), Hyperspectral Remote Sensing of Vegetation, Taylor and Francis Group, USA, pp. 663-688.
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., and 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 M G Anderson (ed), Encyclopedia of Hydrological Sciences, John Wiley & Sons, Ltd, New York, pp. 887-902.
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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.

Books

Chuvieco, E. & Huete, A. 2009, Fundamentals of Satellite Remote Sensing, 1st, CRC/Dekker, Taylor & Francis Informa Group, Boca Raton, Florida USA.

Conference Papers

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', XXII International Society for Photogrammetry & Remote Sensing Congress., Melbourne, Australia, August 2012 in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ed M. Shortis, H. Shimoda, K. Cho, ISPRS Society, Australia, pp. 271-276.
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.

Journal Articles

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= 38 W m++2 and R2=0.72. Direct regressions of observed ET against the VIs resulted in an average RMSE= 60 W m++2 and R2=0.22, while the EF regressions an average RMSE=42 W m++2 and R2=0.64. The MODIS LAI and fPAR product produced the poorest estimates of ET (RMSE>44 W m++2 and R2b0.6); 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=28.4 W m++2, R2=0.66). The normalized difference vegetation index (NDVI) best estimated ET in grassland (RMSE=23.8 W m++2 and R2=0.68), cropland (RMSE=29.2 W m++2 and R2=0.86) and woody savannas (RMSE=25.4 W m++2 and R2=0.82), while the VI-based crop coefficient (Kc) yielded the best estimates for evergreen and deciduous broadleaf forests (RMSE=27 W m++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.
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., Heartsill Scalley, T., Kitchen, S.G., McClaran, M.P., McNab, W.H., Montoya, D.S., Morgan, J.A., Peters, D.P., Sadler, E.J., Seyfried, S. & Starks, P.J. 2013, 'Ecosystem resilience despite large-scale altered hydroclimatic conditions', Nature, vol. 494, pp. 349-353.
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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 regions1. 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 security2, 3. 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 (WUEe: 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 WUEe in drier years that increased significantly with drought to a maximum WUEe 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 WUEe may allow us to predict land-surface consequences as large regions become more arid, starting with water-limited, low-productivity grasslands.
Zhang, Y., Moran, M.S., Nearing, M.A., Ponce Campos, G.E., Huete, A., Buda, A.R., Bosch, D.D., Gunter, S.A., Kitchen, S.G., McNab, W.H., Morgan, J.A., McClaran, M.P., Montoya, D.S., Peters, D.P. & 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 PT. 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.
Miura, T., Turner, J.P. & Huete, A. 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.
Potgieter, A.B., Lawson, K. & Huete, A. 2013, 'Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery', Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 254-263.
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There are increasing societal and plant industry demands for more accurate, objective and near real-time 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.
Monteiro, A.T., Fava, F., Goncalves, 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.
Ma, X., Huete, A., Yu, Q., Restrepo Coupe, N., Davies, K.P., Broich, M., Ratana, P., Beringer, J., Hutley, L.B., Cleverly, J.R., Boulain, N.P. & Eamus, D. 2013, 'Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect', Remote Sensing Of Environment, vol. 139, no. 1, 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 from13 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.
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, no. 1, 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+2500 nm) versus ?2 (400+2500 nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742 nm and 1175 nm (HVI742-1175), (ii) 1296 nm and 1054 nm (HVI1296-1054), (iii) 1225 nm and 697 nm (HVI1225-697), and (iv) 702 nm and 1104 nm (HVI702-1104).
Peng, D., Jiang, Z., Huete, A., 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 to the 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.
Obata, K., Miura, T., Yoshioka, H. & Huete, A. 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, pp. 073467-1-073467-16.
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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.
Bargain, A., Robin, M., Le Men, E., Huete, A. & Barille, 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
Yang, X., Huang, J., Wu, Y., Wang, J., Wang, P., Wang, X. & Huete, A. 2012, '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.
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.
Peng, D., Huete, A., Huang, J., Wang, F. & Sun, H. 2011, 'Detection and estimation of mixed paddy rice cropping patterns with MODIS data', 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 (2.5 m) 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).
Anderson, L.O., Arageo, L.E., 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 of MODIS 250-m data over three contrasting land cover types in the Amazon were used in conjunction with rainfall data, a land cover map 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 a more comprehensive exploration of the spectral and structural changes in vegetation formations.
Glenn, E., Doody, T., Guerschman, J., Huete, A., King, E., Mcvicar, T., Van, A.D., Van, T.N., 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 (ETa) patterns across wide areas of agricultural and natural ecosystems, as opposed to just point measurements of ETa. The Australian Government has tasked the science agencies with operationally developing monthly and annual estimates of ETa 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 ETa 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 ETa. Ground and remote sensing ETa 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 ETa models are estimated to have an error or uncertainty of 10% to 20% in Australia. Developments in Australian ETa research over the past 20?years are reviewed, and sources of error and uncertainty in current methods and models are discussed.
Luvall, J.C., Sprigg, W., Levetin, E., Huete, A., Nickovic, S., Pejanovic, G., de Water, P.V., Myers, O., Budge, A., Crimmins, T., Krapfl, H. & Zelicoff, A. 2011, 'Use of MODIS satellite images and an atmospheric dust transport model to evaluate Juniperus spp. pollen phenology and dispersal to support public health alerts', Journal of Allergy and Clinical Immunology, vol. 127, pp. AB19-AB19.
Jenerette, D.G., Scott, R.L. & Huete, A. 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 inter-annual temporal scales? Location: Southern Arizona, USA. Methods: We compared satellite-derived phenological variation between 38 distinct 625-km2 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.
Glenn, E., Nagler, P. & Huete, A. 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.
Ferreira, N.C., Ferreira, L.G. & Huete, A. 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 times 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.
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.
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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.
Kim, Y., Huete, A., Miura, T. & Jiang, Z. 2010, 'Spectral compatibility of vegetation indices across sensors: A band decomposition analysis with Hyperion data', Journal of applied remote sensing, vol. 4, pp. 043520-1-043520-22.
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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.
Yoshioka, H., Miura, T., Dematte, J.A., Batchily, K. & Huete, A. 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.
Peng, D., Huang, J., Huete, A., Yang, T., 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, 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.
Miura, T. & Huete, A. 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 postflight 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.
Fisher, J.B., Mahli, Y., Bonal, D., da Rocha, H.R., Araujo, A.C., Gamo, M., Goulden, M.L., Hirano, T., Huete, A., Kondo, H., Kumagai, T., Loescher, H.W., Miller, S.D., Nobre, A.D., Nouvellon, Y., Oberbauer, S.F., Panuthai, S., Roupsard, O., Saleska, S.R., 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 O 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 1370 mm yr-1, but this value is dependent on assumptions about energy balance closure for the tropical eddy covariance sites; a lower value (1096 mm yr-1) is considered in discussion on the use of flux data to validate and interpolate models.
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.
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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., Huang, J., Huete, A., Peng, D. & Zhang, F. 2009, 'Mapping paddy rice in China with multi-date MODIS data', Zhejiang University Journal (Science A): applied physics and engineering, 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 moderate-resolution 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
Yoshioka, H., Miura, T., Dematte, J.A., Batchily, K. & Huete, A. 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 infra-red 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.
Potter, C., Klooster, S., Huete, A., Genovese, V., Bustamante, M., Guimaraes Ferreira, L., Cosme de Oliveira Junior, R. & Zepp, R. 2009, 'Terrestrial carbon sinks in the Brazilian Amazon and Cerrado region predicted from MODIS satellite data and ecosystem modeling', Biogeosciences, vol. 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 Cerrado re- gions 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 Rond-+onia and the northern portions of the state of Par - a. These areas were not significantly impacted by the 2002++2003 El Ni-+no 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 Maranh-+ao 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 mea sured 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., 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.
Huete, A., 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.
Glenn, E., Huete, A., 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 light-dependent physiological processes, such as photosynthesis, occurring in the upper canopy. Practical studies have used time-series VIs to measure primary production and evapotranspiration, 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.
Ferreira, N.C., Ferreira, L.G., Huete, A. & 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., Pottors, C., Hutyra, L.R., Huete, A., 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 10 m) 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+3 m) 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+10 m 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.
Saleska, S.R., Didan, K., Huete, A. & da Rocha, H.R. 2007, 'Amazon Forests Green-Up During 2005 Drought', Science, vol. 318, no. 5850, pp. 612-612.
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Coupled climate-carbon cycle models suggest that Amazon forests are vulnerable to both long- and short-term droughts, but satellite observations showed a large-scale photosynthetic green-up in intact evergreen forests of the Amazon in response to a short, intense drought in 2005. These findings suggest that Amazon forests, although threatened by human-caused deforestation and fire and possibly by more severe long-term droughts, may be more resilient to climate changes than ecosystem models assume.
Glenn, E., Huete, A., 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.
Yang, F., Ichii, K., White, M.A., Hashimoto, H., Michaelis, A.R., Votava, P., Zhu, A., 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 1.87 gC/m2/day and an R2 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
Nagler, P.L., Glenn, E., Kim, H., Emmerich, W., Scott, R.L., Huxman, T.E. & Huete, A. 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.
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, pp. 1-21.
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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.
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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.
Myneni, R.B., Yang, W., Nemani, R.R., Huete, A., Dickinson, R.E., Knyazikhin, Y., Didan, K., Fu, R., Negron Juarez, 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, 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, ?owers, 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 ?ushing 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
Barbosa, H.A., Huete, A. & 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 not, vert, similar7+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.
Jiang, Z., Huete, A., Liu, 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 red-near-infrared reflectance space were developed and evaluated in this paper. Unsalan and Boyer previously proposed an angle-based vegetation index, thetas (denoted as thetas NDVI in this paper), based on the normalized difference vegetation index (NDVI) with the objective of overcoming the saturation problem in the NDVI. However, thetasNDVI did not consider strong soil background influences present in the NDVI. To reduce soil background noise, an angle-based vegetation index, thetasSAVI , based on the soil-adjusted vegetation index (SAVI), was derived using trigonometric analysis. The performance of thetasNDVI and thetasSAVI was evaluated and compared with their corresponding vegetation indices, NDVI and SAVI. The soil background influence on thetasNDVI was found to be as significant as that on the NDVI. thetasNDVI 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 thetasNDVI. By contrast, thetasSAVI 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 thetasSAVI 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
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.
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.
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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.
Franklin, K.A., Lyons, K., Nagler, P.L., Lampkin, D., Glenn, E., Molina-Freaner, F., Markow, T. & Huete, A. 2006, 'Buffelgrass (Pennisetum ciliare) land conversion and productivity in the plains of Sonora, Mexico', Biological Conservation, vol. 127, 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.
Cheng, Y., Gamon, J.A., Fuentes, D.A., Mao, Z., Sims, D.A., Qiu, H., 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 SpecNet and FLUXNET networks. The study covered a four-year period (2000-2004), which included a severe drought in 2002 and a subsequent wildfire in July 2003, leading to extreme perturbation in ecosystem 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 meter (tram system in the field) to 1000 m (MODIS satellite sensor). For the EVI, there was excellent agreement between MODIS, AVIRIS and the ground measurements (tram system). AVIRIS and tram-based NDVI and fPAR values were in close agreement. However, MODIS NDVI and fPAR values were consistently higher than those determined from the field and the aircraft sensor, and these differences could not be entirely attributed to differences in sampling scale. Interestingly, MODIS fPAR derived from backup algorithms (NDVI driven) was closer to the AVIRIS and tram fPAR under the cloudy conditions.
Jiang, Z., Huete, A., Chen, J., Chen, Y., Liu, 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. 366-369.
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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.
Huete, A., Didan, K., Shimabukuro, Y.E., Ratana, P., Saleska, S.R., Hutyra, L.R., Yang, W., Nemani, R.R. & Myneni, R.B. 2006, 'Amazon rainforests green-up with sunlight in dry season', Geophysical Research Letters, vol. 33, no. 6, pp. L06405-1-L06405-4.
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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.
Nagler, P.L., Cleverly, J.R., Glenn, E., Lampkin, D., Huete, A. & Zhengming, W. 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 (Ta) 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 Ta had an r2=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.
Nagler, P.L., Scott, R.L., Westenburg, C., Cleverly, J.R., Glenn, E. & Huete, A. 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 Ta 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.
Ratana, P., Huete, A. & Ferreira, L. 2005, 'Analysis of Cerrado Physiognomies and Conversion in the MODIS Seasonal - Temporal Domain', Earth Interactions, vol. 9, no. 3, pp. 1-22.
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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.
Shabanov, N.V., Huang, D., Yang, W., Tan, B., Knyazikhin, Y., Myneni, R.B., Ahl, D.E., Gower, S.T., Huete, A., Aragao, L.E. & 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.
Nagler, P.L., Hinojosa-Huerta, O., Glenn, E., Garcia-Hernandez, J., Romo, R., Curtis, C., Huete, A. & 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.
Sano, E.E., Ferreira, L.G. & Huete, A. 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, pp. 1-15.
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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.
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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.L., Glenn, E., Curtis, C., Hursh, K. & 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.
Ferreira, L.G. & Huete, A. 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.
Nagler, P.L., Glenn, E., Thompson, T.L. & 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.
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).
Yoshioka, H., Miura, T. & Huete, A. 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, 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., 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, 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 Nacunan 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 Nacunan 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., 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.
Gao, X., Huete, A. & 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, 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.
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 Amazonia (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.
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.
Miura, T., Huete, A., 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, 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.
Nagler, P.L., Glenn, E. & Huete, A. 2001, 'Assessment of spectral vegetation indices for riparian vegetation in the Colorado River delta, Mexico', Journal Of Arid Environments, vol. 49, 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=0837), but the soil adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) gave nearly equal results (r2=0807 and 0796, respectively). Normalized difference vegetation index, SAVI and EVI were less useful in predicting GLAI (r2=073, 065, 064, respectively).
Zamora-Arroyo, F., Nagler, P.L., Briggs, M., Radtke, D., Rodriquez, H., Garcia, J., Valdes, C., Huete, A. & Glenn, E. 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 floods that germinate new cohorts of Populus fremontii andSalix 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 3109m3at 80+120 m3s-1is 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.
Yoshioka, H., Miura, T., Huete, A. & Ganapol, B.D. 2000, 'Analysis of Vegetation Isolines in Red-NIR Reflectance Space', Remote Sensing Of Environment, vol. 74, 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.
Yoshioka, H., Huete, A. & Miura, T. 2000, 'Derivation of Vegetation Isoline Equations in Red-NIR Reflectance Space', IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 2, 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 VIs 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.
Qi, J., Kerr, Y.H., Moran, M.S., Weltz, M.A., Huete, A., 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.
Reynolds, C.A., Yitayew, M., Slack, D.C., Hutchinson, C.F., Huete, A. & Petersen, S. 2000, 'Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and groundbased ancillary data', International journal of remote sensing, vol. 21, no. 18, pp. 3487-3508.
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An operational crop yield model was developed by introducing realtime satellite imagery into a Geographical Information System (GIS) and the Crop Specific Water Balance (CSWB) model of the Food and Agriculture Organization (FAO). Input databases were developed with three different resolutions; agro-ecological zone (AEZ) polygons, 7.6 km and 1.1 km pixels; from archived satellite data commonly used by Early Warning Systems (EWS) to simulate maize yield and production in Kenya from 1989 to 1997. Simulated production results from the GIS-based CSWB model were compared to historical maize production reports from two Government of Kenya (GoK) agencies. The coefficients of determination (r2) between the model and GoK district reports ranged from 0.86 to 0.89. The results indicated the 7.6 km pixel-by-pixel analysis was the most favorable method due to the Rainfall Estimate (RFE) input data having the same resolution. The GIS-based CSWB model developed by this study could also be easily expanded for use in other countries, extended for other crops, and improved in the future as satellite technologies improve.
Gao, X., Huete, A., 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-infrared 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.
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.
van Leeuwen, J.D., Huete, A. & 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. & 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 23 incidence angle, whereas the Ku-band data were obtained with 35, 55, and 75 incidence 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 23 incidence angle and Ku-band with a 35 incidence 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., van Leeuwen, J.D. & Didan, K. 1998, 'Vegetation detection through smoke-filled A VIRIS images: an assessment using MODIS band passes', Journal Of Geophysical Research-Atmospheres, vol. 103, no. D24, pp. 32001-32011.
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Abstract. Radiomelrtcally calibrated, Airborne Visible Infrared Imaging SpectromeIer (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 SpectroradiomeIer (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 flm of 0.14, 1.1, and 1.9, respectively. The atmospheric resistant VIs and various middle-infrared (MlR) derived Vis were then analyzed with respect to their ability 10 minimize atmospheric "smoke" contamination. The atmospheric resistant VIs utilized the blue band for correction of !he red band, while !he MlR-derived Vis used !he MIR region (1.3 - 2.5 flm) as a substitute for the red band since it is relatively transparent to smoke, yet remains sensitive to green vegetation. The performance of lhese indices were assessed and compared wi!h !he normalized difference vegetation index (NDVI) and !he soil-adjusIed vegetation index (SAVI). Over !he tropical forests !he NDVI and SAVI had high relative errors over all smoke-filled atmospheric conditions (50-80% error), while !he atmospheric resistant VIs resulted in a 50-80% relative error only over thick levels of smoke.
Justice, C.O., Vermote, E.F., Townshend, J.R., 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., Leeuwen, W.v., Wolfe, R.E., Giglio, L., Muller, J., 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
Huete, A., Liu, H.Q., Batchily, K. & Leeuwen, W.v. 1997, 'A Comparison of Vegetation Indices over a Global Set of TM Images for EOS-MODIS', Remote Sensing Of Environment, vol. 59, 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, temperate 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.
Santibanez, F., Morales, L., la Fuente, J.d., Cellier, P. & Huete, A. 1997, 'Topoclimatic modeling for minimum temperature prediction at a regional scale in the Central Valley of Chile', Agronomy for Sustainable Development, vol. 17, no. 6-7, pp. 307-314.
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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, J.D., Huete, A., Walthall, C.L., Prince, S.D., Begue, 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, 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 a 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 readiation (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.
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, J.D. & Huete, A. 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.
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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 phyameters (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.
Begue, A., Roujean, J.L., Hanan, N.P., Prince, S.D., Thawley, M., Huete, A. & Tanre, 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.
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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.
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.
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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.
Liu, H.Q. & 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.
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The Normalized Difference Vegetation Index (NDVI) equation has a simple, open loop structure (no feedback), which renders it susceptible to large sources of error and uncertainty over variable atmospheric and canopy background conditions. In this study, a systems analysis approach is used to examine noise sources in existing vegetation indices (VIs) 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 the MNDVI are physically-based and are empirically related to the expected range of atmospheric and background +boundary+ conditions. The MNDVI can be used with data uncorrected for atmosphere, as well as with Rayleigh corrected and atmospherically corrected data. In the field observational and simulated data sets tested, the MNDVI was found to considerably reduce noise for any complex soil and atmospheric situation. The resulting uncertainty, expressed as vegetation equivalent noise, was 0.11 leaf area index (LAI) units, which was 7 times less than encountered with the NDVI (0.8 LAI). These results indicate that the MNDVI may be satisfactory in meeting the need for accurate, long term vegetation measurements for the Earth Observing System (EOS) program
Epiphanio, J.C. & Huete, A. 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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This article describes the impacts of sensor view and solar zenith angles on two vegetation indices -NDVI (normalized difference vegetation index) and SAVI (soil adjusted vegetation index). An evaluation of these geometric factors on the relationships between these VIs (vegetation indices) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) was performed. To accomplish this, an experiment was conducted in Phoenix, Arizona, over plots of bare soil and low, medium, and high alfalfa. Reflectances were measured from 0.4 ?m to 1.0 ?m in nine view angles (from -40 to + 40, in 10 steps) over varying solar zenith angles. This was done simultaneously with fAPAR measurements. Changes in view angle caused variations in the indices to be as high as 50% in relation to nadir. However, there was an opposite view angle behavior between NDVI and SAVI, with the former increasing from antisolar to forward scattering view direction. A derivative analysis of the indices showed the SAVI to exhibit a more linear relationship than NDVI with the individual bands. The relationships between both VIs and fAPAR were, in general, linear. However, view angle variations perturbed these relationships and caused an over- or underestimation of fAPAR, depending on view direction (antisolar or forward), view angle, and vegetation index (NDVI or SAVI).
Bannari, A., Morin, D., Bonn, F. & Huete, A. 1995, 'A review of vegetation indices', Remote sensing Reviews, vol. 13, no. 1-2, pp. 95-120.
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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.
Qi, J., Chehbouni, A., Huete, A., 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.
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.
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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.
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.
Franklin, J., Duncan, J., Huete, A., van Leeuwen, J.D., Li, X. & Begue, 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.
van Leeuwen, J.D., Huete, A., 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.
Qi, J., Huete, A., 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.
Huete, A., Hua, G., Qi, J., Chehbouni, A. & van Leeuwen, 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.
Huete, A. & 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.
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 Jr, P.J., Qi, J., Riggs, A.C., Schmugge, T.J., Shutko, A.M., Stannard, D.I., Swiatek, E., van Leeuwen, J.D., Zyl, J.v., 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.
Jackson, R.D. & Huete, A. 1991, 'Interpreting vegetation indices', Preventive Veterinary Medicine, vol. 11, pp. 185-200.
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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.
Huete, A. & 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.
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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.
Huete, A. & 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.
Levitt, D.G., Simpson, J.R. & Huete, A. 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.
Huete, A. 1988, 'A Soil-Adjusted Vegetation Index', 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 (Eragrosticslehmanniana 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.
Huete, A. & 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.
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. & Jackson, R.D. 1987, 'Suitability of Spectral Indices for Evaluating Vegetation Characteristics on Arid Rangelands', Remote Sensing Of Environment, vol. 23, no. 2, pp. 213-232.
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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.
Huete, A. 1987, 'Soil and sun angle interactions on partial canopy spectra', International journal of remote sensing, vol. 8, no. 9, pp. 1307-1317.
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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.
Post, D.F., Huete, A. & Pease, D.S. 1986, 'A comparison of soil scientist estimations and laboratory determination of some Arizona soil properties', Journal of soil and water conservation, vol. 41, no. 6, pp. 421-424.
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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). Linear regression equation slopes and intercepts for all estimates differ from 1 and 0, respectively, indicating the absolute agreements were in error. Coefficients of variation show how estimates varied among soil scientists. Information about the soil scientists' degree of confidence, their estimations, and their performance also are presented
Huete, A. 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 discusse
Huete, A. & Mc Coll, 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.
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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.
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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., 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

Reports

Didan, K., Solano, R., Jacobson, A. & Huete, A. 2010, 'MODIS Vegetation Indices (MOD13) C5 User's Guide', NASA report, Arizona, USA, pp. 1-42.
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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
Solano, R., Didan, K., Jacobson, A. & Huete, A. 2010, 'MODIS vegetation indices (MOD13) C5 user's guide', NASA report, University of Arizona, pp. 1-42.
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A public user manual on how to use the MODIS Vegetation Index satellite product data