Evaluating Satellite-based multilinear regression and gauge- based spatial interpolation techniques for Köppen-Geiger classification in a national scale

Evaluating Satellite-based multilinear regression and gauge- based spatial interpolation techniques for Köppen-Geiger classification in a national scale | 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 Evaluating Satellite-based multilinear regression and gauge- based spatial interpolation techniques for Köppen-Geiger classification in a national scale Asieh Tayebi, Mohammad Hossein Mokhtari, Kaveh Deilami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4187443/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Sep, 2024 Read the published version in Theoretical and Applied Climatology → Version 1 posted 9 You are reading this latest preprint version Abstract Empirical climate classification is a process that makes environmental conditions understandable to humans by using climatic elements. Köppen-Geiger (KG) is a popular climate classification method that uses long-term precipitation and temperature data to classify climate into five primary groups. However, long-term continuous meteorological data is heavily exposed to data scarcity, particularly in a national scale. This research study addresses this challenge by leveraging satellite imageries, multilinear regression models and spatial interpolation within the context of entire country of Iran between 2016 and 2019. Accordingly, this study examined statistical relationship between 14 explanatory variables under four main categories of MODIS-LST, MODIS-NDVI, MODIS-TVDI, GPM-precipitation and SRTM-DEM against ground-based precipitation and temperature data (dependent variables). The spatial interpolation model (i.e. Krigging and CO-krigging) was directly developed from weather observation station datasets. A total of 332 synoptic stations were selected, 67% of which were used in modeling and the remaining 33% in testing. Accuracy assessment was performed with Kappa statistics. Overall, this research study developed three KG classification maps. These include a map per precipitation and temperature from regression model and spatial interpolation and a point-based maps from unused climate data in modelling. This study identified three KG main climate groups of arid, warm temperate and snow and eight KG sub-groups of hot desert, cold steppe, cold desert, hot steppe, warm temperate climate with dry hot summer, Snow climate with dry hot summer, Warm temperate climate with dry warm summer and Snow climate with dry warm summer. A comparison between those maps (kappa = 0.75) showed the higher accuracy of regression-based KG maps against spatial interpolation maps. This study contributes to a more detailed monitor of climate change across countries and regions with sparse distribution of weather observation data. Köppen-Geiger Climate classification MODIS TVDI GPM LST Spatial interpolation Multilinear regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Sep, 2024 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 24 Apr, 2024 Reviews received at journal 23 Apr, 2024 Reviews received at journal 21 Apr, 2024 Reviewers agreed at journal 12 Apr, 2024 Reviewers agreed at journal 10 Apr, 2024 Reviewers invited by journal 09 Apr, 2024 Submission checks completed at journal 01 Apr, 2024 Editor assigned by journal 01 Apr, 2024 First submitted to journal 29 Mar, 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. 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-4187443","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289175258,"identity":"3fa7fb4e-00f3-4b97-b9ed-23063b3fcba6","order_by":0,"name":"Asieh Tayebi","email":"","orcid":"","institution":"Yazd University","correspondingAuthor":false,"prefix":"","firstName":"Asieh","middleName":"","lastName":"Tayebi","suffix":""},{"id":289175259,"identity":"000dce3a-9699-4efd-95c0-8db6ba494e09","order_by":1,"name":"Mohammad Hossein Mokhtari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3RMQrCMBSA4VQxLpGuz0E9gRAQPE+D4OTmqJSIkFHXgngH3R0CgU7BuaKDXTo5dBInMV0EB2PdBPNDXqHkI4Qg5HL9YlAMKc3wZudi8uJHUIKYjzen3xKE4UlsdVe8tsn1KfRXSkzINGzxujqjdPee9E8SH6IkA9gzcSQx7nEypCjILCQJ8JHkCpD2DMGEcTQyd5ElSMeQMbkD4/6lDEkUUEOqDUEZh4+nMHGItGputTdvrhdBT0BGpZ0M4iSPld/W9TS/XMPW0h+k6c1CXh6iQszAZlnBq76V3upyuVz/1AM4plsSO9HM8gAAAABJRU5ErkJggg==","orcid":"","institution":"Yazd University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Hossein","lastName":"Mokhtari","suffix":""},{"id":289175260,"identity":"a6109d0d-83ad-49c1-aa33-4252a68f5ddb","order_by":2,"name":"Kaveh Deilami","email":"","orcid":"","institution":"RMIT University","correspondingAuthor":false,"prefix":"","firstName":"Kaveh","middleName":"","lastName":"Deilami","suffix":""}],"badges":[],"createdAt":"2024-03-29 10:45:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4187443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4187443/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-024-05176-2","type":"published","date":"2024-09-11T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64618898,"identity":"9a26cb45-f308-48c3-b44c-cfc6a79d634c","added_by":"auto","created_at":"2024-09-16 16:07:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1529550,"visible":true,"origin":"","legend":"","description":"","filename":"Mokhtarimanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4187443/v1_covered_ff4b2d7c-2dcb-497a-bf60-75c9ce4977c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Satellite-based multilinear regression and gauge- based spatial interpolation techniques for Köppen-Geiger classification in a national scale","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":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Köppen-Geiger, Climate classification, MODIS, TVDI, GPM, LST, Spatial interpolation, Multilinear regression","lastPublishedDoi":"10.21203/rs.3.rs-4187443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4187443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmpirical climate classification is a process that makes environmental conditions understandable to humans by using climatic elements. 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