Method for soil moisture retrieval in arid prairie using TerraSAR-X data

Xiaojing Bai; Binbin He; Minfeng Xing; Xiaowen Li

doi:10.1117/1.JRS.9.096062

20 April 2015 Method for soil moisture retrieval in arid prairie using TerraSAR-X data

Xiaojing Bai, Binbin He, Minfeng Xing, Xiaowen Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 096062 (April 2015). https://doi.org/10.1117/1.JRS.9.096062

Abstract

In arid prairie, soil moisture has a substantial impact on the prairie resource management. The synthetic aperture radar (SAR) based soil moisture retrieval is often hampered by vegetation effects on the backscattering coefficient. A new method has been proposed to retrieve soil moisture using TerraSAR-X data. The developed method included bare soil backscattering σ^o_ppsoil simulation, vegetation effect correction, and the relationship between σ^o_ppsoil and soil moisture establishment. The bare soil surface was described using the forward advanced integral equation model. The vegetation influence was eliminated through an empirical ratio method. Soil moisture was retrieved from the estimated σ^o_ppsoil. The collected datasets in Wutumeiren prairie were used to verify the developed method. Four different vegetation variables were incorporated to separate the influence of vegetation, including leaf area index (LAI), normalized difference vegetation index, enhanced vegetation index, and vegetation water content, respectively. The lowest soil moisture inversion error was found when LAI was applied to decouple the effect of vegetation, which led the root mean square error to reach up to 5.02 vol.%. From the perspective of experiment results, LAI was recommended to characterize the scattering mechanism of vegetation. These results indicated that TerraSAR-X data have an operational potential for soil moisture retrieval in arid prairie.

Citation Download Citation

Xiaojing Bai, Binbin He, Minfeng Xing, and Xiaowen Li "Method for soil moisture retrieval in arid prairie using TerraSAR-X data," Journal of Applied Remote Sensing 9(1), 096062 (20 April 2015). https://doi.org/10.1117/1.JRS.9.096062

Published: 20 April 2015

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