SMAP Soil Moisture Simulation via GRU-ViT Model and Residual Learning

SMAP Soil Moisture Simulation via GRU-ViT Model and Residual Learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 20 January 2026 V1 Latest version Share on SMAP Soil Moisture Simulation via GRU-ViT Model and Residual Learning Authors : Yunjun Zhan , Senrong Wang , Yan Yan [email protected] , Yu Zhao , Jiejun Huang , Yongsi Luo , and Xueting Wang Authors Info & Affiliations https://doi.org/10.22541/au.176892133.38491577/v1 133 views 59 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Obtaining high-precision soil moisture data is paramount for climate research, early warning of hydrological events, and safeguarding food security. However, achieving high-quality, large-scale soil moisture monitoring remains challenging due to the scarcity of in-situ measurement data. To address this, we propose a two-stage learning framework. In the first stage, we constructed a GRU-ViT model utilizing 12 environmental factors and SMAP L4 SM to generate daily soil moisture time-series products for the Loess Plateau spanning 2017 to 2023. In the second stage, a residual learning method based on in-situ data was employed to correct systematic biases in the initial products. Results indicate that the final soil moisture dataset, derived from GRU-ViT modeling and subsequent residual learning, achieves superior accuracy and reliability, with the R value improving to 0.792 and the error reducing to 0.024 m 3 / m 3 . This proposed two-stage simulation strategy not only offers an effective solution for regions with sparse ground stations but also provides a scientific basis for refined regional water resource management and disaster prevention and mitigation. Supplementary Material File (manuscript0119.docx) Download 3.34 MB Information & Authors Information Version history V1 Version 1 20 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning in-situ data residual learning smap soil moisture Authors Affiliations Yunjun Zhan Wuhan University of Science and Technology School of Resources and Environmental Engineering View all articles by this author Senrong Wang Wuhan University of Science and Technology School of Resources and Environmental Engineering View all articles by this author Yan Yan [email protected] State Key Laboratory of Urban and Regional Ecology View all articles by this author Yu Zhao State Key Laboratory of Urban and Regional Ecology View all articles by this author Jiejun Huang Wuhan University of Science and Technology School of Resources and Environmental Engineering View all articles by this author Yongsi Luo Wuhan University of Science and Technology School of Resources and Environmental Engineering View all articles by this author Xueting Wang Wuhan University of Science and Technology School of Resources and Environmental Engineering View all articles by this author Metrics & Citations Metrics Article Usage 133 views 59 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yunjun Zhan, Senrong Wang, Yan Yan, et al. SMAP Soil Moisture Simulation via GRU-ViT Model and Residual Learning. Authorea . 20 January 2026. DOI: https://doi.org/10.22541/au.176892133.38491577/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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