Enhancing SWOT Water Surface Detection inWetlands Using LSTM-Based Temporal Modeling

Enhancing SWOT Water Surface Detection inWetlands Using LSTM-Based Temporal Modeling | 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 Enhancing SWOT Water Surface Detection inWetlands Using LSTM-Based Temporal Modeling shayan shirafkan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8213488/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Surface Water and Ocean Topography (SWOT) mission mission provides unprecedented high-resolution water surface observations; however, its pixel cloud (PIXC) data often suffer from incomplete water boundaries in certain acquisition epochs, especially over complex wetland environments. To address this limitation, we propose a deep learning (DL)-based classification framework that integrates SWOT radar observations with optical features from Sentinel-2 imagery to improve water body delineation.Initially, we analyzed various features from SWOT PIXC and Sentinel-2 bands and indices. Using principal component analysis (PCA), we selected four key variables: Water Fraction, Coherent Power, and Interferogram 1 from SWOT, and the Modified Normalized Difference Water Index (MNDWI) index from Sentinel-2. A two-layer LSTM architecture (16 and 32 units) was optimized for sequence modeling using five temporal snapshots from the Anzali Wetland. The trained model was then generalized to classify surface water pixels across five investigated Iranian wetlands.Our approach successfully reconstructed missing water shapes in incomplete SWOT epochs, enhancing the spatial continuity of water extent. Validation using Sentinel-1 GRD data confirmed the reliability of our results. These findings demonstrate that fusing temporal learning with multi-sensor data effectively mitigates limitations in single-source observations, offering a scalable solution for surface water monitoring. SWOT PIXC data Sentinel Wetlands GRD Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8213488","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576429293,"identity":"5acc4b4d-9c6e-42d8-bd54-41be5019fccb","order_by":0,"name":"shayan 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Modeling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SWOT, PIXC data, Sentinel, Wetlands, GRD","lastPublishedDoi":"10.21203/rs.3.rs-8213488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8213488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Surface Water and Ocean Topography (SWOT) mission mission provides unprecedented high-resolution water surface observations; however, its pixel cloud (PIXC) data often suffer from incomplete water boundaries in certain acquisition epochs, especially over complex wetland environments. To address this limitation, we propose a deep learning (DL)-based classification framework that integrates SWOT radar observations with optical features from Sentinel-2 imagery to improve water body delineation.Initially, we analyzed various features from SWOT PIXC and Sentinel-2 bands and indices. Using principal component analysis (PCA), we selected four key variables: Water Fraction, Coherent Power, and Interferogram 1 from SWOT, and the Modified Normalized Difference Water Index (MNDWI) index from Sentinel-2. A two-layer LSTM architecture (16 and 32 units) was optimized for sequence modeling using five temporal snapshots from the Anzali Wetland. The trained model was then generalized to classify surface water pixels across five investigated Iranian wetlands.Our approach successfully reconstructed missing water shapes in incomplete SWOT epochs, enhancing the spatial continuity of water extent. Validation using Sentinel-1 GRD data confirmed the reliability of our results. These findings demonstrate that fusing temporal learning with multi-sensor data effectively mitigates limitations in single-source observations, offering a scalable solution for surface water monitoring.\u003c/p\u003e","manuscriptTitle":"Enhancing SWOT Water Surface Detection inWetlands Using LSTM-Based Temporal Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 16:20:20","doi":"10.21203/rs.3.rs-8213488/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8485618d-b4be-4f6d-8ccb-6ab2d8445ee5","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T13:10:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 16:20:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8213488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8213488","identity":"rs-8213488","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}