Combining OPTRAM soil moisture index with environmental variables to downscale monthly IMERG rainfall data

Combining OPTRAM soil moisture index with environmental variables to downscale monthly IMERG rainfall data | 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 Combining OPTRAM soil moisture index with environmental variables to downscale monthly IMERG rainfall data Syed Muhammad Talha, Mujtaba Hassan, Bashir Ahmad, Shahanshah Abbas, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3869124/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 Rainfall is an essential variable for modeling various Land-Use-Land-Cover (LULC) dynamics along with hydrological and climatic modeling. Various satellite products are available for rainfall monitoring but lack high spatial resolution. In this study, the Integrated Multi-satellite Retrievals for GPM(IMERG) rainfall product is downscaled using the Optical Trapezoidal Model (OPTRAM) soil index along with other auxiliary variables to a resolution of 500m. This approach is validated using station data for the district of Sahiwal in Pakistan, with an R 2 of 0.98 and 0.89 for the years 2019 and 2020 respectively and an RMSE of 7.09mm/month and 12.25mm/month respectively using the Random Forest Algorithm. The study used both Random Forest (RF) and Epsilon Support Vector Regressor (E-SVR) Algorithm and established that RF outperformed E-SVR. The used approach achieved satisfactory results and can be used to downscale rainfall products to a suitable spatial resolution which is important for various hydrological and climatic models. This study is important for researchers and signifies the importance of freely available remote sensing datasets for predicting and monitoring urban-climatic dynamics. OPTRAM IMERG Downscaling Rainfall Remote Sensing 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3869124","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269119631,"identity":"762774b2-3ddd-4976-b9f7-06c278b27388","order_by":0,"name":"Syed Muhammad Talha","email":"","orcid":"","institution":"Institute of Space Technology","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"Muhammad","lastName":"Talha","suffix":""},{"id":269119632,"identity":"6dfa921f-4ec3-4a62-a2f5-9928771aa298","order_by":1,"name":"Mujtaba Hassan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYHCCBAbGBgY5IIPxAIMBWMQALIoHJDYAtRiDWCAtEsRoYQRpSWwAa2EgQovujITnD37usEufH334wAGGgro6BvbmbRKMO9JwajG7kZDY2HsmOXfjubQEoMMOSzDwHCuTYDyTg1dLA28bc+7GHh4DoJYDEgwSOWYSjG0V+G3521afbtjD/wGopU6CQf4NYS3NvG2HE+R5eEAhxgy0hQekBY/DzjxInC175rjhBh42gwMJBocl23jSii0Sz+Dx/vGchI9vd1TLy/cwP3zw4U8dPz/74Y03Pu5IxqmFgYEnAUwB/Q6JDDYQDxxNOAH7ATAlj6KIEa+WUTAKRsEoGGEAAMzxV88VaUzQAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Space Technology","correspondingAuthor":true,"prefix":"","firstName":"Mujtaba","middleName":"","lastName":"Hassan","suffix":""},{"id":269119633,"identity":"aac84ff7-dd06-416c-a5f1-bd6cc9c8e067","order_by":2,"name":"Bashir Ahmad","email":"","orcid":"","institution":"National Agricultural Research Center (NARC)","correspondingAuthor":false,"prefix":"","firstName":"Bashir","middleName":"","lastName":"Ahmad","suffix":""},{"id":269119634,"identity":"e9eb30fe-2452-4fcc-a6fc-4b0432655f89","order_by":3,"name":"Shahanshah Abbas","email":"","orcid":"","institution":"Institute of Space Technology","correspondingAuthor":false,"prefix":"","firstName":"Shahanshah","middleName":"","lastName":"Abbas","suffix":""},{"id":269119635,"identity":"6bda3c1d-79f4-4d7b-82f6-92f2502a8a5c","order_by":4,"name":"Asim Qadeer","email":"","orcid":"","institution":"Institute of Space Technology","correspondingAuthor":false,"prefix":"","firstName":"Asim","middleName":"","lastName":"Qadeer","suffix":""}],"badges":[],"createdAt":"2024-01-16 07:44:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3869124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3869124/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51223840,"identity":"eceeea0d-c718-4a82-9f02-6bb297b184ac","added_by":"auto","created_at":"2024-02-16 10:54:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6723160,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscriptTalhaetal.2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3869124/v1_covered_d6786adf-72d6-4403-a8bd-7317651f32d6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combining OPTRAM soil moisture index with environmental variables to downscale monthly IMERG rainfall data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"OPTRAM, IMERG, Downscaling, Rainfall, Remote Sensing","lastPublishedDoi":"10.21203/rs.3.rs-3869124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3869124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRainfall is an essential variable for modeling various Land-Use-Land-Cover (LULC) dynamics along with hydrological and climatic modeling. 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