Surface Soil Moisture Retrieval Using Optical/Thermal Infrared Remote Sensing Data

文献类型： 外文期刊

第一作者： Wang, Yawei

作者： Wang, Yawei;Peng, Jian;Ludwig, Ralf;Loew, Alexander;Peng, Jian;Peng, Jian;Song, Xiaoning;Leng, Pei

作者机构：

关键词： Ellipse model; land surface temperature (LST); net surface shortwave radiation (NSSR); optical/thermal infrared data; surface soil moisture (SSM)

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：5.6； 五年影响因子：6.086 ）

ISSN： 0196-2892

年卷期： 2018 年 56 卷 9 期

页码：

收录情况： SCI

摘要： Surface soil moisture (SSM) plays significant roles in various scientific fields, including agriculture, hydrology, meteorology, and ecology. However, the spatial resolutions of microwave SSM products are too coarse for regional applications. Most current optical/thermal infrared SSM retrieval models cannot directly estimate the quantitative volumetric soil water content without establishing empirical relationships between ground-based SSM measurements and satellite-derived proxies of SSM. Therefore, in this paper, SSM is estimated directly from 5-km-resolution Chinese Geostationary Meteorological Satellite FY-2E data based on an elliptical-new SSM retrieval model developed from the synergistic use of diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). The elliptical-original model was constructed for bare soil and did not consider the impacts of different fractional vegetation cover (FVC) conditions. To optimize the elliptical-original model for regional-scale SSM estimates, it is improved in this paper by considering the influence of FVC, which is based on a dimidiate pixel model and a Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product. A preliminary validation of the model is conducted based on ground measurements from the counties of Maqu, Luqu, and Ruoergai in the source area of the Yellow River. A correlation coefficient (R) of 0.620, a root-mean-square error (RMSE) of 0.146 m(3)/m(3), and a bias of 0.038 m(3)/m(3) were obtained when comparing the in situ measurements with the FY-2E-derived SSM using the elliptical-original model. In contrast, the FY-2E-derived SSM using the elliptical-new model exhibited greater consistency with the ground measurements, as evidenced by an R of 0.845, an RMSE of 0.064 m(3)/m(3), and a bias of 0.017 m(3)/m(3). To provide accurate SSM estimates, high-accuracy FVC, LST, and NSSR data are required. To complement the point-scale validation conducted here, cross-comparisons with other existing SSM products will be conducted in the future studies.

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