{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T01:23:50Z","timestamp":1772501030172,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordination for the Improvement of Higher Education Personnel (CAPES\u2014Brazil)","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Methods using remote sensing associated with artificial intelligence to forecast corn yield at the management zone level can help farmers understand the spatial variability of yield before harvesting. Here, spectral bands, topographic wetness index, and topographic position index were integrated to predict corn yield at the management zone using machine learning approaches (e.g., extremely randomized trees, gradient boosting machine, XGBoost algorithms, and stacked ensemble models). We tested four approaches: only spectral bands, spectral bands + topographic position index, spectral bands + topographic wetness index, and spectral bands + topographic position index + topographic wetness index. We also explored two approaches for model calibration: the whole-field approach and the site-specific model at the management zone level. The model\u2019s performance was evaluated in terms of accuracy (mean absolute error) and tendency (estimated mean error). The results showed that it is possible to predict corn yield with reasonable accuracy using spectral crop information associated with the topographic wetness index and topographic position index during the flowering growth stage. Site-specific models increase the accuracy and reduce the tendency of corn yield forecasting on management zones with high, low, and intermediate yields.<\/jats:p>","DOI":"10.3390\/rs14236171","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T04:00:37Z","timestamp":1670385637000},"page":"6171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction"],"prefix":"10.3390","volume":"14","author":[{"given":"Mailson Freire de","family":"Oliveira","sequence":"first","affiliation":[{"name":"Department of Engineering and Mathematical Sciences, S\u00e3o Paulo State University, Jaboticabal 14884-900, Brazil"},{"name":"Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL 36849, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brenda Valeska","family":"Ortiz","sequence":"additional","affiliation":[{"name":"Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL 36849, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilherme Trimer","family":"Morata","sequence":"additional","affiliation":[{"name":"Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL 36849, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9s-F","family":"Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Physics, Faculty of Basic Sciences and Engineering, Macrypt R.G. Universidad de los Llanos, Villavicencio 500017, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Glauco de Souza","family":"Rolim","sequence":"additional","affiliation":[{"name":"Department of Engineering and Mathematical Sciences, S\u00e3o Paulo State University, Jaboticabal 14884-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8852-2548","authenticated-orcid":false,"given":"Rouverson Pereira da","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Engineering and Mathematical Sciences, S\u00e3o Paulo State University, Jaboticabal 14884-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105779","DOI":"10.1016\/j.agwat.2019.105779","article-title":"Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI)","volume":"225","author":"Venancio","year":"2019","journal-title":"Agric. 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