{"paper_id":"171dc0cf-c515-4dcf-8ccf-52ee6b388194","body_text":"XCO2 distribution in China based on multi-source satellite data fusion | 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 XCO 2 distribution in China based on multi-source satellite data fusion Xiaolin Wang, Jingjing Ai, Peng Gao, Yujie Cui, Mingkun Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7072225/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 The spatiotemporal distribution of the carbon dioxide column concentration (XCO 2 ) in China is generated by utilizing the high accuracy surface modeling (HASM) data fusion method, where the simulation output of the global earth observing system chemistry model (GEOS-Chem) is used as the driving field while the observation data from the greenhouse gases observing satellite (GOSAT) and orbiting carbon observatory-2 (OCO-2) as the optimal control constraints. The simulation results of the GEOS-Chem model with its grid resolutions of 2°×2.5° and 0.25°×0.3125° are compared with the total carbon column observing network (TCCON) station data, and the mean error (ME) together with the root mean square error (RMSE) at Hefei station decrease by 0.7248 ppm and 0.6901 ppm, respectively, while the ME and RMSE decreasing by 0.0371 ppm and 0.2426 ppm at Xianghe station. Besides, the sensitivity of the sampling weight \\(\\lambda\\) of observation data and the sampling radius to the HASM data fusion accuracy is analyzed, and the optimal values \\(\\lambda =0.6\\) and \\(r=3\\) corresponding to the highest fusion accuracy are obtained through the cross validation. The validation results show that the MAE and RMSE decrease from 1.6282 ppm and 2.3808 ppm to 1.1890 ppm and 1.9377 ppm before and after the HASM data fusion, respectively. The spatial distribution of the monthly XCO 2 after the HASM data fusion is similar to the simulation result from 2017 to 2018, indicating that the HASM data fusion method not only simulates the XCO 2 with high precision, but also ameliorates effectively the spatial distribution of the XCO 2 . HASM GEOS-Chem XCO2 GOSAT OCO-2 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers invited by journal 23 Oct, 2025 Editor assigned by journal 08 Jul, 2025 Submission checks completed at journal 08 Jul, 2025 First submitted to journal 08 Jul, 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. 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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-7072225\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":538867811,\"identity\":\"27b9d1b6-c5fc-4f42-8f8c-2d2658a9b6dc\",\"order_by\":0,\"name\":\"Xiaolin Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qingdao University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaolin\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":538867812,\"identity\":\"fcb046e5-c050-4e81-b3b0-03e382557ed6\",\"order_by\":1,\"name\":\"Jingjing 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The simulation results of the GEOS-Chem model with its grid resolutions of 2\\u0026deg;\\u0026times;2.5\\u0026deg; and 0.25\\u0026deg;\\u0026times;0.3125\\u0026deg; are compared with the total carbon column observing network (TCCON) station data, and the mean error (ME) together with the root mean square error (RMSE) at Hefei station decrease by 0.7248 ppm and 0.6901 ppm, respectively, while the ME and RMSE decreasing by 0.0371 ppm and 0.2426 ppm at Xianghe station. Besides, the sensitivity of the sampling weight \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\lambda\\\\)\\u003c/span\\u003e\\u003c/span\\u003e of observation data and the sampling radius \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003c/span\\u003e to the HASM data fusion accuracy is analyzed, and the optimal values \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\lambda =0.6\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(r=3\\\\)\\u003c/span\\u003e\\u003c/span\\u003e corresponding to the highest fusion accuracy are obtained through the cross validation. The validation results show that the MAE and RMSE decrease from 1.6282 ppm and 2.3808 ppm to 1.1890 ppm and 1.9377 ppm before and after the HASM data fusion, respectively. 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