Optical–SAR Data Fusion and Machine Learning for Soil Erosion Susceptibility Mapping in the Rukuru Basin, Malawi

Optical–SAR Data Fusion and Machine Learning for Soil Erosion Susceptibility Mapping in the Rukuru Basin, Malawi | 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 Optical–SAR Data Fusion and Machine Learning for Soil Erosion Susceptibility Mapping in the Rukuru Basin, Malawi Japhet Khendlo, Isaac Matenda, Tiwonge Baloyi, Chimwemwe Oscar Mbewe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8910155/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 erosion threatens agricultural productivity and sediment-related hazards in Sub-Saharan Africa. This study integrates optical and synthetic aperture radar (SAR) remote sensing with Random Forest machine learning to map soil erosion susceptibility in the South and North Rukuru River Basin, Malawi. Predictor variables, including slope, topographic wetness index, drainage density, NDVI, and SAR VV backscatter, were fused to capture spatial heterogeneity. The integrated model outperformed single-sensor configurations, achieving RMSE = 0.124, MAE = 0.091, R² = 0.872, AUC = 0.97 (95% CI: 0.955–0.983), and confusion matrix metrics of accuracy = 0.914, sensitivity = 0.923, specificity = 0.905, precision = 0.906, and F1-score = 0.914. Optical-only and SAR-only models yielded lower AUCs (0.84 and 0.88), emphasizing the benefit of multi-sensor integration. Results show that 21.4% of the basin is high to very high susceptibility, concentrated in northern uplands with steep slopes (> 15°), while 43.9% falls within low to very low susceptibility, largely in floodplains and vegetated valley bottoms. Land cover change (2000–2025) reveals that 68% of woodland-to-cropland and 61% of shrubland-to-cropland conversions occurred in moderate to high susceptibility zones, increasing mean erosion probability by + 0.32 and + 0.27. The study demonstrates that multi-sensor Random Forest models provide robust, spatially explicit erosion susceptibility predictions, offering essential guidance for targeted soil conservation and sustainable land management in rapidly transforming tropical catchments. Environmental Engineering Environmental Policy Soil erosion susceptibility Random Forest Optical–SAR fusion Remote sensing Land cover change Rukuru Basin Topographic analysis 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-8910155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593371963,"identity":"3b50a85e-8902-4f17-b17a-b0b9d898f988","order_by":0,"name":"Japhet Khendlo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACCQkgwVPAwMDY3gDmE6vFAKil5wCpWhgkEoh0mOTs5mMP3hjYMDDPfJ38mXeHBQN/+wHWzTx4tEjLHEs3nGOQxsA4O3eDMe8ZCQaJMwlst/FpkZPIMZPmMTgM1pLM2wZ05w0Gtts5eLXkf4NomXl2w2GQFnlCWqQlctggWmbwbmwGaTEgpEVyRpqZJNAvPIw9uZsZ57ZJ8BieSWy7/QePFokbyc8k3lTYyBm2n9384W1bnZzc8cPHbs7AowUGeAwboAxgrDYQoQEI5IlTNgpGwSgYBSMRAACeBERXWZe9EQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-0970-0237","institution":"Mzuzu University","correspondingAuthor":true,"prefix":"","firstName":"Japhet","middleName":"","lastName":"Khendlo","suffix":""},{"id":593371964,"identity":"f4c8890b-1704-41d5-9bd1-fdde19416111","order_by":1,"name":"Isaac Matenda","email":"","orcid":"","institution":"Mzuzu University Faculty of Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Matenda","suffix":""},{"id":593371966,"identity":"4ac6fe24-ef00-408e-b86b-b84878fe7c66","order_by":2,"name":"Tiwonge Baloyi","email":"","orcid":"","institution":"Mzuzu University Faculty of Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tiwonge","middleName":"","lastName":"Baloyi","suffix":""},{"id":593371967,"identity":"17894048-937a-47bb-b56c-986a5260b8d7","order_by":3,"name":"Chimwemwe Oscar Mbewe","email":"","orcid":"","institution":"Mzuzu University Faculty of Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chimwemwe","middleName":"Oscar","lastName":"Mbewe","suffix":""}],"badges":[],"createdAt":"2026-02-18 14:14:06","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8910155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8910155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103506345,"identity":"f5d5392c-4ff1-4667-b525-d7f8b4ebbb52","added_by":"auto","created_at":"2026-02-26 13:35:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":801247,"visible":true,"origin":"","legend":"","description":"","filename":"IntegratingOpticalandSARRemoteSensing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8910155/v1_covered_aecc590d-bcc9-4e04-bccc-93e1fb348e1a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eOptical–SAR Data Fusion and Machine Learning for Soil Erosion Susceptibility Mapping in the Rukuru Basin, Malawi\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Mzuzu University","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 erosion susceptibility, Random Forest, Optical–SAR fusion, Remote sensing, Land cover change, Rukuru Basin, Topographic analysis","lastPublishedDoi":"10.21203/rs.3.rs-8910155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8910155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil erosion threatens agricultural productivity and sediment-related hazards in Sub-Saharan Africa. This study integrates optical and synthetic aperture radar (SAR) remote sensing with Random Forest machine learning to map soil erosion susceptibility in the South and North Rukuru River Basin, Malawi. Predictor variables, including slope, topographic wetness index, drainage density, NDVI, and SAR VV backscatter, were fused to capture spatial heterogeneity. The integrated model outperformed single-sensor configurations, achieving RMSE\u0026thinsp;=\u0026thinsp;0.124, MAE\u0026thinsp;=\u0026thinsp;0.091, R\u0026sup2; = 0.872, AUC\u0026thinsp;=\u0026thinsp;0.97 (95% CI: 0.955\u0026ndash;0.983), and confusion matrix metrics of accuracy\u0026thinsp;=\u0026thinsp;0.914, sensitivity\u0026thinsp;=\u0026thinsp;0.923, specificity\u0026thinsp;=\u0026thinsp;0.905, precision\u0026thinsp;=\u0026thinsp;0.906, and F1-score\u0026thinsp;=\u0026thinsp;0.914. Optical-only and SAR-only models yielded lower AUCs (0.84 and 0.88), emphasizing the benefit of multi-sensor integration. Results show that 21.4% of the basin is high to very high susceptibility, concentrated in northern uplands with steep slopes (\u0026gt;\u0026thinsp;15\u0026deg;), while 43.9% falls within low to very low susceptibility, largely in floodplains and vegetated valley bottoms. Land cover change (2000\u0026ndash;2025) reveals that 68% of woodland-to-cropland and 61% of shrubland-to-cropland conversions occurred in moderate to high susceptibility zones, increasing mean erosion probability by +\u0026thinsp;0.32 and +\u0026thinsp;0.27. The study demonstrates that multi-sensor Random Forest models provide robust, spatially explicit erosion susceptibility predictions, offering essential guidance for targeted soil conservation and sustainable land management in rapidly transforming tropical catchments.\u003c/p\u003e","manuscriptTitle":"Optical–SAR Data Fusion and Machine Learning for Soil Erosion Susceptibility Mapping in the Rukuru Basin, Malawi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 03:27:48","doi":"10.21203/rs.3.rs-8910155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"d0d56597-c64c-4bdf-ad38-cb8791de2462","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63141769,"name":"Environmental Engineering"},{"id":63141770,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2026-02-24T03:27:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 03:27:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8910155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8910155","identity":"rs-8910155","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}