Temporal and spatial variations of urban surface temperature and correlation study of influencing factors

Temporal and spatial variations of urban surface temperature and correlation study of influencing factors | 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 Temporal and spatial variations of urban surface temperature and correlation study of influencing factors Lei Ding, Xiao Xiao, Haitao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4562718/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Current studies on the effect of urban form on land surface temperature (LST) are mostly conducted from the daytime and 2D morphological perspectives, with less attention paid to the vertical structure of urban areas and their seasonal and diurnal variations, which have a significant impact on heat redistribution. In our study, we selected the spatial scale of urban neighbourhoods and calculated six 3D building form factors, and used the Gradient Boosting Machine (GBM) to quantify the effects of 3D building form on LST production captured by Landsat thermal sensors between seasons and Day/Night. The results show that MH, BD, and FAR are seasonal stabilising factors, with MH having the strongest cooling effect on LST, with a four-season average of 2.1°C and a diurnal difference in its effect on LST. There is a strong positive correlation between BD and LST during the daytime, and the strongest heating effect is in autumn, up to 3.5°C. BVD, GFA, and SVF are seasonal variation factors, with GFA and SVF having a cooling effect in all seasons except spring, and BVD having a slight cooling effect in autumn. These results will provide a reference for future urban planning and mitigation of urban heat island effect. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Three-dimensional building form Land surface temperature Satellite Remote Sensing Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Aug, 2024 Reviews received at journal 16 Jul, 2024 Reviews received at journal 08 Jul, 2024 Reviewers agreed at journal 28 Jun, 2024 Reviewers agreed at journal 28 Jun, 2024 Reviewers invited by journal 26 Jun, 2024 Editor assigned by journal 18 Jun, 2024 Editor invited by journal 18 Jun, 2024 Submission checks completed at journal 14 Jun, 2024 First submitted to journal 11 Jun, 2024 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-4562718","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":320430209,"identity":"4aa7b359-0ec4-40c8-b70f-2829ba423a99","order_by":0,"name":"Lei Ding","email":"","orcid":"","institution":"Tianjin Chengjian University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ding","suffix":""},{"id":320430210,"identity":"add68bf4-08cc-4ebe-8757-ebe1aa15487d","order_by":1,"name":"Xiao Xiao","email":"","orcid":"","institution":"Tianjin Chengjian University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Xiao","suffix":""},{"id":320430211,"identity":"c29423c0-f622-4bfe-be09-f39820041b22","order_by":2,"name":"Haitao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYDACCQaGA4n/bCAcHuK0MDM++MCWJkGSFmbDGWyHSdBicLv/mDQPz/k6/hkJjA/etjHImxPUcucwmzSPxG0JiRsJzIZz2xgMdzYQ0GJ2IxmoxeC2hIFEAps0bxtDgsEBorQknANpYf9NrBag9w8cANvCTJQW+xvJhg8+NiRLzjjzsFlyzjkJww2EtEjOSHxwILHBjp+/PfnghzdlNvIEbUECjA0M4MQwCkbBKBgFo4ByAABJQjwMmA8kCQAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin Chengjian University","correspondingAuthor":true,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-11 09:00:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4562718/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4562718/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-85146-4","type":"published","date":"2025-01-06T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73694223,"identity":"c67bc637-b5a5-4eec-ab54-d11f5e9eefbb","added_by":"auto","created_at":"2025-01-13 16:12:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":752470,"visible":true,"origin":"","legend":"","description":"","filename":"Temporalandspatialvariationsofurbansurfacetemperatureandcorrelationstudyofinfluencingfactors.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4562718/v1_covered_37fd2487-06f7-4e06-8cce-be054d7d2ede.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal and spatial variations of urban surface temperature and correlation study of influencing factors","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Three-dimensional building form, Land surface temperature, Satellite Remote Sensing, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4562718/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4562718/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent studies on the effect of urban form on land surface temperature (LST) are mostly conducted from the daytime and 2D morphological perspectives, with less attention paid to the vertical structure of urban areas and their seasonal and diurnal variations, which have a significant impact on heat redistribution. 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