Explainable AI – based study of the interactions between remote sensing and ground-truth climate variables and Lake Chad’s level fluctuations

Explainable AI – based study of the interactions between remote sensing and ground-truth climate variables and Lake Chad’s level fluctuations | 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 Explainable AI – based study of the interactions between remote sensing and ground-truth climate variables and Lake Chad’s level fluctuations Kim-Ndor Djimadoumngar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6718364/v2 This work is licensed under a CC BY 4.0 License Archived Versions: Posted Version 2 posted You are reading this latest preprint version Abstract Research area: Lake Chad (Republic of Chad). Purpose: To identify significant remote sensing and ground-truth climate factors and their interactions and contributions in predicting remote sensing and ground-truth lake levels. A comparative analysis from 2013 to 2021 using Linear model (LM), regression tree (RT), random forest (RF), and gradient boosting regression (GBR) shows that GBR outperforms other methods for both remote sensing and ground-truth data. Ground-truth lake level regressed on ground-truth features ( = 71%, = 0.23, = 0.09, = 0.12) outperforms that regressed on remote sensing features ( = 64%, = 0.27, = 0.11, = 0.15). Local Interpretable Model-agnostic Explanations and SHapley Additive exPlanations based on GBR reveal that ground-truth air temperature influences the most ground-truth lake level: higher temperatures decrease predictions, while lower temperatures increase them. Remote sensing precipitation also significantly affects ground-truth lake level: higher precipitation reduces predictions, while lower amounts increase them. Air temperature emerges as the most critical factor, whether from remote sensing or ground-truth data. Precipitation and evaporation are 90% clustered, irrespective of the data source. These findings provide valuable insights for decision-makers regarding the impacts of climate change and water resource management. Further studies are necessary for validation purposes. Explainable AI interactions remote sensing ground-truth climate variables lake level Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Archived Versions: Posted Version 2 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-6718364","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":2,"date":"2025-11-12 14:43:55","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"remote-sensing-in-earth-systems-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rses","sideBox":"Learn more about [Remote Sensing in Earth Systems Sciences](https://link.springer.com/journal/41976)","snPcode":"41976","submissionUrl":"https://submission.nature.com/new-submission/41976/3","title":"Remote Sensing in Earth Systems Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541891174,"identity":"51ea1c7c-c66c-48b2-b032-76119e737317","order_by":0,"name":"Kim-Ndor Djimadoumngar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYDCCA2wMBxgYmBkY2BuAPAMLUrTwHABpkSBOCwNYi0QCiEGEFr4DbImHbu6wljOf+fzqhh8FEgz87d0JeLVIHmA7cDj3TLqxzO2csps9QIdJnDm7Aa8WgwPsDYdz2w4nzpDOSbvBA9RiIJFLrBbJM2k3/xCnBeQwkBYJ9mO3ibJF8jBbAlBLurEETw7bbRkDCR6CfuE73mb8ObfNWk6C/fizm2/+2Mjxt/fi1wKKESjgMQCT+JWjAvYHpKgeBaNgFIyCEQQAYiNI9AOQPjAAAAAASUVORK5CYII=","orcid":"","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Kim-Ndor","middleName":"","lastName":"Djimadoumngar","suffix":""}],"badges":[],"createdAt":"2025-05-21 16:38:05","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-6718364/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-6718364/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41976-025-00265-w","type":"published","date":"2026-01-06T15:59:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100070172,"identity":"b4a240ea-39e2-49c3-ab57-ad83dd06caf6","added_by":"auto","created_at":"2026-01-12 16:16:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":919708,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptRSESSRevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6718364/v2_covered_00a269c4-bfdc-4abb-b511-1cc3eec9ee4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainable AI – based study of the interactions between remote sensing and ground-truth climate variables and Lake Chad’s level fluctuations","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":"remote-sensing-in-earth-systems-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rses","sideBox":"Learn more about [Remote Sensing in Earth Systems Sciences](https://link.springer.com/journal/41976)","snPcode":"41976","submissionUrl":"https://submission.nature.com/new-submission/41976/3","title":"Remote Sensing in Earth Systems Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Explainable AI, interactions, remote sensing, ground-truth, climate variables, lake level","lastPublishedDoi":"10.21203/rs.3.rs-6718364/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6718364/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResearch area: Lake Chad (Republic of Chad).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePurpose: To identify significant remote sensing and ground-truth climate factors and their interactions and contributions in predicting remote sensing and ground-truth lake levels. A comparative analysis from 2013 to 2021 using Linear model (LM), regression tree (RT), random forest (RF), and gradient boosting regression (GBR) shows that GBR outperforms other methods for both remote sensing and ground-truth data. Ground-truth lake level regressed on ground-truth features (\u0026nbsp;= 71%, = 0.23, \u0026nbsp;= 0.09, = 0.12) outperforms that regressed on remote sensing features (\u0026nbsp;= 64%, = 0.27, \u0026nbsp;= 0.11, = 0.15). Local Interpretable Model-agnostic Explanations and SHapley Additive exPlanations based on GBR reveal that ground-truth air temperature influences the most ground-truth lake level: higher temperatures decrease predictions, while lower temperatures increase them. Remote sensing precipitation also significantly affects ground-truth lake level: higher precipitation reduces predictions, while lower amounts increase them.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAir temperature emerges as the most critical factor, whether from remote sensing or ground-truth data. Precipitation and evaporation are 90% clustered, irrespective of the data source. These findings provide valuable insights for decision-makers regarding the impacts of climate change and water resource management. Further studies are necessary for validation purposes.\u003c/p\u003e","manuscriptTitle":"Explainable AI – based study of the interactions between remote sensing and ground-truth climate variables and Lake Chad’s level fluctuations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 02:56:28","doi":"10.21203/rs.3.rs-6718364/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-09T09:09:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T11:33:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103820992178293910212036715164486969447","date":"2025-09-24T05:09:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252921750208799083300193060850922138384","date":"2025-09-24T04:32:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-07T10:11:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209341786804072283051978400763346329969","date":"2025-06-16T10:32:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-06T07:34:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-31T00:48:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-30T08:01:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Remote Sensing in Earth Systems Sciences","date":"2025-05-21T16:23:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"remote-sensing-in-earth-systems-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rses","sideBox":"Learn more about [Remote Sensing in Earth Systems Sciences](https://link.springer.com/journal/41976)","snPcode":"41976","submissionUrl":"https://submission.nature.com/new-submission/41976/3","title":"Remote Sensing in Earth Systems Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ce300f21-6e56-4bb8-940e-561080365c45","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:10:43+00:00","versionOfRecord":{"articleIdentity":"rs-6718364","link":"https://doi.org/10.1007/s41976-025-00265-w","journal":{"identity":"remote-sensing-in-earth-systems-sciences","isVorOnly":false,"title":"Remote Sensing in Earth Systems Sciences"},"publishedOn":"2026-01-06 15:59:15","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-11-12 14:43:55","video":"","vorDoi":"10.1007/s41976-025-00265-w","vorDoiUrl":"https://doi.org/10.1007/s41976-025-00265-w","workflowStages":[]},"version":"v2","identity":"rs-6718364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6718364","identity":"rs-6718364","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}