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Soil Moisture Estimation by Combining L-Band Brightness Temperature and Vegetation Related Information

文献类型： 外文期刊

作者： Fu, Yuanyuan 1 ; Zhao, Chunjiang 1 ; Yang, Guijun 1 ; Feng, Haikuan 1 ;

作者机构： 1.Minist Agr PR China, Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Beijing, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Beijing Engn Res Ctr Agr Internet Things, Beijing 100097, Peoples R China

关键词： Soil moisture; L-band brightness temperature; Vegetation water content; Normalized difference infrared index; Leaf area index; Enhanced vegetation index

期刊名称：COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE XI, CCTA 2017, PT II

ISSN： 1868-4238

年卷期： 2019 年 546 卷

页码：

收录情况： SCI

摘要： Passive radiometry at L-band has been widely accepted as one of the most promising techniques for monitoring soil moisture content (SMC). However, with vegetation cover, the scatter and attenuation of microwave signals by vegetation make the discrimination of SMC related signal complicated. To improve SMC estimate, this study proposed the combined use of L-band brightness temperature (T-B) and optical remote sensing data to take into account the effect of vegetation. The normalized difference infrared index (NDII) and enhanced vegetation index (EVI) were used as proxy for including the effect of vegetation water content and structure. Considering viewing angle effects, T-B data were normalized to three different angles (7 degrees, 21.5 degrees, and 38.5 degrees). The model based on the combination of NDII and horizontally polarized TB normalized to 7 degrees produced the best result (R-2 = 0.678, RMSE = 0.026 m(3)/m(3)). It suggests that involving NDII into the model could significantly improve pasture covered SMC estimation accuracy.

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