Transfer learning reveals large discrepancies between air and land surface temperatures in cities

Transfer learning reveals large discrepancies between air and land surface temperatures in cities | 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 Physical Sciences - Article Transfer learning reveals large discrepancies between air and land surface temperatures in cities Lei Zhao, Yiwen Zhang, TC Chakraborty, Priyam Mazumdar, Keer Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6665951/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Understanding of urban weather and climate is severely limited by data poverty resulting from a dearth of true urban weather stations 1 , 2 . As a result, land surface temperature ( T s ), obtained from remote sensing platforms, has been widely used as a stand-in for near-surface air temperature ( T a ) 3 – 7 despite their fundamental differences 8 , especially in urban areas. This has led to erroneous characterization of urban heat stress and urban climate impacts 9 . Here we develop a novel urban transfer learning framework (U-TL) to address this critical gap and to provide urban high-resolution air temperature (U-HAT) data at large scales across the contiguous United States (CONUS). U-TL demonstrates high accuracy and strong robustness in predicting urban T a , even with limited training data. The resulting U-HAT is the first high-resolution urban T a dataset capable of accurately reproducing observed and well-established urban climatology. U-HAT reveals substantial T s –T a discrepancies and therefore cautions the use of T s to characterize urban heat. We show that satellite-measured T s substantially overestimates both urban heat stress magnitude and intra-city disparities, which have very consequential implications for urban heat exposure, vulnerability, and adaptation policy making. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Climate sciences/Atmospheric science Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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-6665951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":467662647,"identity":"32f359e4-127f-4562-bfbf-80e13e18bdf0","order_by":0,"name":"Lei 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As a result, land surface temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e), obtained from remote sensing platforms, has been widely used as a stand-in for near-surface air temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e)\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e despite their fundamental differences\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, especially in urban areas. This has led to erroneous characterization of urban heat stress and urban climate impacts\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Here we develop a novel urban transfer learning framework (U-TL) to address this critical gap and to provide urban high-resolution air temperature (U-HAT) data at large scales across the contiguous United States (CONUS). U-TL demonstrates high accuracy and strong robustness in predicting urban \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e, even with limited training data. The resulting U-HAT is the first high-resolution urban \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e dataset capable of accurately reproducing observed and well-established urban climatology. U-HAT reveals substantial \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u0026ndash;T\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e discrepancies and therefore cautions the use of \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e to characterize urban heat. We show that satellite-measured \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e substantially overestimates both urban heat stress magnitude and intra-city disparities, which have very consequential implications for urban heat exposure, vulnerability, and adaptation policy making.\u003c/p\u003e","manuscriptTitle":"Transfer learning reveals large discrepancies between air and land surface temperatures in cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 05:37:24","doi":"10.21203/rs.3.rs-6665951/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4cdf04c1-22be-451c-9ec5-72afcc59526f","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":49658770,"name":"Earth and environmental sciences/Climate sciences"},{"id":49658771,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science"}],"tags":[],"updatedAt":"2025-06-23T05:37:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 05:37:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6665951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6665951","identity":"rs-6665951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}