{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T01:39:06Z","timestamp":1772847546941,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precision agriculture aims to optimize field management to increase agronomic yield, reduce environmental impact, and potentially foster soil carbon sequestration. In 2015, the Copernicus mission, with Sentinel-1 and -2, opened a new era by providing freely available high spatial and temporal resolution satellite data. Since then, many studies have been conducted to understand, monitor and improve agricultural systems. This paper presents results from the SolumScire project, focusing on the prediction of the spatial distribution of soil zones and topsoil properties, such as pH, soil organic matter (SOM) and clay content in agricultural fields through random forest algorithms. For this purpose, samples from 120 fields were investigated. The zoning and soil property prediction has an accuracy greater than 90%. This is supported by a high agreement of the derived zones with farmer\u2019s observations. The trained models revealed a prediction accuracy of 94%, 89% and 96% for pH, SOM and clay content, respectively. The obtained models for soil properties can support precision field management, the improvement of soil sampling and fertilization strategies, and eventually the management of soil properties such as SOM.<\/jats:p>","DOI":"10.3390\/rs12071116","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T05:52:30Z","timestamp":1585720350000},"page":"1116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2731-247X","authenticated-orcid":false,"given":"Onur","family":"Yuzugullu","sequence":"first","affiliation":[{"name":"AgriCircle, Rapperswil-Jona, 8640 St.Gallen, Switzerland"},{"name":"Group of Crop Science, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland"}]},{"given":"Frank","family":"Lorenz","sequence":"additional","affiliation":[{"name":"LUFA Nord-West, J\u00e4gerstr. 23 - 27, 26121 Oldenburg, Germany"}]},{"given":"Peter","family":"Fr\u00f6hlich","sequence":"additional","affiliation":[{"name":"AgriCircle, Rapperswil-Jona, 8640 St.Gallen, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0000-7491","authenticated-orcid":false,"given":"Frank","family":"Liebisch","sequence":"additional","affiliation":[{"name":"Group of Crop Science, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland"},{"name":"Water Protection and Substance Flows, Department of Agroecology and Environment, Agroscope, Reckenholzstrasse 191, 8046 Z\u00fcrich, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. 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