A method for estimating surface soil moisture from diurnal land surface temperature observations over vegetated regions: A preliminary result over an AmeriFlux site and the REMEDHUS network

文献类型： 外文期刊

第一作者： Geng, Yun-Jing

作者： Geng, Yun-Jing;Leng, Pei;Li, Zhao-Liang

作者机构：

期刊名称：JOURNAL OF HYDROLOGY （ 影响因子：6.4； 五年影响因子：6.6 ）

ISSN： 0022-1694

年卷期： 2023 年 617 卷

页码：

收录情况： SCI

摘要： Most current optical/thermal-based surface soil moisture (SSM) retrieval methods only utilize the instantaneous measurements made by onboard polar-orbit satellites, and they require soil texture as prior knowledge to obtain the volumetric SSM content. A recent study developed a diurnal land surface temperature (LST)-based SSM retrieval method to directly estimate the volumetric SSM content without the knowledge of soil texture. However, most subsequent studies have only examined the use of this method on bare soils. Therefore, the present study aims to investigate the feasibility of the method over vegetated regions with both experimental data and satellite observations. First, the impacts of vegetation on SSM retrieval were investigated through physics-based Common Land Model simulations and high accuracy with an overall root mean square error (RMSE) of 0.010 m(3)m(-3) was achieved when the underlying surface was covered by moderate fractional vegetation cover from 0 to 0.6 with a given soil texture. Based on the simulated results, the SSM was then estimated using data from an AmeriFlux site dominated by crops during the growing season in 2018; the results were further verified using in situ measurements, and this showed that the estimated SSM correlated well with in-situ measurements (with an RMSE of 0.078 m(3)m(-3)). A further evaluation of the Meteosat Second Generation (MSG)-derived SSM at the pixel scale was made using the proposed method, and the results revealed an overall acceptable accuracy (RMSE of 0.074 m(3)m(-3) and bias of 0.071 m(3)m(-3)) against the SMAP/Sentinel-1 SSM product at the same spatial resolution of 3 km.

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