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Title:   Author  item
                                            HUANG, J - UNIVERSITY OF MIAMIitem
                                            CHEN, D - UNIVERSITY OF LIVERPOOLitem
                                            Cosh, Michael
  Submitted to: International Journal of Remote Sensing
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 6/1/2008
                                    Publication  Date: 4/1/2009
                                    Citation: Huang, J., Chen, D., Cosh, M.H. 2009. Sub-pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites. International Journal of Remote Sensing. 30(8):2075-2104.Interpretive Summary: Remote sensing of the earth’s vegetation layer is complicated by two independent variables, the atmosphere and soil color reflection.  There are models which are able to correct the atmospheric influences and soil reflectance, but none addresses sub-pixel scale variability.  Several approaches are analyzed and a hybrid model was developed using both modeling and empirical approaches to address the problem.  The 6S atmospheric model is used to remote the atmospheric effect.  Local bare soil pixels are used to provide a baseline comparison for vegetated pixels and the soil reflectance errors can be eliminated.  This error is most evident in low vegetation pixels which have a higher percentage of soil visible to the remote sensor.  As a result of this research, vegetation coverage and water content can be more accurately estimated than with previously established methods.  Both conservation efforts and drought monitoring would be improved by incorporation of these techniques into operational monitoring and modeling.
                                Technical Abstract: Estimating vegetation cover, water content and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modeling, 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. Sensitivity of spectral bands and vegetation indices to such contamination were 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 to 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 of 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 over 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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