{"paper_id":"10294ada-7bfa-48a1-b0ee-78bdcbfa5c22","body_text":"Soil Moisture Retrieval with High Spatial-temporal Resolution by Fusion of CYGNSS and SAR Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Soil Moisture Retrieval with High Spatial-temporal Resolution by Fusion of CYGNSS and SAR Data Xin Chang, Qi Wang, Jiaojiao Sun, Zuozhu Tan, Dawei Li, Kegen Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5993379/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract As the probability and intensity of global drought events continue to increase, soil moisture is an important basis for drought monitoring and drought assessment, and the need to accurately obtain soil moisture distribution information with high spatial-temporal resolution is becoming extremely important. CYGNSS data based on spaceborne GNSS-R has the advantage of high temporal resolution, while SAR data can provide information on surface features with high spatial resolution, and the combination of the two provides favourable conditions for obtaining soil moisture with high spatial-temporal resolution. This paper proposes a soil moisture retrieval method with high spatial-temporal resolution by the fusion of spaceborne GNSS-R (CYGNSS) and SAR (Sentinel-1) data. This method constructs a function relationship between surface reflectivity of spaceborne GNSS-R and backscattering coefficient of SAR, with the aim of preparing for fusion of CYGNSS and Sentinel-1. By fusing sentinel-1 data, a two-layer machine learning framework based on CYGNSS data is constructed to retrieve the soil moisture with high spatial-temporal resolution, and the retrieval results are compared with the measured data and soil moisture products of SMAP. The results indicate that, the surface reflectivity of spaceborne GNSS-R shows an approximate linear relationship with the backscattering coefficient of SAR. The constructed first-layer framework is able to supplement CYGNSS surface reflectivity data, and verifies the feasibility of converting backscattering coefficients of SAR to the CYGNSS surface reflectivity. The soil moisture retrieval by the two-layer framework method in this paper is comparable to the soil moisture product of SMAP in terms of retrieval accuracy (average ubRMSE = 0.070cm 3 /cm 3 , average R = 0.65) at the same spatial resolution (3 km), and the temporal resolution is improved by 3.9 times on average, which confirms the feasibility of soil moisture retrieval by CYGNSS at 3 km spatial resolution. Soil moisture CYGNSS SAR Global Navigation Satellite System reflectometry (GNSS-R) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Jan, 2026 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers invited by journal 18 Mar, 2025 Editor assigned by journal 23 Feb, 2025 Submission checks completed at journal 11 Feb, 2025 First submitted to journal 09 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5993379\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":414243918,\"identity\":\"87f8e83d-91f7-492b-96bd-20daa334a307\",\"order_by\":0,\"name\":\"Xin Chang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Wuhan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xin\",\"middleName\":\"\",\"lastName\":\"Chang\",\"suffix\":\"\"},{\"id\":414243919,\"identity\":\"8a25464c-4a73-426b-bd4a-8c1df3b96468\",\"order_by\":1,\"name\":\"Qi 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resolution is becoming extremely important. CYGNSS data based on spaceborne GNSS-R has the advantage of high temporal resolution, while SAR data can provide information on surface features with high spatial resolution, and the combination of the two provides favourable conditions for obtaining soil moisture with high spatial-temporal resolution. This paper proposes a soil moisture retrieval method with high spatial-temporal resolution by the fusion of spaceborne GNSS-R (CYGNSS) and SAR (Sentinel-1) data. This method constructs a function relationship between surface reflectivity of spaceborne GNSS-R and backscattering coefficient of SAR, with the aim of preparing for fusion of CYGNSS and Sentinel-1. By fusing sentinel-1 data, a two-layer machine learning framework based on CYGNSS data is constructed to retrieve the soil moisture with high spatial-temporal resolution, and the retrieval results are compared with the measured data and soil moisture products of SMAP. The results indicate that, the surface reflectivity of spaceborne GNSS-R shows an approximate linear relationship with the backscattering coefficient of SAR. The constructed first-layer framework is able to supplement CYGNSS surface reflectivity data, and verifies the feasibility of converting backscattering coefficients of SAR to the CYGNSS surface reflectivity. 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