Shen, J, Huete, A, Tran, NN, Devadas, R, Ma, X, Eamus, D & Yu, Q 2018, 'Diverse sensitivity of winter crops over the growing season to climate and land surface temperature across the rainfed cropland-belt of eastern Australia', Agriculture, Ecosystems and Environment, vol. 254, pp. 99-110.View/Download from: UTS OPUS or Publisher's site
© 2017 Elsevier B.V. The rainfed cropland belt in Australia is of great importance to the world grain market but has the highest climate variability of all such regions globally. However, the spatial-temporal impacts of climate variability on crops during different crop growth stages across broadacre farming systems are largely unknown. This study aims to quantify the contributions of climate and Land Surface Temperature (LST) variations to the variability of the Enhanced Vegetation Index (EVI) by using remote sensing methods. The datasets were analyzed at an 8-day time-scale across the rainfed cropland of eastern Australia. First, we found that EVI values were more variable during the crop reproductive growth stages than at any other crop life stage within a calendar year, but nevertheless had the highest correlation with crop grain yield (t ha−1). Second, climate factors and LST during the crop reproductive growth stages showed the largest variability and followed a typical east-west gradient of rainfall and a north-south temperature gradient across the study area during the crop growing season. Last, we identified two critical 8-day periods, beginning on day of the year (DoY) 257 and 289, as the key 'windows' of crop growth variation that arose from the variability in climate and LST. Our results show that the sum of the variability of the climate components within these two 8-day 'windows' explained >88% of the variability in the EVI, with LST being the dominant factor. This study offers a fresh understanding of the spatial-temporal climate-crop relationships in rainfed cropland and can serve as an early warning system for agricultural adaptation in broadacre rainfed cropping practices in Australia and worldwide.
Wu, J, Kobayashi, H, Stark, SC, Meng, R, Guan, K, Tran, NN, Gao, S, Yang, W, Restrepo-Coupe, N, Miura, T, Oliviera, RC, Rogers, A, Dye, DG, Nelson, BW, Serbin, SP, Huete, AR & Saleska, SR 2018, 'Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest.', New Phytologist, vol. 217, no. 4, pp. 1507-1520.View/Download from: UTS OPUS or Publisher's site
Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun-sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate-phenology relationships in the tropics.
Tran, NN, Huete, A & Hardtke, L 2017, 'Impact of bidirectional reflectance distribution function on modis vegetation indices in southeast Asia tropical forests', 38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017, Asian Conference on Remote Sensing, AARS, Delhi, India, pp. 1-6.View/Download from: UTS OPUS
Copyright © 2017 ISRS, All Rights Reserved. Tropical forests play important roles on global climate and biodiversity. The Moderate Resolution Imaging Spectroradiometer (MODIS), with high temporal resolution, provide a useful tool to study tropical forest dynamics, including seasonality and inter-annual variation. However, optical satellite data have cloud, aerosol and bidirectional reflectance distribution function (BRDF) effects, that create uncertainty in tropical forest studies. In the Amazon, some researchers demonstrated the difficulties in separating true forest dynamics from BRDF artefacts and seasonal cloud and aerosol influences. Lastly, optical reflectance saturation in dense tropical forests may restrict the retrieval of phenology information. In this study, we investigated the impact of BRDF effects on MODIS vegetation indices (VI) in Southeast Asia (SEA) tropical forests, the least studied area compared to other major tropical forests (South America and Central Africa). Moreover, unlike Amazon tropical forests, VI seasonality in SEA forests is not synchronous with sun-sensor geometries. We used 10-year data of daily MODIS BRDF (MCD43A1) collection 6 product, a kernel-driven model product that allows us to retrieve VI values for a range of fixed solar zenith angles (SZA). We compared these with the standard VI products (MOD13A1, MYD13A1) to analyse BRDF influences. The results show significant BRDF effects in all forest sites. Generally, smaller SZA yielded higher VI signals in forests. We found tradeoff's between VI robustness to BRDF effects and saturation that impacted upon the retrievals of phenology parameters.
Shen, J, Tran, NN, Devadas, R, Huete, A, Zhang, H & Yu, Q 2016, 'Climate impacts on wheat phenology and production using mutisource data in NSW, Australia', International Geoscience and Remote Sensing Symposium (IGARSS), IEEE International Geoscience and Remote Sensing Symposium, IEEE, Beijing, China, pp. 6296-6299.View/Download from: Publisher's site
© 2016 IEEE.Wheat is the most important grain crop in Australia, which plays a significant role in world grain-trading market. However, climate warming, water shortage, as well as more frequent extreme weather events (e.g., heatwaves, droughts and floods), under pressure of food demand, would pose great risks to all aspects of wheat production worldwide, especially in Australia with high climate variability. This study aggregated multi-source observational data by using meteorological statistics, in-situ investigation data and the MODIS Enhanced Vegetation Index (EVI) product to explore and examine the correlation between climate variability and spatial-temporal patterns of wheat phenology metrics and productivity. The results from tests over 370 wheat trial sites showed: 1) narrower and earlier sowing and harvesting windows occurred in a drought year (2006) compared with a normal year (2005). Differences in sowing and harvesting window lengths were 9 and 5 days, respectively; 2) different weather patterns in each agro-climatic zone were followed by different remotely sensed crop EVI seasonality profiles. Crop growth was least affected by climate variability in agro-climate region E2, which is located in the south part of study area. This study reveals new information on cropland-climate relationships across the wheat belt in NSW in a changing climate.