An Integrated Optical and SAR Multi-Index Approach for Crop Discrimination Using Optimized Random Forest

An Integrated Optical and SAR Multi-Index Approach for Crop Discrimination Using Optimized Random Forest | 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 An Integrated Optical and SAR Multi-Index Approach for Crop Discrimination Using Optimized Random Forest Ajay Prakash M, Ragunath K. P, Pazhanivelan S, Muthumanickam D, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8853952/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Accurately distinguishing multiple crop types in diverse agricultural landscapes is essential for effective crop monitoring, food security assessments, and sustainable land management. However, this task remains challenging in mixed and smallholder farming systems, where crops often share overlapping phenological stages, and optical observations are frequently obstructed by persistent cloud cover. To address these challenges, this study developed a unified optical–SAR multi-index framework that integrates complementary multispectral and radar features with statistical separability analysis and an optimized Random Forest (RF) classifier for robust multi-crop mapping. Multi-temporal Sentinel-2 surface reflectance data were used to derive vegetation greenness, red-edge, moisture, stress, and disturbance indices that capture crop physiological conditions and phenological dynamics. Concurrently, Sentinel-1 SAR VV and VH backscatter coefficients and polarization-based indices were extracted to quantify the canopy structure, surface roughness, and moisture variability under cloud-prone conditions. These optical and SAR predictors were fused into a high-dimensional feature stack and evaluated using the Bhattacharyya and Jeffries–Matusita distance metrics to quantify pairwise inter-class separability and identify informative, non-redundant features, particularly for spectrally similar crops. Multi-crop classification was performed using an RF classifier with hyperparameter optimization and feature importance–driven top-N predictor selection to reduce dimensionality and improve model generalization. Post-classification refinement using majority filtering and connected pixel cleaning enhanced the spatial coherence of the output maps. The framework was implemented in the coastal agricultural region of Cuddalore District, Tamil Nadu, India, during the Rabi season (2024–2025). The optimized model achieved an overall classification accuracy of approximately 0.81 and produced realistic crop area estimates that were consistent with regional agricultural patterns. The results indicate that Sentinel-2 red-edge and greenness indices were the dominant predictors, whereas Sentinel-1 SAR features provided complementary structural and moisture information that strengthened the discrimination among spectrally ambiguous crops. The framework successfully mapped rice, cotton, maize, groundnut, sugarcane, coconut, cashew, and casuarina, demonstrating a scalable and operational solution for multi-crop mapping in complex and cloud-prone agricultural landscapes. Optical–SAR data fusion Multi-crop classification Sentinel-1 and Sentinel-2 Random Forest optimization Statistical separability analysis Precision agriculture Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 02 May, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 11 Mar, 2026 Editor invited by journal 05 Mar, 2026 Editor assigned by journal 17 Feb, 2026 Submission checks completed at journal 17 Feb, 2026 First submitted to journal 11 Feb, 2026 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-8853952","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604814178,"identity":"af9053bc-f0a6-4720-9b51-a51b36f34f96","order_by":0,"name":"Ajay Prakash M","email":"","orcid":"","institution":"Tamil Nadu Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ajay","middleName":"Prakash","lastName":"M","suffix":""},{"id":604814179,"identity":"f82254e9-e04c-4c6d-94fc-a32ba283ad6d","order_by":1,"name":"Ragunath K. 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However, this task remains challenging in mixed and smallholder farming systems, where crops often share overlapping phenological stages, and optical observations are frequently obstructed by persistent cloud cover. To address these challenges, this study developed a unified optical\u0026ndash;SAR multi-index framework that integrates complementary multispectral and radar features with statistical separability analysis and an optimized Random Forest (RF) classifier for robust multi-crop mapping. Multi-temporal Sentinel-2 surface reflectance data were used to derive vegetation greenness, red-edge, moisture, stress, and disturbance indices that capture crop physiological conditions and phenological dynamics. Concurrently, Sentinel-1 SAR VV and VH backscatter coefficients and polarization-based indices were extracted to quantify the canopy structure, surface roughness, and moisture variability under cloud-prone conditions. These optical and SAR predictors were fused into a high-dimensional feature stack and evaluated using the Bhattacharyya and Jeffries\u0026ndash;Matusita distance metrics to quantify pairwise inter-class separability and identify informative, non-redundant features, particularly for spectrally similar crops. Multi-crop classification was performed using an RF classifier with hyperparameter optimization and feature importance\u0026ndash;driven top-N predictor selection to reduce dimensionality and improve model generalization. Post-classification refinement using majority filtering and connected pixel cleaning enhanced the spatial coherence of the output maps. The framework was implemented in the coastal agricultural region of Cuddalore District, Tamil Nadu, India, during the Rabi season (2024\u0026ndash;2025). The optimized model achieved an overall classification accuracy of approximately 0.81 and produced realistic crop area estimates that were consistent with regional agricultural patterns. The results indicate that Sentinel-2 red-edge and greenness indices were the dominant predictors, whereas Sentinel-1 SAR features provided complementary structural and moisture information that strengthened the discrimination among spectrally ambiguous crops. The framework successfully mapped rice, cotton, maize, groundnut, sugarcane, coconut, cashew, and casuarina, demonstrating a scalable and operational solution for multi-crop mapping in complex and cloud-prone agricultural landscapes.\u003c/p\u003e","manuscriptTitle":"An Integrated Optical and SAR Multi-Index Approach for Crop Discrimination Using Optimized Random Forest","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 04:25:02","doi":"10.21203/rs.3.rs-8853952/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T09:59:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T07:04:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T02:58:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T17:02:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T08:02:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287421852825121481267857559102475576810","date":"2026-03-16T10:59:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24031249318493787548508825115709882112","date":"2026-03-16T10:33:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145982037085888065552428732976430278512","date":"2026-03-14T14:28:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261369911465628251918605171870274763798","date":"2026-03-11T12:53:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-11T10:22:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-05T18:40:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T10:44:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-17T10:39:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Environment","date":"2026-02-11T15:58:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9eeb6f49-8824-49f5-a6dd-7221398504e9","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T09:59:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T07:04:52+00:00","index":57,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T10:11:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 04:25:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8853952","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8853952","identity":"rs-8853952","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}