AI-driven remote sensing for environmental characterization and rice crop modeling in water-limited regions

AI-driven remote sensing for environmental characterization and rice crop modeling in water-limited regions | 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 AI-driven remote sensing for environmental characterization and rice crop modeling in water-limited regions Edgar S. Correa, Francisco C. Calderon, Julian D. Colorado This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6440110/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Sep, 2025 Read the published version in Discover Food → Version 1 posted 10 You are reading this latest preprint version Abstract Advancing our understanding of environmental interactions in rice crops contributes to food production in water-limited regions. This paper proposes an integrated crop modeling architecture, demonstrating how machine-learning (ML) models enhance classic Mechanistic Crop Modeling (MCM) estimations by learning directly from environmental data. Here, we quantify the impact of noise-induced uncertainty on the CERES-Rice crop growth model, particularly relevant for drought-tolerant varieties that exhibit complex adaptation mechanisms, such as Nerica 4. Environment characterization is achieved through a novel 3D Gaussian Mixture Model (GMM), offering enhanced precision and scalability when coupled with remote-sensing satellite-derived environmental data. By coupling both MCM and ML models, we achieved higher estimations for grain yield (R2=0.99) and biomass (R2=0.8) in the northwest Tambacounda region of Senegal in Africa, providing reliable estimates of 30% grain conversion efficiency and 2.18kg/ha·mm water use efficiency from an environment characterized by sandy soils with high saturated hydraulic conductivity (1.1 cm/h) and the lowest regional precipitation (513mm, 49%). Crop modeling Satellite-based genotype-by-environment (GxE) water use efficiency (WUE) rice food security Full Text Additional Declarations No competing interests reported. Supplementary Files snSupplementaryMaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 28 Sep, 2025 Read the published version in Discover Food → Version 1 posted Editorial decision: Revision requested 25 Jun, 2025 Reviews received at journal 21 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviews received at journal 28 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 07 May, 2025 Submission checks completed at journal 07 May, 2025 First submitted to journal 13 Apr, 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. 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-6440110","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442299004,"identity":"fa5c301d-957c-4dac-b639-9800f8911a4b","order_by":0,"name":"Edgar S. 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