Forecasting field rice grain moisture content using Sentinel-2 and weather data

Forecasting field rice grain moisture content using Sentinel-2 and weather data | 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 Forecasting field rice grain moisture content using Sentinel-2 and weather data James Brinkhoff, Brian W. Dunn, Tina Dunn, Alex Schultz, Josh Hart This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5390827/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 Optimizing the timing of rice paddy drainage and harvest is crucial for maximizing yield and quality. These decisions are guided by rice grain moisture content (GMC), which is typically determined by destructive sampling fields at point locations. Providing spatial forecasts of grain moisture to rice farmers will reduce the time required to gather, thresh and measure sample moistures, and will reduce errors due to samples being taken from unrepresentative areas and allow advanced planning of end-of-season drain and harvest timing. This work demonstrates consistent relationships between rice GMC and indices derived from Sentinel-2 satellite imagery, particularly those involving selected shortwave infrared and red edge bands (r=0.84, 1620 field samples, 3 years). A methodology was developed to allow forecasts of grain moisture past the latest image date to be provided, by fusing remote sensing and accumulated weather data as inputs to machine learning models. The moisture content predictions had root mean squared error between 1.6 and 2.6 % and R 2 of 0.7 with forecast horizons from 0 to 28 days. Time-series grain moisture dry-down predictions were summarised per field to find the optimal harvest date (22% grain moisture), with an average RMSE around 6.5 days. The developed methodology was operationalized to provide rice growers with current and forecast grain moisture, enabling data-driven decisions, ultimately enhancing operational efficiency and crop outcomes. Agronomy Artificial Intelligence and Machine Learning Rice grain moisture harvest timing machine learning remote sensing forecasting crop maturity Full Text Additional Declarations The authors declare no competing interests. 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. 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-5390827","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374068433,"identity":"f4250690-b039-48c7-8f49-c92d4295ef56","order_by":0,"name":"James Brinkhoff","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie3QPWuDQBjA8UeEmy7JarHoV3hEKJT2wxgK55IjW+goFMwSyHr9FpZAuloEM7RkPqiDWbIF7GahhZ72ZShKOna4P+jBeT/uTgCd7h9mRO2QAgFCAC4d53Oe/Jkw3z9KvkqbVS0ZR8eIOX8c72oopkM7ZmUVYJg836RQzTIYiaD7YAu+8insz+PTfOOJAHlS5IEhthlYsodEfG0DZEisMLYH9RtP5ATNQZwB9JHl4f61/iZUHQzltDLfFXH7iOBroC1heUMClBMwDUWwlxxWNsV9Q9iJuot3Kxk+LLYh9Z7KTuIt+d1LfV2gK9iZpf6YO5RXu7KeXTjOpnsXL2re+Gs2VQ/tXK9y+z7odDqd7qcPXdpgVrm7SnYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0721-2458","institution":"University of New England","correspondingAuthor":true,"prefix":"","firstName":"James","middleName":"","lastName":"Brinkhoff","suffix":""},{"id":374068434,"identity":"ff056ecb-705b-4c81-b59e-b316531f364b","order_by":1,"name":"Brian W. 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These decisions are guided by rice grain moisture content (GMC), which is typically determined by destructive sampling fields at point locations. Providing spatial forecasts of grain moisture to rice farmers will reduce the time required to gather, thresh and measure sample moistures, and will reduce errors due to samples being taken from unrepresentative areas and allow advanced planning of end-of-season drain and harvest timing. This work demonstrates consistent relationships between rice GMC and indices derived from Sentinel-2 satellite imagery, particularly those involving selected shortwave infrared and red edge bands (r=0.84, 1620 field samples, 3 years). A methodology was developed to allow forecasts of grain moisture past the latest image date to be provided, by fusing remote sensing and accumulated weather data as inputs to machine learning models. The moisture content predictions had root mean squared error between 1.6 and 2.6 % and R\u003csup\u003e2\u003c/sup\u003e of 0.7 with forecast horizons from 0 to 28 days. Time-series grain moisture dry-down predictions were summarised per field to find the optimal harvest date (22% grain moisture), with an average RMSE around 6.5 days. 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