Crop Classification in Uzbekistan Using Random Forest: Integrating Sentinel-1 SAR and Sentinel-2 Optical Data with Ground-Truth Validation

Crop Classification in Uzbekistan Using Random Forest: Integrating Sentinel-1 SAR and Sentinel-2 Optical Data with Ground-Truth Validation | 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 Crop Classification in Uzbekistan Using Random Forest: Integrating Sentinel-1 SAR and Sentinel-2 Optical Data with Ground-Truth Validation Azad Rasul This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6822402/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Sep, 2025 Read the published version in Remote Sensing in Earth Systems Sciences → Version 1 posted 7 You are reading this latest preprint version Abstract This study investigates the effectiveness of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery for crop classification using the Random Forest (RF) algorithm. We aim to assess the potential of combining these two datasets for improved classification accuracy of crop types and land cover. Sentinel-1 SAR data, which provides valuable information on surface roughness and backscatter, was applied to classify crops in an agricultural area. However, results showed that while Sentinel-1 was effective for some crop types, it struggled to distinguish rice and maize from cotton, which exhibited similar backscatter characteristics. In contrast, Sentinel-2 optical data, leveraging its rich spectral bands, showed a significant improvement in class separability, particularly for crops like cotton, fallow, and other. Combining both Sentinel-1 and Sentinel-2 data resulted in a notable enhancement in classification performance, with higher overall accuracy compared to the use of each sensor individually. The RF classifier, applied to the multi-sensor data, demonstrated robust performance with an overall accuracy of 0.98 and a Kappa coefficient of 0.96. This study highlights the complementary nature of SAR and optical data and their potential for enhancing crop classification accuracy. The findings underscore the importance of using multi-sensor datasets for accurate agricultural monitoring, offering valuable insights for land management, crop monitoring, and decision-making in precision agriculture. Sentinel-1 SAR Sentinel-2 Optical Imagery Crop Classification Random Forest Algorithm Remote Sensing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Sep, 2025 Read the published version in Remote Sensing in Earth Systems Sciences → Version 1 posted Editorial decision: Revision requested 12 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 04 Jun, 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. We do this by developing innovative software and high quality services for the global research community. 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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-6822402","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471272041,"identity":"0c00a23d-7409-4d98-a703-c2773e9fed54","order_by":0,"name":"Azad Rasul","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYJACCTDJznwAJmAARoS1MLMlkKyFB64Mv3r+/sMPbzDusbM3OMzz+cPHPXZ5DOzN2yQYCqxx23DgmLEFw7PkxA2HebdJzniWXMzAc6xMgsEgHbc1BxvMJBgOMCcYALUw8xxgTmyQyAGKGBzGqUP+MPs3oJZ6kMMef+Y5UJ/YIP8GvxaDYzwgWw4zbjjMwyDNc+Aw0BYe/FoMz/AUWyQcOJ448zCbmeQMIKONJw0ogscvcuePb7zx4UC1Pd/x5scfgIzEfvbDQJE/uEMMDBKQOWwQEWb8WrABMrSMglEwCkbBcAUAK+ZPac+R4vsAAAAASUVORK5CYII=","orcid":"","institution":"Soran University","correspondingAuthor":true,"prefix":"","firstName":"Azad","middleName":"","lastName":"Rasul","suffix":""}],"badges":[],"createdAt":"2025-06-04 16:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6822402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6822402/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41976-025-00248-x","type":"published","date":"2025-09-10T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91359051,"identity":"e7c6e9a7-3362-4361-89eb-1a799238d86f","added_by":"auto","created_at":"2025-09-15 16:04:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1240327,"visible":true,"origin":"","legend":"","description":"","filename":"SummerCropClassificationUzbakistan04062025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6822402/v1_covered_972f1a95-bf68-4e04-9dd0-6c5a29213fcb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Crop Classification in Uzbekistan Using Random Forest: Integrating Sentinel-1 SAR and Sentinel-2 Optical Data with Ground-Truth Validation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"remote-sensing-in-earth-systems-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rses","sideBox":"Learn more about [Remote Sensing in Earth Systems Sciences](https://link.springer.com/journal/41976)","snPcode":"41976","submissionUrl":"https://submission.nature.com/new-submission/41976/3","title":"Remote Sensing in Earth Systems Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sentinel-1 SAR, Sentinel-2 Optical Imagery, Crop Classification, Random Forest Algorithm, Remote Sensing","lastPublishedDoi":"10.21203/rs.3.rs-6822402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6822402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the effectiveness of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery for crop classification using the Random Forest (RF) algorithm. 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