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

• Published in: Frontiers of earth science Vol. 8; no. 3; pp. 427 - 438
• Main Authors: Zeng, Jiangyuan, Li, Zhen, Chen, Quan, Bi, Haiyun
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer-Verlag 01.09.2014; Higher Education Press; Springer Nature B.V
• Subjects: Algorithms; AMSR-E; Earth and Environmental Science; Earth Sciences; inversion; microwave radiometers; Moisture content; Optical analysis; passive microwave remote sensing; Radiative transfer; Remote sensing; Research Article; roughness; SMOS; Soil adsorption; Soil moisture; Soil Moisture and Ocean Salinity satellite; Soil sciences; Soil surfaces; soil water; surface roughness; Surface temperature; Vegetation; Vegetation effects; 土壤水分; 检索; 物理数据; 算法; 美国航空航天局; 表层; 表面粗糙度; China, People's Rep., Xizang, Tibetan Plateau; SMOS; soil moisture; inversion; passive microwave remote sensing; AMSR-E
• ISSN: 2095-0195; 2095-0209
• DOI: 10.1007/s11707-014-0412-4

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.