Downscaling of the European Space Agency's CCI Soil Moisture Product Based on Artificial Neural Network

Downscaling of the European Space Agency's CCI Soil Moisture Product Based on Artificial Neural Network | 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 Downscaling of the European Space Agency's CCI Soil Moisture Product Based on Artificial Neural Network Hongtao Jiang, Hao Liu, Tianyi Song, Sanxiong Chen, Chengrui Fei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6138914/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 The European Space Agency's CCI soil moisture (SM) product spans from 1978 to now with 0.25° scale. Downscaling of CCI SM can estimate high resolution data, but it is easily affected by the scale invariance assumption. The applicability of this assumption requires further exploration at global scale. The artificial neural network (ANN) method is used to downscale daily CCI SM in 2020 from 0.25° to 0.05° under scale invariance assumption in the study. It shows that the downscaled SM (DSM) provides more abundant detailed spatial information and decreases the data gaps by 20% compared with CCI SM. The evaluations against in-situ data demonstrate that the temporal accuracy of DSM is not inferior to CCI SM with global average accuracy of r = 0.580, rmse = 0.091 m 3 /m 3 , bias=-0.039 m 3 /m 3 and ubrmse = 0.057 m 3 /m 3 . Moreover, the 100 downscaling fitting formulas with different accuracies are constructed by ANN and then the downscaling performances between them are analyzed. It suggests that there is a very good positive linear relationship between accuracy of downscaling model and accuracy of DSM verifying the applicability of scale invariance assumption. Therefore, the study will play an important role in promoting the application and research of CCI SM. Earth and environmental sciences/Ecology/Freshwater ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Planetary science/Hydrology CCI soil moisture downscaling scale invariance assumption artificial neural network 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. 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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-6138914","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":427842440,"identity":"c3059560-9d41-4af9-99d7-4c8dbf9c3509","order_by":0,"name":"Hongtao Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACxmYQ0QDlfWA4YACiJYjTwgakZxCjBaIPqoWZhxgtzO3Mzx5+3WGTJx/f/PCxzZ87xgYHmA/e5mGwy8PtMDZzY9kzacWGx9iMjXN4npkZHGBLtuZhSC7G4xczacm2w4kb24CMHInDNgYHeMykgS5MbMCphf0bVAuQYWEA0sL/jYAWHjPJj0At89mAhjMkHAY6jIeNkJYyaca2tMQNbDnFhj0HDhtLHmYztpxjkIxTi2H/8W2SP9tsEuc3H9/44Mefw4Z9x5sf3nhTYYdbSwM4OhgYDA7AhJhBhAEO9UAgD3LcDxADl6GjYBSMglEwCgBof1bO3VZ2igAAAABJRU5ErkJggg==","orcid":"","institution":"Zhongkai University of Agriculture and Engineering","correspondingAuthor":true,"prefix":"","firstName":"Hongtao","middleName":"","lastName":"Jiang","suffix":""},{"id":427842441,"identity":"7ff3b2d0-f46e-4950-a5dd-7a60156b01c3","order_by":1,"name":"Hao Liu","email":"","orcid":"","institution":"Zhongkai University of Agriculture and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Liu","suffix":""},{"id":427842442,"identity":"a564dba4-8342-4b05-a1f4-0f0b679b8ba3","order_by":2,"name":"Tianyi Song","email":"","orcid":"","institution":"Zhongkai University of Agriculture and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Tianyi","middleName":"","lastName":"Song","suffix":""},{"id":427842443,"identity":"67afd7d3-2f4c-448e-a8e5-a0307b037bd4","order_by":3,"name":"Sanxiong Chen","email":"","orcid":"","institution":"Zhongkai University of Agriculture and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Sanxiong","middleName":"","lastName":"Chen","suffix":""},{"id":427842444,"identity":"7eade0fa-dcfb-440f-ae41-2ef3c557a408","order_by":4,"name":"Chengrui Fei","email":"","orcid":"","institution":"Zhongkai University of Agriculture and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Chengrui","middleName":"","lastName":"Fei","suffix":""}],"badges":[],"createdAt":"2025-03-02 10:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6138914/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6138914/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82813934,"identity":"c15d0c0a-44e1-4971-8dd4-54e33eee6fa8","added_by":"auto","created_at":"2025-05-15 14:01:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1513223,"visible":true,"origin":"","legend":"","description":"","filename":"DownscalingoftheEuropeanSpaceAgencysCCISoilMoistureProductBasedonArtificialNeuralNetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6138914/v1_covered_b069a69a-3e36-4624-a300-1708d6565875.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Downscaling of the European Space Agency's CCI Soil Moisture Product Based on Artificial Neural Network","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":"CCI soil moisture, downscaling, scale invariance assumption, artificial neural network","lastPublishedDoi":"10.21203/rs.3.rs-6138914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6138914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe European Space Agency's CCI soil moisture (SM) product spans from 1978 to now with 0.25\u0026deg; scale. Downscaling of CCI SM can estimate high resolution data, but it is easily affected by the scale invariance assumption. The applicability of this assumption requires further exploration at global scale. The artificial neural network (ANN) method is used to downscale daily CCI SM in 2020 from 0.25\u0026deg; to 0.05\u0026deg; under scale invariance assumption in the study. It shows that the downscaled SM (DSM) provides more abundant detailed spatial information and decreases the data gaps by 20% compared with CCI SM. The evaluations against in-situ data demonstrate that the temporal accuracy of DSM is not inferior to CCI SM with global average accuracy of r\u0026thinsp;=\u0026thinsp;0.580, rmse\u0026thinsp;=\u0026thinsp;0.091 m\u003csup\u003e3\u003c/sup\u003e/m\u003csup\u003e3\u003c/sup\u003e, bias=-0.039 m\u003csup\u003e3\u003c/sup\u003e/m\u003csup\u003e3\u003c/sup\u003e and ubrmse\u0026thinsp;=\u0026thinsp;0.057 m\u003csup\u003e3\u003c/sup\u003e/m\u003csup\u003e3\u003c/sup\u003e. Moreover, the 100 downscaling fitting formulas with different accuracies are constructed by ANN and then the downscaling performances between them are analyzed. It suggests that there is a very good positive linear relationship between accuracy of downscaling model and accuracy of DSM verifying the applicability of scale invariance assumption. Therefore, the study will play an important role in promoting the application and research of CCI SM.\u003c/p\u003e","manuscriptTitle":"Downscaling of the European Space Agency's CCI Soil Moisture Product Based on Artificial Neural Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-13 03:39:55","doi":"10.21203/rs.3.rs-6138914/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9f3bf5b6-80f5-48e2-b307-fe08b5352811","owner":[],"postedDate":"March 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45583470,"name":"Earth and environmental sciences/Ecology/Freshwater ecology"},{"id":45583471,"name":"Earth and environmental sciences/Environmental sciences"},{"id":45583472,"name":"Earth and environmental sciences/Planetary science/Hydrology"}],"tags":[],"updatedAt":"2025-05-15T13:53:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-13 03:39:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6138914","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6138914","identity":"rs-6138914","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}