Digital Soil Subgroup Mapping in Semi-Arid Mountainous Regions Using Multi-Source Environmental Covariates

Digital Soil Subgroup Mapping in Semi-Arid Mountainous Regions Using Multi-Source Environmental Covariates | 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 Article Digital Soil Subgroup Mapping in Semi-Arid Mountainous Regions Using Multi-Source Environmental Covariates Maryam Osat, Shahrokh Fatehi, Mohammad Jamshidi, Mohammad Hossein Sedri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7727051/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Digital Soil Mapping (DSM) provides a modern framework for assessing soil variability in complex landscapes. This study evaluated the performance of Random Forest (RF) and Support Vector Machine (SVM) classifiers for predicting soil subgroups in a semi-arid mountainous area of 3,000 ha, based on 81 soil profiles and multiple environmental covariates. Two predictor sets were tested: (i) topographic indices and spectral data, and (ii) an extended set including subsurface soil properties. Results showed that both models benefited from the integration of soil profile variables, with RF outperforming SVM. The most influential predictors were soil depth, clay content of second horizon, and a sentinel carbonate index derived from satellite data, which capture key pedogenic processes in semi-arid conditions. The final soil map indicated that Typic Calcixerepts and Lithic Xerorthents were the most widespread subgroups. Overall, the study demonstrates that combining pedological information with environmental covariates significantly improves DSM performance, offering more reliable soil maps for land evaluation and sustainable management in mountainous semi-arid regions. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Machine Learning Soil Covariates Soil Classification Topography Remote Sensing Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.rar Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor invited by journal 13 Oct, 2025 Editor assigned by journal 13 Oct, 2025 Submission checks completed at journal 12 Oct, 2025 First submitted to journal 04 Oct, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7727051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":535312710,"identity":"c1f9169e-129e-4177-8010-3dee1b535027","order_by":0,"name":"Maryam 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