Modeling soil organic carbon changes using signal-to-noise analysis: a case study using European soil survey datasets

Modeling soil organic carbon changes using signal-to-noise analysis: a case study using European soil survey datasets | 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 Modeling soil organic carbon changes using signal-to-noise analysis: a case study using European soil survey datasets Xuemeng Tian, Sytze de Bruin, Florian Schneider, Martin Herold, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7308469/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 Soil organic carbon (SOC) is a key indicator of soil health and a crucial component for assessing climate mitigation, making its reliable monitoring increasingly relevant. While digital soil mapping (DSM) using machine learning (ML) and Earth observation (EO) data enables time series of spatially explicit SOC predictions, detecting temporal soil carbon stock changes remains challenging and raises the question whether multi-temporal SOC measurements are sufficient to quantify changes with confidence. This study introduces a model-based signal-to-noise ratio (SNR) framework to assess the detectability of SOC change using both model-then-derive and derive-then-model approaches. We define SNR as the ratio of predicted SOC change to its modeled uncertainty. This enables the evaluation of change modeling reliability at both pixel and aggregated spatial levels. Applied to repeated SOC observations from the LUCAS soil survey at the European scale, the framework assesses the reliability of SOC change modeling across multiple land cover types using Random Forest (RF) and Quantile Regression Forests (QRF). At the site level prediction accuracy was poor, and SNR values were consistently low (<1). This highlights the limitations of current data for modeling SOC dynamics. However, spatial aggregation improved the SNR, supporting SOC change assessments at broader scales. SNR offers a practical diagnostic of model confidence, with a weak correlation to observation-based error, reflecting its role as an internal metric rather than a direct measure of accuracy. We advocate for routine SNR reporting to enhance the transparency and credibility of DSM-based SOC change monitoring while EO datasets enhance and ground-measurement surveys expand in time and quality. Soil organic carbon Machine learning Digital soil mapping Signal-to-noise ratio Change detection Full Text Additional Declarations The authors declare no competing interests. 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. 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-7308469","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496521563,"identity":"f827f36c-f4f4-48dc-86e9-be8a7df4e3d3","order_by":0,"name":"Xuemeng Tian","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Xuemeng","middleName":"","lastName":"Tian","suffix":""},{"id":496521564,"identity":"822bdfb1-ce36-41bf-9805-09e06a9dde6e","order_by":1,"name":"Sytze de Bruin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sytze","middleName":"","lastName":"de Bruin","suffix":""},{"id":496521565,"identity":"61025943-3b30-47e9-9d83-4ad3f0989b06","order_by":2,"name":"Florian Schneider","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Florian","middleName":"","lastName":"Schneider","suffix":""},{"id":496521566,"identity":"521ff229-7be0-4ea2-a7fb-35c69bdc6d97","order_by":3,"name":"Martin Herold","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Herold","suffix":""},{"id":496521567,"identity":"f8bbec2e-55eb-444d-9d2b-7bd535d2dd4a","order_by":4,"name":"Kirsten de Beurs","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kirsten","middleName":"","lastName":"de Beurs","suffix":""}],"badges":[],"createdAt":"2025-08-06 10:08:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7308469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7308469/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88508241,"identity":"9643c2b5-cb41-488d-bee1-4e8237b0c8cd","added_by":"auto","created_at":"2025-08-07 07:41:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2892386,"visible":true,"origin":"","legend":"","description":"","filename":"SNRanalysisonSOCchangemodeling.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7308469/v1_covered_a0a751eb-8690-4b21-bf90-89c28c6e2bbb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eModeling soil organic carbon changes using signal-to-noise analysis: a case study using European soil survey datasets\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"OpenGeoHub","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":"Soil organic carbon; Machine learning; Digital soil mapping; Signal-to-noise ratio; Change detection","lastPublishedDoi":"10.21203/rs.3.rs-7308469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7308469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil organic carbon (SOC) is a key indicator of soil health and a crucial component for assessing climate mitigation, making its reliable monitoring increasingly relevant. 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