A Multivariable Approach for Estimating Soil Moisture from Microwave Radiation Imager(MWRI)  

作　　者：Sibo ZHANG Fuzhong WENG Wei YAO 

机构地区：[1]Qian Xuesen Laboratory of Space Technology,China Academy of Space Technology,Beijing 100094 [2]State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,China Meteorological Administration,Beijing 100081

出　　处：《Journal of Meteorological Research》2020年第4期732-747,共16页气象学报（英文版）

基　　金：Supported by the National Key Research and Development Program of China(2018YFC1506501);China Academy of Space Technology“Spaceborne Observations Coping with the Crisis of Global Warming Responsibility of Major Powers in the Paris Agreement”and“Research on the Design of the Spaceborne Observation System of Global Climate Change”projects。

摘　　要：Accurate measurements of soil moisture are beneficial to our understanding of hydrological processes in the earth system. A multivariable approach using the random forest(RF) machine learning technique is proposed to estimate the soil moisture from Microwave Radiation Imager(MWRI) onboard Fengyun-3 C satellite. In this study, Soil Moisture Operational Products System(SMOPS) products disseminated from NOAA are used as a truth to train the algorithm with the input of MWRI brightness temperatures(TBs) at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz, TB polarization ratios(PRs) at 10.65, 18.7, and 23.8 GHz, height in digital elevation model(DEM), and soil porosity. The retrieved soil moisture is also validated against the independent SMOPS data, and the correlation coefficient is about0.8 and mean bias is 0.002 m^3 m^-3 over the period from 1 August 2017 to 31 May 2019. Our retrieval of soil moisture also has a higher correlation with ECMWF ERA5 soil moisture data than the MWRI operational products. In the western part of China, the spatial distribution of MWRI soil moisture is much improved, compared to the MWRI operational products.

关 键 词：soil moisture Microwave Radiation Imager(MWRI) machine learning microwave remote sensing 

分 类 号：S152.7[农业科学—土壤学]