Spatiotemporal evolution of winter urban heat and vegetation dynamics in Ahmedabad based on decadal land surface temperature and NDVI analysis from 2015 to 2024

Spatiotemporal evolution of winter urban heat and vegetation dynamics in Ahmedabad based on decadal land surface temperature and NDVI analysis from 2015 to 2024 | 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 Spatiotemporal evolution of winter urban heat and vegetation dynamics in Ahmedabad based on decadal land surface temperature and NDVI analysis from 2015 to 2024 Aniruddha Chowdhury This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8450460/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 This comprehensive study analyzes Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) trends in Ahmedabad, India, from 2015 to 2024 (excluding 2017) using three LST retrieval methods: Split Window (SW), Single Channel (SC), and Mono Window algorithms applied to Landsat 8 and 9 thermal infrared data, alongside NDVI derived from reflective bands. Quality Assessment (QA) masks were applied, filtering out approximately 15% of pixels on average by excluding clouds, water bodies, non-land surfaces, bare soil, and sparse vegetation areas. A detailed analysis of masking impacts reveals that cloud masking increases LST by 1.19–2◦ C, water masking by 0.4–0.8◦ C, non-land surface masking decreases LST by 0.1–0.3◦ C, soil masking decreases LST by 0.19–0.5◦ C, and vegetation masking increases NDVI by 0.01–0.03, with combined effects increasing NDVI by 0.04–0.05. The SW algorithm, adjusted for this study, indicates a warming trend of 0.42◦ C/year, matching the SC algorithm, while the Mono Window algorithm shows 0.43◦ C/year. NDVI declines at -0.0036/year, reflecting urbanization. Urban areas are 8–10◦ C warmer than riverbank zones, where the Sabarmati River reduces temperatures by 2–4◦ C. A strong negative LST–NDVI correlation (-0.81) underscores vegetation’s cooling effect. This paper provides detailed methodologies, algorithm comparisons, spatial and temporal analyses, and actionable implications for urban planning and climate adaptation in Ahmedabad, supported by robust remote sensing techniques [1, 2]. Land Surface Temperature NDVI Split Window Algorithm Single Channel Algorithm Mono Window Algorithm Urban Heat Island Ahmedabad Landsat 8/9 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. 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-8450460","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586544253,"identity":"0b60e55e-96b4-4cb2-b38f-9c4b40867678","order_by":0,"name":"Aniruddha Chowdhury","email":"data:image/png;base64,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","orcid":"","institution":"Space Applications Centre","correspondingAuthor":true,"prefix":"","firstName":"Aniruddha","middleName":"","lastName":"Chowdhury","suffix":""}],"badges":[],"createdAt":"2025-12-25 17:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8450460/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8450460/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102297317,"identity":"9b2ef132-392b-41a0-81ed-1538c2f3b428","added_by":"auto","created_at":"2026-02-10 10:26:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":35205671,"visible":true,"origin":"","legend":"","description":"","filename":"Archive.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8450460/v1_covered_f3c8dc5f-5466-4ac2-8830-c21a1ad3ea78.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal evolution of winter urban heat and vegetation dynamics in Ahmedabad based on decadal land surface temperature and NDVI analysis from 2015 to 2024","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Land Surface Temperature, NDVI, Split Window Algorithm, Single Channel Algorithm, Mono Window Algorithm, Urban Heat Island, Ahmedabad, Landsat 8/9","lastPublishedDoi":"10.21203/rs.3.rs-8450460/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8450460/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis comprehensive study analyzes Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) trends in Ahmedabad, India, from 2015 to 2024 (excluding 2017) using three LST retrieval methods: Split Window (SW), Single Channel (SC), and Mono Window algorithms applied to Landsat 8 and 9 thermal infrared data, alongside NDVI derived from reflective bands. Quality Assessment (QA) masks were applied, filtering out approximately 15% of pixels on average by excluding clouds, water bodies, non-land surfaces, bare soil, and sparse vegetation areas. A detailed analysis of masking impacts reveals that cloud masking increases LST by 1.19–2◦ C, water masking by 0.4–0.8◦ C, non-land surface masking decreases LST by 0.1–0.3◦ C, soil masking decreases LST by 0.19–0.5◦ C, and vegetation masking increases NDVI by 0.01–0.03, with combined effects increasing NDVI by 0.04–0.05. The SW algorithm, adjusted for this study, indicates a warming trend of 0.42◦ C/year, matching the SC algorithm, while the Mono Window algorithm shows 0.43◦ C/year. NDVI declines at -0.0036/year, reflecting urbanization. Urban areas are 8–10◦ C warmer than riverbank zones, where the Sabarmati River reduces temperatures by 2–4◦ C. A strong negative LST–NDVI correlation (-0.81) underscores vegetation’s cooling effect. 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