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Proceedings Paper

Comparison of the sensor dependence of vegetation indices and vegetation water indices based on radiative transfer model
Author(s): Xiaoping Chen; Shudong Wang; Hailing Jiang; Xia Zhang
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Paper Abstract

The vegetation index (VI) and vegetation water index (VIw) have long been used for plant water stress detection indiscriminately, without considering the effects of differences in their band selection. To address this, this study quantitatively compared the difference of sensor dependence for the two indices based on canopy/atmospheric radiative transfer model. Five different bandwidths at canopy and top-of-atmosphere scale were simulated separately for 23 classic indices. The results show that VIws exhibited better correlation with vegetation water content (VWC) at both scale ( R2 : 0.835; 0.812) in comparison with VIs ( R2 : 0.474; 0.475). To quantitatively describe the uncertainty caused by bandwidth, a new index variability was established. VIws and VIs performed entirely differently: at canopy scale, the uncertainty caused by bandwidths for VIws and VIs is 13.703% and 43.451%, respectively. However, at top-of-atmosphere scale, the uncertainty for VIws and VIs is 32.021% and 41.265%. VIws exhibited less dependence on bandwidth and were more affected by atmospheric effect than VIs. We attribute these differences to differences in band selection: VIws based on water absorption features are more sensitive to not only variation of VWC but also atmospheric conditions. Conversely, as chlorophyll absorption features which VIs are calculated on effectively avoid atmospheric absorption features and are located in red edge region, VIs are found less affected by the atmosphere condition and extremely sensitive to bandwidth. Results figure out the differences we should focus on when we choose VI or VIw from different sensors for VWC retrieval.

Paper Details

Date Published: 8 November 2014
PDF: 16 pages
Proc. SPIE 9260, Land Surface Remote Sensing II, 92603G (8 November 2014); doi: 10.1117/12.2069033
Show Author Affiliations
Xiaoping Chen, Harbin Institute of Technology (China)
Shudong Wang, Institute of Remote Sensing and Digital Earth (China)
Hailing Jiang, Peking Univ. (China)
Xia Zhang, Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 9260:
Land Surface Remote Sensing II
Thomas J. Jackson; Jing Ming Chen; Peng Gong; Shunlin Liang, Editor(s)

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