Assessing Agricultural Drought Risk Under CMIP6 Scenarios Using Hybrid AI Models and Satellite-Derived TVDI

Assessing Agricultural Drought Risk Under CMIP6 Scenarios Using Hybrid AI Models and Satellite-Derived TVDI | 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 Assessing Agricultural Drought Risk Under CMIP6 Scenarios Using Hybrid AI Models and Satellite-Derived TVDI Mohammad Zare, Marius Hobart, Michael Schirrmann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8137749/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 This study focuses on predicting the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale Ghana using climate projections and satellite imagery. The nonlinear cross-correlation between the Standardized Precipitation Index (SPI) and TVDI was examined to comprehend the impact of meteorological drought on agricultural drought. Historical precipitation data and CMIP6 projected data (2015–2050) from 35 climate models across four Shared Socioeconomic Pathway (SSP) scenarios were subjected to bias correction and utilized to compute SPI. TVDI was obtained from Landsat 8/9 imagery and validated by UAV-based data, demonstrating good agreement. A nonlinear study indicated that SPI precedes TVDI by 2–4 months, facilitating lag-based forecasting. A hybrid Wavelet-ANFIS/FCM model predicted TVDI utilizing SPI as input, with significant accuracy. Mutual information (MI) analysis indicated SPI leads TVDI by 0–6 months, facilitating lag-based prediction. A hybrid model predicted TVDI using SPIs as inputs with high accuracy (RMSE < 0.08 in training and test phases). These findings underscore the escalating drought risk associated with high-emission SSP scenarios and emphasize the necessity for adaptive methods such as supplementary measures. The system provides AI-driven solution for predicting agricultural drought, thereby promoting sustainable development goals (SDGs) in the context of climate change. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Climate change Drought Lag time SPI TVDI Wavelet-ANFIS Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Dec, 2025 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers invited by journal 23 Nov, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 17 Nov, 2025 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-8137749","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":551301275,"identity":"6d80069f-599b-4cc5-8423-f12fbe6fc543","order_by":0,"name":"Mohammad 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These findings underscore the escalating drought risk associated with high-emission SSP scenarios and emphasize the necessity for adaptive methods such as supplementary measures. The system provides AI-driven solution for predicting agricultural drought, thereby promoting sustainable development goals (SDGs) in the context of climate change.\u003c/p\u003e","manuscriptTitle":"Assessing Agricultural Drought Risk Under CMIP6 Scenarios Using Hybrid AI Models and Satellite-Derived TVDI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 17:57:23","doi":"10.21203/rs.3.rs-8137749/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-26T05:08:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T18:44:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317642226313442216652735046812265292010","date":"2025-12-20T03:20:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151245767583172266132640234790031293110","date":"2025-12-20T01:51:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258371577677814969658719748651711866940","date":"2025-11-26T05:34:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T19:25:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123271764831299130696553263494545791091","date":"2025-11-24T17:18:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-24T04:13:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T15:16:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T15:08:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Natural Hazards","date":"2025-11-17T16:33:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)","snPcode":"44304","submissionUrl":"https://submission.springernature.com/new-submission/44304/3","title":"npj Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ae176b0-8bf6-4c67-a354-a57145c4b4bf","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58664557,"name":"Earth and environmental sciences/Climate sciences"},{"id":58664558,"name":"Earth and environmental sciences/Environmental sciences"},{"id":58664559,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2026-03-06T09:25:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 17:57:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8137749","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8137749","identity":"rs-8137749","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}