{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T03:26:28Z","timestamp":1775705188870,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,17]],"date-time":"2023-12-17T00:00:00Z","timestamp":1702771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Third Xinjiang Scientific Expedition Program","award":["2022xjkk0402"],"award-info":[{"award-number":["2022xjkk0402"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2022xjkk0402"],"award-info":[{"award-number":["2022xjkk0402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs15245768","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T10:04:47Z","timestamp":1702893887000},"page":"5768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor"],"prefix":"10.3390","volume":"15","author":[{"given":"Dewei","family":"Yin","sequence":"first","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Xiaoning","family":"Song","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9256-5644","authenticated-orcid":false,"given":"Xinming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"}]},{"given":"Han","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Yongrong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Yanan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Yanshan Earth Critical Zone and Surface Fluxes Research Station, University of Chinese Academy of Sciences, Beijing 101408, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1002\/hyp.315","article-title":"Improvement of TOPLATS-based discharge predictions through assimilation of ERS-based remotely sensed soil moisture values","volume":"16","author":"Pauwels","year":"2002","journal-title":"Hydrol. 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