Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor

文献类型： 外文期刊

第一作者： Yin, Dewei

作者： Yin, Dewei;Song, Xiaoning;Guo, Han;Zhang, Yanan;Yin, Dewei;Song, Xiaoning;Guo, Han;Zhang, Yanan;Zhu, Xinming;Zhang, Yongrong

作者机构： Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China;Univ Chinese Acad Sci, Yanshan Earth Crit Zone & Surface Fluxes Res Stn, Beijing 101408, Peoples R China;Kunming Univ Sci & Technol, Fac Land Resources Engn, Kunming 650093, Peoples R China;Chinese Acad Agr Sci, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China

关键词： soil moisture; spatial downscaling; variability; OPGD; Heihe River Basin

期刊名称：REMOTE SENSING （ 2022影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 24 期

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收录情况： SCI

摘要： Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors.

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