Construction of a 0.01° Monthly Seamless XCO₂ Dataset over China: Based on a Temporally Adaptive Forest Model

Construction of a 0.01° Monthly Seamless XCO₂ Dataset over China: Based on a Temporally Adaptive Forest Model | 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 data-descriptor Construction of a 0.01° Monthly Seamless XCO₂ Dataset over China: Based on a Temporally Adaptive Forest Model Wenkai Zhang, Xi Chen, Li Duan, Shiran Song, Qian Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8385745/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 High-resolution column-averaged carbon dioxide (XCO₂) data are of significant importance for accurately characterizing regional carbon emission patterns and constraining carbon flux processes, particularly with urgent demand in urban and regional carbon management practices. However, most existing XCO₂ products are based on static modeling assumptions, making it difficult to overcome concept drift in long-term time series data. This results in insufficient reliability at fine regional scales and an inability to effectively capture the non-stationary characteristics of the time series. To address this, this study integrates OCO-2/3 observations with multi-source environmental data and proposes a Temporal Adaptive Forest model. This model employs time decomposition based on the piecewise stationary approximation theory and adopts a feature-space adaptive alignment strategy. Through annual training and a dynamic one-hot encoding mechanism, it effectively mitigates the modeling challenges posed by temporal non-stationarity and enables dynamic diagnosis of driving factors. Based on this model, a seamless monthly XCO₂ dataset covering China with a spatial resolution of 0.01° was constructed. Independent validation results from TCCON stations indicate that this dataset can provide crucial data support for precise emission source identification, separation of carbon sources and sinks, and fine-scale characterization of regional carbon emission patterns. OCO-2/3 XCO₂ Timing adaptive framework Carbon Sources and Sinks 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. 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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-8385745","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":580926573,"identity":"e4113ab1-e50e-4ff6-a2b9-260fb96b1596","order_by":0,"name":"Wenkai Zhang","email":"","orcid":"","institution":"Xinjiang Institute of Ecology and Geography","correspondingAuthor":false,"prefix":"","firstName":"Wenkai","middleName":"","lastName":"Zhang","suffix":""},{"id":580926574,"identity":"1fb39506-ad47-4401-bba8-1669923d4e74","order_by":1,"name":"Xi Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACCRBhYANmG5OiJY1kLQyHwSQzUTr4Zzcfe8xTcD5avoHHoLiAwU6egf3sAfyW3DmWbjjD4HbuhgNsCcYzGJING3jyEvBqMZDIMZP4ANLCwHzAmIeBOYFBgseAgJb8bxIJBudy5zcwNgC11BOjJYcNaMuB3IYDYFsOE9YicSPNTHKGQXLuhsMgvxgcN2zjycGvhX9G8jNpnj92ufPbe8yMCyqq5fnZz+DXggDMDGwGDEDFbESqh2h6QIrqUTAKRsEoGDkAALLYONZ2Mz8rAAAAAElFTkSuQmCC","orcid":"","institution":"Xinjiang Institute of Ecology and Geography","correspondingAuthor":true,"prefix":"","firstName":"Xi","middleName":"","lastName":"Chen","suffix":""},{"id":580926575,"identity":"c9445936-e009-4d02-a2a0-005238dd9d5c","order_by":2,"name":"Li Duan","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Duan","suffix":""},{"id":580926576,"identity":"f4653000-ec4c-49e3-b736-a495b8558280","order_by":3,"name":"Shiran Song","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shiran","middleName":"","lastName":"Song","suffix":""},{"id":580926577,"identity":"02d20971-521a-4818-8beb-3e638326ec14","order_by":4,"name":"Qian Zhou","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-12-17 12:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8385745/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8385745/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101284384,"identity":"019d6f05-3384-4b31-831f-c5b802c671a9","added_by":"auto","created_at":"2026-01-28 06:11:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1280275,"visible":true,"origin":"","legend":"","description":"","filename":"Constructionofa0.01MonthlySeamlessXCODatasetoverChinaBasedonaTemporallyAdaptiveForestModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8385745/v1_covered_3e2464a9-e612-4cfa-bba7-d4ab69f4f640.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of a 0.01° Monthly Seamless XCO₂ Dataset over China: Based on a Temporally Adaptive Forest Model","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":"OCO-2/3, XCO₂, Timing adaptive framework, Carbon Sources and Sinks","lastPublishedDoi":"10.21203/rs.3.rs-8385745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8385745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-resolution column-averaged carbon dioxide (XCO₂) data are of significant importance for accurately characterizing regional carbon emission patterns and constraining carbon flux processes, particularly with urgent demand in urban and regional carbon management practices. However, most existing XCO₂ products are based on static modeling assumptions, making it difficult to overcome concept drift in long-term time series data. This results in insufficient reliability at fine regional scales and an inability to effectively capture the non-stationary characteristics of the time series. To address this, this study integrates OCO-2/3 observations with multi-source environmental data and proposes a Temporal Adaptive Forest model. This model employs time decomposition based on the piecewise stationary approximation theory and adopts a feature-space adaptive alignment strategy. Through annual training and a dynamic one-hot encoding mechanism, it effectively mitigates the modeling challenges posed by temporal non-stationarity and enables dynamic diagnosis of driving factors. Based on this model, a seamless monthly XCO₂ dataset covering China with a spatial resolution of 0.01\u0026deg; was constructed. 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