A simplified physically-based algorithm for surface soil moisture retrieval using AMSR-E data

Abstract

A simplified physically-based algorithm for surface soil moisture inversion from satellite microwave radiometer data is presented. The algorithm is based on a radiative transfer model, and the assumption that the optical depth of the vegetation is polarization independent. The algorithm combines the effects of vegetation and roughness into a single parameter. Then the microwave polarization difference index (MPDI) is used to eliminate the effects of surface temperature, and to obtain soil moisture, through a nonlinear iterative procedure. To verify the present algorithm, the 6.9 GHz dual-polarized brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) were used. Then the soil moisture values retrieved by the present algorithm were validated by in-situ data from 20 sites in the Tibetan Plateau, and compared with both the NASA AMSR-E soil moisture products, and Soil Moisture and Ocean Salinity (SMOS) soil moisture products. The results show that the soil moisture retrieved by the present algorithm agrees better with ground measurements than the two satellite products. The advantage of the algorithm is that it doesn’t require field observations of soil moisture, surface roughness, or canopy biophysical data as calibration parameters, and needs only single-frequency brightness temperature observations during the whole retrieval process.

Keywords

passive microwave remote sensing / soil moisture / inversion / AMSR-E / SMOS

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Acknowledgments

We are grateful to Dr. Alok Sahoo from Princeton University for his valuable assistance, and professor Yang Kun from the Institute of Tibetan Plateau Research, Chinese Academy of Sciences, for kindly providing the precipitation data. We also thank NSIDC, CATDS (Centre Aval de Traitement des Données SMOS), and the International Soil Moisture Network for providing the AMSR-E products, SMOS products, and in-situ soil moisture data, respectively. This work was supported by the Chinese Ministry of Science and Technology (No. 2011AA120403), the Director Foundation for Postgraduates of Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences (No. Y3ZZ17101B), and partially supported by the National Natural Science Foundation of China (Grant No. 41101391) and the Key Laboratory of Mapping from Space, National Administration of Surveying, Mapping and Geoinformation (No. K201301).

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg