Enhancing Crop Classification through Remote Sensing: Landsat 8-9 Fusion and Feature Selection

Enhancing Crop Classification through Remote Sensing: Landsat 8-9 Fusion and Feature Selection | 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 Enhancing Crop Classification through Remote Sensing: Landsat 8-9 Fusion and Feature Selection Zeeshan Ramzan, Nisar Ahmed, Qurat-ul-Ain Akram, Shahzad Asif, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5710241/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 Remote sensing offers a highly effective method for obtaining accurate information on total cropped area and crop types. The study focuses on crop cover identification for irrigated regions of Central Punjab. Data collection was executed in two stages: the first involved identifying and geocoding six target crops through field surveys conducted in January and February 2023. The second stage involved acquiring Landsat 8-9 imagery for each geocoded field to construct a labeled dataset. The satellite imagery underwent extensive pre-processing, including radiometric calibration for reflectance values, atmospheric correction, and georeferencing verification to ensure consistency within a common coordinate system. Subsequently, image fusion techniques were applied to combine spectral bands from both Landsat 8 and 9, creating a composite image with enhanced spectral information, followed by contrast enhancement. During data acquisition , farmers were interviewed, and fields were meticulously mapped using GPS instruments, resulting in a comprehensive dataset of 50,835 data points. This dataset facilitated the extraction of vegetation indices such as NDVI, SAVO, RECI, and NDRE. These indices, along with raw reflectance values, were utilized for classification modeling using conventional classifiers, ensemble learning, and artificial neural networks. Additionally, a feature selection approach was incorporated to identify the optimal feature set for classification learning. This study demonstrates the effectiveness of combining remote sensing data and 1 advanced modeling techniques to improve crop classification accuracy in irrigated agricultural regions. crop cover identification remote sensing classification vegetation indices landsat Full Text Additional Declarations No competing interests reported. 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-5710241","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398890874,"identity":"0430fb56-13aa-4d77-b560-15c94c4bfb36","order_by":0,"name":"Zeeshan Ramzan","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zeeshan","middleName":"","lastName":"Ramzan","suffix":""},{"id":398890879,"identity":"a59bb457-bffe-4c92-b27c-e880f8d039ad","order_by":1,"name":"Nisar Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACZjB5AIgTQAwbBgYJErWkEaGFAVXLYcJazNnZH36uqLkTzc+e+/BzQc35xP7ZzQcfMNTYROPSYtnMYyx55tiz3Jk9z42lZxy7nTjjzrFkA4ZjabkNOLQYHOZhkGxgO5y74UYagzQP2+3Ehhs5ZhKMDYfxaGF//LPh3+Hc/TfSmH/z/DuXOJ+wFgYzycY2oC0SaWzSvG0HEjcQ1sJjZtnYdzh3xplnbNa8fcnGG2+kJRsk4PPL+eOPbzZ8O5zb357GfJvnm53svBvJBx98qLHBqQUDOIJVJhCrHATsSVE8CkbBKBgFIwMAANgLYiKRshf2AAAAAElFTkSuQmCC","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Nisar","middleName":"","lastName":"Ahmed","suffix":""},{"id":398890880,"identity":"93c479d6-141b-4469-8152-ebbd9c6d979a","order_by":2,"name":"Qurat-ul-Ain Akram","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qurat-ul-Ain","middleName":"","lastName":"Akram","suffix":""},{"id":398890882,"identity":"0abc7a52-7be3-4a25-8ec6-0c10c484f1aa","order_by":3,"name":"Shahzad Asif","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shahzad","middleName":"","lastName":"Asif","suffix":""},{"id":398890883,"identity":"243a1ba9-88bb-4ebf-954f-e886fb9ea2a0","order_by":4,"name":"Muhammad Shahbaz","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Shahbaz","suffix":""}],"badges":[],"createdAt":"2024-12-25 08:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5710241/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5710241/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73407203,"identity":"4a53964d-0e54-4159-96ea-7af886793880","added_by":"auto","created_at":"2025-01-09 15:32:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1879906,"visible":true,"origin":"","legend":"","description":"","filename":"MTAPEnhancingCropClassificationthroughRemoteSensingLandsat89FusionandFeatureSelection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5710241/v1_covered_8e465eaa-3693-47cd-98a4-80ec62043266.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Crop Classification through Remote Sensing: Landsat 8-9 Fusion and Feature Selection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"crop cover identification, remote sensing, classification, vegetation indices, landsat","lastPublishedDoi":"10.21203/rs.3.rs-5710241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5710241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Remote sensing offers a highly effective method for obtaining accurate information on total cropped area and crop types. 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