Generation of Spatially and Temporally Fine-Resolution Imagery Using STF Algorithms and CACAO Post-Processing

Generation of Spatially and Temporally Fine-Resolution Imagery Using STF Algorithms and CACAO Post-Processing | 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 Generation of Spatially and Temporally Fine-Resolution Imagery Using STF Algorithms and CACAO Post-Processing Jaejun Gou, Dongwon Kang, Hyeokjin Lee, Seongju Jang, Inhong Song This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9501632/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Spatio-temporal fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, using Planet SuperDove satellite imagery which has 3 m spatial resolution and near-daily temporal resolution, and Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) data which has 0.05 m spatial resolution, downscaled to target resolution 0.5 m, and 1–4 week irregular temporal resolution. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms— Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (FitFC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)—within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous NDVI trajectories, from which growth metrics such as Vegetation Growth Metrics (VGM)85 and VGMmax were derived. The validation results indicated that ESTARFM achieved the highest Normalized Difference Vegetation Index (NDVI) performance among the evaluated algorithms, with an Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697, and CACAO further improved the results, with CA-ESTARFM providing the highest NDVI accuracy, with an RMSE of 0.108 and a UIQI of 0.740. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using VGM85 and VGMmax confirmed that CA-ESTARFM enhanced the reliability of crop growth evaluation compared to simple linear interpolation of UAV observations. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Consistent Adjustment of the Climatology to Actual Observations (CACAO) Remote sensing Satellite Spatial Temporal Fusion (STF) UAV NDVI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 30 Apr, 2026 Editor invited by journal 29 Apr, 2026 Editor assigned by journal 24 Apr, 2026 Submission checks completed at journal 24 Apr, 2026 First submitted to journal 22 Apr, 2026 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-9501632","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":635935243,"identity":"e04142cc-00b1-4fac-9397-ef9bbfcba475","order_by":0,"name":"Jaejun Gou","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Jaejun","middleName":"","lastName":"Gou","suffix":""},{"id":635935247,"identity":"23d38019-e14e-4d35-a9fd-ca3bd528bc3a","order_by":1,"name":"Dongwon Kang","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Dongwon","middleName":"","lastName":"Kang","suffix":""},{"id":635935248,"identity":"2439601f-1406-44fd-a9c1-30f7148a771a","order_by":2,"name":"Hyeokjin Lee","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Hyeokjin","middleName":"","lastName":"Lee","suffix":""},{"id":635935250,"identity":"b6278665-8f1b-40ea-9baa-4a86491dd668","order_by":3,"name":"Seongju Jang","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Seongju","middleName":"","lastName":"Jang","suffix":""},{"id":635935251,"identity":"2a9b4a81-e9d2-4667-b571-a15d6d4d29ea","order_by":4,"name":"Inhong Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACNgYeMC0HEzAgWosx8VoYoFoSG4jWwifde/Bxwa+69PmzewwYftQwGJs3ENDCJnMu2Xhm3+HcDXfOGDD2HGMwkzlASItEjpk0b8+B3A0SOQYMvA0MNhKEHAbVUpcuPyPHgPEv0Vp4fjAnMNzIMWAG2mJGjBZjY96Gw4YbbqQVHJY5JmFMUAvQPYaPef7UycvPSN748E2NjeEMQlrAgLENQh9gYCBoBwz8IVbhKBgFo2AUjEgAALciNNnzYe69AAAAAElFTkSuQmCC","orcid":"","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Inhong","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2026-04-23 03:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9501632/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9501632/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108977185,"identity":"1e34e827-1e4e-4f1f-9e1d-7917b892475f","added_by":"auto","created_at":"2026-05-11 11:30:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1232977,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9501632/v1_covered_6ab8f1e4-0c49-41ab-98a5-e83585c84533.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Generation of Spatially and Temporally Fine-Resolution Imagery Using STF Algorithms and CACAO Post-Processing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Consistent Adjustment of the Climatology to Actual Observations (CACAO), Remote sensing, Satellite, Spatial Temporal Fusion (STF), UAV, NDVI","lastPublishedDoi":"10.21203/rs.3.rs-9501632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9501632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpatio-temporal fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, using Planet SuperDove satellite imagery which has 3 m spatial resolution and near-daily temporal resolution, and Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) data which has 0.05 m spatial resolution, downscaled to target resolution 0.5 m, and 1\u0026ndash;4 week irregular temporal resolution. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms\u0026mdash; Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (FitFC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)\u0026mdash;within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous NDVI trajectories, from which growth metrics such as Vegetation Growth Metrics (VGM)85 and VGMmax were derived. The validation results indicated that ESTARFM achieved the highest Normalized Difference Vegetation Index (NDVI) performance among the evaluated algorithms, with an Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697, and CACAO further improved the results, with CA-ESTARFM providing the highest NDVI accuracy, with an RMSE of 0.108 and a UIQI of 0.740. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using VGM85 and VGMmax confirmed that CA-ESTARFM enhanced the reliability of crop growth evaluation compared to simple linear interpolation of UAV observations. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.\u003c/p\u003e","manuscriptTitle":"Generation of Spatially and Temporally Fine-Resolution Imagery Using STF Algorithms and CACAO Post-Processing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:41:40","doi":"10.21203/rs.3.rs-9501632/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T02:54:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T12:09:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262131537064686089475796173384601009707","date":"2026-05-03T12:15:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24465865558866231961382368858544187840","date":"2026-05-03T11:17:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210047727120890472338451937805665659913","date":"2026-05-01T08:29:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155248020052363189324487395451901652117","date":"2026-04-30T23:49:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T23:03:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-29T12:27:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-24T14:32:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-24T14:31:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-23T02:50:44+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"26554cc5-acc6-4a4a-bff2-22b60ee0a9a1","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T02:54:12+00:00","index":77,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T12:09:36+00:00","index":75,"fulltext":""},{"type":"reviewerAgreed","content":"262131537064686089475796173384601009707","date":"2026-05-03T12:15:44+00:00","index":73,"fulltext":""},{"type":"reviewerAgreed","content":"24465865558866231961382368858544187840","date":"2026-05-03T11:17:11+00:00","index":72,"fulltext":""},{"type":"reviewerAgreed","content":"210047727120890472338451937805665659913","date":"2026-05-01T08:29:37+00:00","index":65,"fulltext":""},{"type":"reviewerAgreed","content":"155248020052363189324487395451901652117","date":"2026-04-30T23:49:23+00:00","index":63,"fulltext":""},{"type":"reviewersInvited","content":"40","date":"2026-04-30T23:03:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-29T12:27:57+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67681734,"name":"Earth and environmental sciences/Climate sciences"},{"id":67681735,"name":"Biological sciences/Ecology"},{"id":67681736,"name":"Earth and environmental sciences/Ecology"},{"id":67681737,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-05-09T00:41:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:41:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9501632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9501632","identity":"rs-9501632","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}