A Spatially-Informed Interpolation Framework for Reconstructing Lake Area Time Series via Semantic Neighborhood Correlation

A Spatially-Informed Interpolation Framework for Reconstructing Lake Area Time Series via Semantic Neighborhood Correlation | 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 A Spatially-Informed Interpolation Framework for Reconstructing Lake Area Time Series via Semantic Neighborhood Correlation Chen Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6437916/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 3 You are reading this latest preprint version Abstract Long-term, high-resolution records of lake surface area are essential for characterizing the spatiotemporal dynamics of inland water bodies. Although Synthetic Aperture Radar has substantially improved water extent detection under adverse conditions, optical remote sensing imagery remains the dominant data source owing to its higher spatial resolution. Nevertheless, optical data are frequently compromised by persistent cloud cover and sensor limitations, leading to substantial observational gaps. To effectively address this challenge, this study introduces a novel spatially-informed interpolation method termed Semantic Neighborhood Correlation-based Interpolation (SNCI), which leverages spatial correlations among hydrologically interconnected lakes to reconstruct missing lake area observations. By explicitly modeling the inherent hydrological and climatic coherence among neighboring lakes, SNCI provides robust, accurate, and scalable interpolations even in the presence of extensive temporal data losses. The method was evaluated on monthly lake area data from 54 lakes in the Wuhan region between 2000 and 2020, using the Global Surface Water dataset, and validated against high-resolution Dynamic World observations. Several representative lakes were analyzed in detail to assess SNCI’s robustness across diverse seasonal and interannual conditions. Compared with polynomial fitting, Random Forest, and Long Short-Term Memory, SNCI consistently achieves lower interpolation errors. In the case of East Lake, SNCI reduces mean absolute error by 50.1% and root mean square error by 28.3% relative to the best-performing baseline. Across all lakes, SNCI demonstrates superior accuracy and correlation, particularly under data-sparse conditions. These results underscore SNCI’s potential to enhance lake area reconstruction accuracy and support broader applications in hydrological modeling, environmental monitoring, and climate impact assessment. Lake area interpolation Remote sensing Spatiotemporal analysis Hydrological monitoring Full Text Supplementary Files Supplement.docx Cite Share Download PDF Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 26 Jun, 2025 Submission checks completed at journal 24 Jun, 2025 First submitted to journal 23 Jun, 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. 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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-6437916","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485820574,"identity":"2c7a252d-733c-4bc2-8365-122982b03915","order_by":0,"name":"Chen Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYNCCCmYI/QGI2diJ0XHgDEQL4wyQFmZitBxsgyhj5gGTBFTLz0g++PjDGWs5+ejDBx/b/Nomz8fMwPjhYw5uLQY30pINDlSkGxueS0s2zu27bQi0kVly5jY8WqRzzCQOnDmcuLGHx0w6t+c2I1ALGzMvHi3ys/O//zjYdrh+Yw//99+WPbftCWphuJ3DBvT+4QR5Hh5gWP24nUhQi8H9Z8YSZ86kG27gYTOW7G24ndzGzNiM1y/yPYcffqiosJaX72F++OHHn9u289ubD374iM9hcOsOAAnGNhCTsYEI9SDrwOr+EKd4FIyCUTAKRhYAANeBU99UqtJMAAAAAElFTkSuQmCC","orcid":"","institution":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-04-13 07:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6437916/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6437916/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-09410-3","type":"published","date":"2025-07-09T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87466826,"identity":"8bb3c4a9-e45c-459f-8f24-4884157eb878","added_by":"auto","created_at":"2025-07-24 07:34:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2747781,"visible":true,"origin":"","legend":"","description":"","filename":"SNCIManuscriptClean.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6437916/v1_covered_e8a8f4b7-d91b-41af-a56f-1f79ae6e2fa4.pdf"},{"id":86790171,"identity":"e930ae94-bad9-4529-9f4a-a9657ae45314","added_by":"auto","created_at":"2025-07-15 14:45:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47594,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-6437916/v1/e3a8d19945e18bb8f8458c32.docx"}],"financialInterests":"","formattedTitle":"A Spatially-Informed Interpolation Framework for Reconstructing Lake Area Time Series via Semantic Neighborhood Correlation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lake area interpolation, Remote sensing, Spatiotemporal analysis, Hydrological monitoring","lastPublishedDoi":"10.21203/rs.3.rs-6437916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6437916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLong-term, high-resolution records of lake surface area are essential for characterizing the spatiotemporal dynamics of inland water bodies. Although Synthetic Aperture Radar has substantially improved water extent detection under adverse conditions, optical remote sensing imagery remains the dominant data source owing to its higher spatial resolution. Nevertheless, optical data are frequently compromised by persistent cloud cover and sensor limitations, leading to substantial observational gaps. To effectively address this challenge, this study introduces a novel spatially-informed interpolation method termed Semantic Neighborhood Correlation-based Interpolation (SNCI), which leverages spatial correlations among hydrologically interconnected lakes to reconstruct missing lake area observations. By explicitly modeling the inherent hydrological and climatic coherence among neighboring lakes, SNCI provides robust, accurate, and scalable interpolations even in the presence of extensive temporal data losses. The method was evaluated on monthly lake area data from 54 lakes in the Wuhan region between 2000 and 2020, using the Global Surface Water dataset, and validated against high-resolution Dynamic World observations. Several representative lakes were analyzed in detail to assess SNCI\u0026rsquo;s robustness across diverse seasonal and interannual conditions. Compared with polynomial fitting, Random Forest, and Long Short-Term Memory, SNCI consistently achieves lower interpolation errors. In the case of East Lake, SNCI reduces mean absolute error by 50.1% and root mean square error by 28.3% relative to the best-performing baseline. Across all lakes, SNCI demonstrates superior accuracy and correlation, particularly under data-sparse conditions. These results underscore SNCI\u0026rsquo;s potential to enhance lake area reconstruction accuracy and support broader applications in hydrological modeling, environmental monitoring, and climate impact assessment.\u003c/p\u003e","manuscriptTitle":"A Spatially-Informed Interpolation Framework for Reconstructing Lake Area Time Series via Semantic Neighborhood Correlation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 14:45:07","doi":"10.21203/rs.3.rs-6437916/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-06-27T02:26:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-24T11:59:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-23T08:17:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed7435af-707d-4fe3-a672-d67d71d40a9c","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-15T14:45:07+00:00","versionOfRecord":{"articleIdentity":"rs-6437916","link":"https://doi.org/10.1038/s41598-025-09410-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-09 15:57:02","publishedOnDateReadable":"July 9th, 2025"},"versionCreatedAt":"2025-07-15 14:45:07","video":"","vorDoi":"10.1038/s41598-025-09410-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-09410-3","workflowStages":[]},"version":"v1","identity":"rs-6437916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6437916","identity":"rs-6437916","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}