数字农科院2.0

Estimation Of Leaf Water Content U.sing New Vegetation Indices C ombined By Near- And Middle Infrared Spectral Reflectances

文献类型： 外文期刊

作者： Zhang, ZY; Tang, BH; Li, ZL; Tang, RL; Zhong, RF

作者机构：

关键词： Leaf water content (LWC); vegetation indices; near-infrared (NIR); middle-infrared (MIR)

期刊名称： 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)

ISSN： 2153-6996

年卷期： 2017 年

页码：

摘要： This paper attempts to retrieve leaf water content (LWC) by developing new vegetation indices from the combination of the near-infrared (NIR) and middle-infrared (MIR) spectral reflectances. The expanded vegetation leaf model PROSPECT-VISIR and the widely validated four-stream scattering by arbitrarily inclined leaves (4SAIL) model are employed to simulate canopy reflectance in 0.4-5.7 mu m region with various leaf water content scenarios. Change of standard deviation of the canopy reflectance with respect to wavelength is used to analyze the sensitive of the spectral reflectance to the LWC. The results show that the spectral reflectances at 1.405 mu m, 1.875 mu m, 2.015 mu m, and 4.3751 mu m are most sensitive to the change of LWC, and the difference vegetation index (DVI) combined by spectral reflectances in 1.4051 mu m and 4.3751 mu m is the best index to retrieve LWC with root mean square error (RMSE) of 0.0008 g/cm(2).

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