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Original Articles

An exploratory analysis of spectral indices to estimate vegetation water content using sensitivity function

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Pages 161-169 | Received 21 Aug 2010, Accepted 30 Dec 2010, Published online: 01 Jun 2011
 

Abstract

Water is an important component of vegetation canopies and the timely monitoring of vegetation moisture status is very useful for agricultural drought assessment and forest fire prediction. Currently, the retrieval of vegetation water content (VWC) is mainly based on spectral indices. However, the different performances under varying conditions make the evaluation of spectral indices very necessary. In previous studies, the determination coefficient (R 2), mean square error (MSE) and root mean square error (RMSE) were widely used for the evaluation of spectral indices. However, using these constants as evaluation criteria, the performance of spectral indices was the same within a wide range and thus could not be precisely described. In this study, the sensitivity function was introduced to investigate the strength of the spectral indices to VWC estimation based on LOPEX (Leaf Optical Properties EXperiment) data set. Equivalent water thickness (EWT) and fuel moisture content (FMC), both FMC based on fresh weight (FMCw) and FMC based on dry weight (FMCd), were used to indicate vegetation moisture status. According to the results, the major conclusions are as follows: (1) the sensitivity of spectral indices to VWC is not a constant but a function of VWC; (2) for each VWC indicator, the spectral indices show varying performances at different moisture levels, which can provide reference for spectral index selection while monitoring the vegetation moisture status; and (3) besides, the sensitivity of the same spectral index to different vegetation moisture indicators is not the same and the comparative study indicates that spectral indices are much more sensitive to EWT than FMCw and FMCd.

Acknowledgements

This research received financial support from the National High-tech R&D Program (863 Program) of China (grant number: 2006AA120108). The authors express their thanks to Lei Ji from USGS for his help.

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