Research on optimal spatial interpolation methods for soil organic carbon across spatial scales

Research on optimal spatial interpolation methods for soil organic carbon across spatial scales | 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 Research on optimal spatial interpolation methods for soil organic carbon across spatial scales Tingting Xie, Haijuan Zhao, Honghong Lin, Guokun Chen, Xingwu Duan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7182311/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 Accurate estimation of soil organic carbon (SOC) storage is crucial for global carbon budgeting and climate policy formulation. Spatial interpolation bridges localized measurements with regional estimates, yet method performance variability across spatial scales remains poorly quantified. This study systematically evaluates four widely-used approaches (Ordinary Kriging (OK), Inverse Distance Weighting (IDW), Stepwise Linear Regression (SLR), and Random Forest (RF)) through a multi-scale experiment encompassing national (9.6×10 6 km 2 ), basin (1.03×10 5 km 2 ), catchment (2.9 km 2 ) and field (0.3 km 2 ) scales in China. Key findings reveal: 1) Scale-dependent accuracy shifts with 6.06-fold variation in R 2 (0.16–0.97), where machine learning (RF) outperforms at national/catchment scales (R²=0.37/0.97) while SLR excels in field-scale predictions (R²=0.84); 2) Geostatistical methods (OK) show comparative advantage at basin scales (RMSE = 1.051 vs 1.052–1.116 for others); 3) National-scale carbon stock estimates vary 23% (36.74–45.20 Pg C) across methods, highlighting methodological sensitivity. Spatial analysis demonstrates machine learning (RF) and regression approaches (SLR) better resolve SOC heterogeneity than geostatistical methods. We propose a hierarchical decision matrix informed by relative improvement metrics (RI = 0.08–78.05%), offering spatially-adaptive methodological recommendations to enhance precision in carbon accounting frameworks. These scale-explicit insights advance optimization strategies for SOC mapping in climate governance applications. soil organic carbon spatial interpolation scale dependency carbon budgeting machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarydata.docx 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. 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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-7182311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515950658,"identity":"52bb405f-c733-447b-ad0a-f2d64dc30ce1","order_by":0,"name":"Tingting Xie","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Xie","suffix":""},{"id":515950659,"identity":"c5ccfaa2-6a1e-47c5-97d4-291041afc2f2","order_by":1,"name":"Haijuan Zhao","email":"","orcid":"","institution":"Kunming University of Science and 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