Estimating vegetation cover, water content, and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modelling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often by the contamination of the remote sensing signal by the atmosphere and soil reflectance variations at the surface. This study used Landsat TM/ETM+ and MODIS data to investigate how sub-pixel atmospheric and soil reflectance contamination can be removed from the remotely sensed vegetation growth signals. The sensitivity of spectral bands and vegetation indices to such contamination was evaluated. Combining the strengths of atmospheric models and empirical approaches, a hybrid atmospheric correction scheme was proposed. With simplicity, it can achieve reasonable accuracy in comparison with the 6S model. Insufficient vegetation coverage information and poor evaluation of fractional sub-pixel bare soil reflectance are major difficulties in sub-pixel soil reflectance unmixing. Vegetation coverage was estimated by the Normalized Difference Water Index (NDWI). Sub-pixel soil reflectance was approximated from the nearest bare soil pixel. A linear reflectance mixture model was employed to unmix sub-pixel soil reflectance from vegetation reflectance. Without sub-pixel reflectance contamination, results demonstrate the true linkage between the growth of sub-pixel vegetation and the corresponding change in satellite spectral signals. Results suggest that the sub-pixel soil reflectance contamination is particularly high when vegetation coverage is low. After unmixing, the visible and shortwave infrared reflectances decrease and the near-infrared reflectances increase. Vegetation water content and dry biomass were estimated using the unmixed vegetation indices. Superior to the NDVI and the other NDWIs, the SWIR (1650 nm) band-based NDWI showed the best overall performance. The use of the NIR (1240 nm), which is a unique band of MODIS, was also discussed.
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Document Type: Research Article
Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL 33149-1031
Department of Engineering, The University of Liverpool, Liverpool L69 3GQ, UK
Hydrology and Remote Sensing Laboratory, Agricultural Research Service, United States Department of Agriculture, MD, 20705-2350
Publication date: 2009-04-01
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