A Multivariable Approach for Estimating Soil Moisture from Microwave Radiation Imager (MWRI)
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Advanced Applications of Meteorological Satellite Observations in Ecological Remote Sensing
AUGUST 2020
A Multivariable Approach for Estimating Soil Moisture from Microwave Radiation Imager (MWRI) Sibo ZHANG1*, Fuzhong WENG2, and Wei YAO1 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 (Received December 2, 2019; in final form March 6, 2020)
ABSTRACT 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-3C 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 about 0.8 and mean bias is 0.002 m3 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. Key words: soil moisture, Microwave Radiation Imager (MWRI), machine learning, microwave remote sensing Citation: Zhang, S. B., F. Z. Weng, and W. Yao, 2020: A multivariable approach for estimating soil moisture from Microwave Radiation Imager (MWRI). J. Meteor. Res., 34(4), 732–747, doi: 10.1007/s13351-020-9203-x.
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Introduction
Soil moisture controls the evapotranspiration (ET) process of land surfaces and plays an irreplaceable role in the interactions between water, energy, and carbon cycles over land (Albergel et al., 2012; Cao et al., 2019; Green et al., 2019; Liao et al., 2019; Rudd et al., 2019; Seager et al., 2019; Zhang et al., 2019). In the natural environment, soil moisture varies greatly with soil properties (i.e., porosity, texture, density, and structure), surface roughness, topography, land cover, land temperature, rainfall, and ET. Therefore, spatial–temporal heterogeneity is the major characteristic of soil moisture. Previous studies have shown the impact of soil moisture on atmospheric variables at various scales (Xu et al., 2012;
Zhang et al., 2013; Parrens et al., 2014; Ruosteenoja et al., 2018; Pangaluru et al., 2019). Today, in addition to the point measurements of soil moisture from in situ probes, microwave remote sensing is widely used to determine large-scale surface soil moisture, typically in the low frequencies from 1 to 10 G
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