Machine Learning-based Multi-scale Dynamics of Terrestrial Carbon Fluxes and Their Environmental Drivers Along the U.S. East Coast

Machine Learning-based Multi-scale Dynamics of Terrestrial Carbon Fluxes and Their Environmental Drivers Along the U.S. East Coast | 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 Machine Learning-based Multi-scale Dynamics of Terrestrial Carbon Fluxes and Their Environmental Drivers Along the U.S. East Coast Jie Wang, RunBin Hu, Can Zhang, Shengqi Wang, Haiyang Zhang, Yixuan Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8802588/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 With global climate change intensifying, the carbon cycle and its related processes have become a central topic in ecological research. In this study, a large-scale carbon flux estimation model for the U.S. East Coast was developed based on long-term eddy covariance observations. Through this model, carbon flux characteristics and their spatiotemporal patterns across different ecosystem types in the region were analyzed over the past two decades. By integrating correlation analysis and the Geodetector method, the roles of multiple environmental drivers in carbon flux estimation were elucidated. Subsequently, models for gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP) were constructed using four machine learning algorithms: random forest (RF), artificial neural network (ANN), support vector regression (SVR), and extreme gradient boosting (XGBoost). The results indicate that: (1) The combination of eight factors, including T2M, VPD, SSRD, EVI, LSWI, LAI H, EVAVT, and DEM, exhibited the most accurate and stable performance in carbon flux estimation. Under identical input combinations, the RF model achieved the highest accuracy for GPP, NEP, and ER estimation. (2) The verification accuracy of GPP, ER, and NEP achieved R 2 values of 0.88, 0.81, and 0.55, respectively. These accuracies markedly outperform those of existing carbon-flux products such as FLUXCOM, which show lower R 2 values of 0.61, 0.57, and 0.28. (3) The analysis of environmental variable importance reveals that EVI is the most important variations in carbon fluxes across all ecosystems, underscoring that vegetation growth status is the most critical driver of carbon exchange processes. FLUXNET Net ecosystem productivity (NEP) random forest (RF) remote sensing Terrestrial Ecosystems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers agreed at journal 22 Feb, 2026 Reviewers invited by journal 18 Feb, 2026 Editor assigned by journal 18 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 05 Feb, 2026 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-8802588","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595185053,"identity":"ef4ed141-d3a0-4c86-8229-5a556ca6d276","order_by":0,"name":"Jie 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