{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T10:59:56Z","timestamp":1760785196124,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T00:00:00Z","timestamp":1724544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Science and Engineering Research Council of Canada (NSERC) Discovery","doi-asserted-by":"publisher","award":["RGPIN-2022-05051"],"award-info":[{"award-number":["RGPIN-2022-05051"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Western Graduate Research Scholarship provided by The University of Western Ontario","award":["RGPIN-2022-05051"],"award-info":[{"award-number":["RGPIN-2022-05051"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Reliable and accurate crop yield prediction at the field scale is critical for meeting the global demand for reliable food sources. In this study, we tested the viability of VEN\u03bcS satellite data as an alternative to other popular and publicly available multispectral satellite data to predict winter wheat yield and produce a yield prediction map for a field located in southwestern Ontario, Canada, in 2020. Random forest (RF) and support vector regression (SVR) were the two machine learning techniques employed. Our results indicate that machine learning models paired with vegetation indices (VIs) derived from VEN\u03bcS imagery can accurately predict winter wheat yield 1~2 months prior to harvest, with the most accurate predictions achieved during the early fruit development stage. While both machine learning approaches were viable, SVR produced the most accurate prediction with an R2 of 0.86 and an RMSE of 0.3925 t\/ha using data collected from tillering to the early fruit development stage. NDRE-1, NDRE-2, and REP from various growth stages were ranked among the top seven variables in terms of importance for the prediction. These findings provide valuable insights into using high-resolution satellites as tools for non-destructive yield potential analysis.<\/jats:p>","DOI":"10.3390\/rs16173132","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T03:14:31Z","timestamp":1724642071000},"page":"3132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Local Field-Scale Winter Wheat Yield Prediction Using VEN\u00b5S Satellite Imagery and Machine Learning Techniques"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2510-284X","authenticated-orcid":false,"given":"Marco Spencer","family":"Chiu","sequence":"first","affiliation":[{"name":"Department of Geography and Environment, The University of Western Ontario, London, ON N6G 3K7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8404-0530","authenticated-orcid":false,"given":"Jinfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography and Environment, The University of Western Ontario, London, ON N6G 3K7, Canada"},{"name":"The Institute for Earth and Space Exploration, The University of Western Ontario, London, ON N6A 3K7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"87","DOI":"10.4296\/cwrj2801087","article-title":"Impacts of Recent Climate Trends on Agriculture in Southwestern Ontario","volume":"28","author":"Tan","year":"2003","journal-title":"Can. 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