{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T18:04:22Z","timestamp":1766599462662,"version":"build-2065373602"},"reference-count":79,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T00:00:00Z","timestamp":1682726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Regional Agricultural Research for Northern Sweden and Swedish Farmers\u2019 Foundation for Agricultural Research","award":["10\/2018","JCK-2126"],"award-info":[{"award-number":["10\/2018","JCK-2126"]}]},{"DOI":"10.13039\/501100007067","name":"Kempe Foundation","doi-asserted-by":"publisher","award":["10\/2018","JCK-2126"],"award-info":[{"award-number":["10\/2018","JCK-2126"]}],"id":[{"id":"10.13039\/501100007067","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely supportive field-specific information such as available biomass. Sentinel-2 satellites provide open access imagery that can monitor vegetation frequently. These spectral data were used to estimate the dry matter yield (DMY) of harvested forage fields in northern Sweden. Field measurements were conducted over two years at four sites with contrasting soil and climate conditions. Univariate regression and multivariate regression, including partial least square, support vector machine and random forest, were tested for their capability to accurately and robustly estimate in-season DMY using reflectance values and vegetation indices obtained from Sentinel-2 spectral bands. Models were built using an iterative (300 times) calibration and validation approach (75% and 25% for calibration and validation, respectively), and their performances were formally evaluated using an independent dataset. Among these algorithms, random forest regression (RFR) produced the most stable and robust results, with Nash\u2013Sutcliffe model efficiency (NSE) values (average \u00b1 standard deviation) for the calibration, validation and evaluation of 0.92 \u00b1 0.01, 0.55 \u00b1 0.22 and 0.86 \u00b1 0.04, respectively. Although relatively promising, these results call for larger and more comprehensive datasets as performances vary largely between calibration, validation and evaluation datasets. Moreover, RFR, as any machine learning algorithm regression, requires a very large dataset to become stable in terms of performance.<\/jats:p>","DOI":"10.3390\/rs15092350","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes"],"prefix":"10.3390","volume":"15","author":[{"given":"Junxiang","family":"Peng","sequence":"first","affiliation":[{"name":"Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 90183 Ume\u00e5, Sweden"}]},{"given":"Niklas","family":"Zeiner","sequence":"additional","affiliation":[{"name":"Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 90183 Ume\u00e5, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1393-8431","authenticated-orcid":false,"given":"David","family":"Parsons","sequence":"additional","affiliation":[{"name":"Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 90183 Ume\u00e5, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0151-1334","authenticated-orcid":false,"given":"Jean-Baptiste","family":"F\u00e9ret","sequence":"additional","affiliation":[{"name":"TETIS, INRAE, AgroParisTech, CIRAD, CNRS, Universit\u00e9 Montpellier, 34093 Montpellier, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9946-0979","authenticated-orcid":false,"given":"Mats","family":"S\u00f6derstr\u00f6m","sequence":"additional","affiliation":[{"name":"Department of Soil and Environment, Swedish University of Agricultural Sciences, 53223 Skara, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3209-5668","authenticated-orcid":false,"given":"Julien","family":"Morel","sequence":"additional","affiliation":[{"name":"Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 90183 Ume\u00e5, Sweden"},{"name":"European Commission Joint Research Centre (JRC), 21027 Ispra, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,29]]},"reference":[{"key":"ref_1","unstructured":"Jordbruksverket (2022, December 02). 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