The Role of Remote Sensing-based in Crop Yield Prediction: A Systematic Literature Review of Approaches, Data Sources, and Challenges

The Role of Remote Sensing-based in Crop Yield Prediction: A Systematic Literature Review of Approaches, Data Sources, and Challenges | 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 Systematic Review The Role of Remote Sensing-based in Crop Yield Prediction: A Systematic Literature Review of Approaches, Data Sources, and Challenges Soka Zimba, Aaron Zimba, Bob Jere This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8491675/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 Crop yields is crucial to food security, agricultural management, and policy planning with the growing climate variability and resource limitations. Remote sensing with machine learning and deep learning has become an effective tool of yield estimation that can be performed at scale and in an objective manner. The current paper reports a systematic literature review of remote-sensing-based crop yield prediction including 106 peer-reviewed articles published in 2015–2025, which is conducted in a PRISMA-compliant manner. The review covers the important methodological strategies, sources of data, types of crops, geographic coverage, and performance measures, challenges, and research trends. Sentinel-2 is the most popular satellite platform with its best balance of spatial resolution, revisit rate, spectral content, and free access which is usually complemented by SAR, Landsat, MODIS, UAVs and ancillary data by multi-modal sensor fusion. In crops like wheat, maize, rice, and soybean, higher order Deep Learning and fusion-based methods are normally associated with coefficients of determination (R 2 ) between 0.75 and 0.90, which is higher than other single-source and pure statistical methods. Nevertheless, some of these issues have not been fully addressed such as the unavailability of ground truth data, cloud pollution, trade-off in spatial resolution, lack of model transferability and uneven evaluation procedures. The new trends emphasize the increased significance of attention procedures, transfer learning, explainable Artificial Intelligence, data assimilation with crop growth models, and cloud-based systems of operations. Overall, this review offers a systematic review of the existing knowledge, unveils the key gaps, and represents evidence-based recommendations on the direction of future research and functional implementation in the field of precision agriculture and global food security. This review contributes to the literature in that it is a systematic synthesis of methods of modelling, data, and evaluation practices and where research gaps and methodological biases are identified that would influence future remote sensing-based crop yield prediction. Remote sensing crop yield prediction machine learning deep learning PRISMA systematic review precision agriculture satellite imagery sensor fusion Full Text Additional Declarations No competing interests reported. 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. 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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-8491675","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":570011827,"identity":"2837d215-df43-43d5-99e5-f7613b06a8b5","order_by":0,"name":"Soka 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Remote sensing with machine learning and deep learning has become an effective tool of yield estimation that can be performed at scale and in an objective manner. The current paper reports a systematic literature review of remote-sensing-based crop yield prediction including 106 peer-reviewed articles published in 2015\u0026ndash;2025, which is conducted in a PRISMA-compliant manner. The review covers the important methodological strategies, sources of data, types of crops, geographic coverage, and performance measures, challenges, and research trends. Sentinel-2 is the most popular satellite platform with its best balance of spatial resolution, revisit rate, spectral content, and free access which is usually complemented by SAR, Landsat, MODIS, UAVs and ancillary data by multi-modal sensor fusion. In crops like wheat, maize, rice, and soybean, higher order Deep Learning and fusion-based methods are normally associated with coefficients of determination (R\u003csup\u003e2\u003c/sup\u003e ) between 0.75 and 0.90, which is higher than other single-source and pure statistical methods. Nevertheless, some of these issues have not been fully addressed such as the unavailability of ground truth data, cloud pollution, trade-off in spatial resolution, lack of model transferability and uneven evaluation procedures. The new trends emphasize the increased significance of attention procedures, transfer learning, explainable Artificial Intelligence, data assimilation with crop growth models, and cloud-based systems of operations. 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