Soil moisture estimation from satellite-derived temporal signals in the thermal domain with an improved parameterization scheme

文献类型： 外文期刊

第一作者： Geng, Yun-Jing

作者： Geng, Yun-Jing;Song, Xiaoning;Geng, Yun-Jing;Song, Xiaoning;Leng, Pei;Kasim, Abba Aliyu;Li, Zhao-Liang;Kasim, Abba Aliyu;Li, Zhao-Liang

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关键词： Soil moisture; Land surface temperature; Temporal information; Numerical simulation; Geostationary satellite data

期刊名称：JOURNAL OF HYDROLOGY （ 影响因子：6.3； 五年影响因子：6.9 ）

ISSN： 0022-1694

年卷期： 2025 年 662 卷

页码：

收录情况： SCI

摘要： Soil moisture (SM) is an indispensable variable in water, energy, and carbon cycles. This study aimed to develop a practical method for estimating SM from the perspective of intra-day temporal signals of land surface temperature (LST) using an improved parameterization scheme over vegetated conditions. The original method was inspired by the surface energy budget principle, in which SM plays a dominant role in the diurnal LST cycle over bare soils. According to a comprehensive dataset from the physics-based Common Land Model simulation under different underlying surface and atmospheric conditions, it was found that the parameters to be calibrated in the SM retrieval method at 44 AmeriFlux sites across the continental US satisfied the normal distribution conditions. Based on this finding, a new concept of spatiotemporal-invariant parameters was first defined, and these parameters were subsequently determined using Common Land Model simulations with historical meteorological observations. This has significantly enhanced the practicability of the SM retrieval method at the regional scale with satellite data, as no real-time auxiliary data are required for individual parameterization/calibration when dealing with each image scenario, as in other traditional SM retrieval methods. Finally, using Meteosat Second Generation satellite data, the proposed SM retrieval method was comprehensively evaluated at two SM networks (TWENTE in the Netherlands and REMEDHUS in Spain) dominated by different land cover types and climate patterns between 2015 and 2020. Compared to in-situ SM measurements, the proposed method reveals reasonable accuracy, with an average root mean square error of similar to 0.05 m(3)m(-3) at the two networks, indicating promising prospects for estimating SM from geostationary satellite data in future developments.

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