{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T17:21:27Z","timestamp":1780075287338,"version":"3.54.0"},"reference-count":63,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["CRDPJ507141-16"],"award-info":[{"award-number":["CRDPJ507141-16"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, vegetation indices (VI), crop height, field topographic metrics, and soil properties to predict canopy nitrogen weight (g\/m2) of a corn field in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models were evaluated for canopy nitrogen weight prediction from 29 variables. RF consistently had better performance than SVR, and the top-performing validation model was RF using 15 selected height, spectral, and topographic variables with an R2 of 0.73 and Root Mean Square Error (RMSE) of 2.21 g\/m2. Of the model\u2019s 15 variables, crop height was the most important predictor, followed by 10 VIs, three MicaSense band reflectance mosaics (blue, red, and green), and topographic profile curvature. The model information can be used to improve field nitrogen prediction, leading to more effective and efficient N fertilizer management.<\/jats:p>","DOI":"10.3390\/rs13163105","type":"journal-article","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T08:01:42Z","timestamp":1628236902000},"page":"3105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Evaluation of Soil Properties, Topographic Metrics, Plant Height, and Unmanned Aerial Vehicle Multispectral Imagery Using Machine Learning Methods to Estimate Canopy Nitrogen Weight in Corn"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6810-993X","authenticated-orcid":false,"given":"Jody","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Geography and Environment, The University of Western Ontario, London, ON N6G 3K7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brigitte","family":"Leblon","sequence":"additional","affiliation":[{"name":"Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,6]]},"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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