{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:51:40Z","timestamp":1784821900156,"version":"3.55.0"},"reference-count":105,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,19]],"date-time":"2021-06-19T00:00:00Z","timestamp":1624060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010473","name":"Bundesamt f\u00fcr Landwirtschaft","doi-asserted-by":"publisher","award":["Projekt 'DeepField'"],"award-info":[{"award-number":["Projekt 'DeepField'"]}],"id":[{"id":"10.13039\/501100010473","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Horizon 2020","award":["644227"],"award-info":[{"award-number":["644227"]}]},{"DOI":"10.13039\/100006541","name":"Scientific Education and Research Institute","doi-asserted-by":"publisher","award":["15.0029"],"award-info":[{"award-number":["15.0029"]}],"id":[{"id":"10.13039\/100006541","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["755617"],"award-info":[{"award-number":["755617"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc\u2014and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and\/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.<\/jats:p>","DOI":"10.3390\/rs13122404","type":"journal-article","created":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T21:50:15Z","timestamp":1624225815000},"page":"2404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5093-5352","authenticated-orcid":false,"given":"Gregor","family":"Perich","sequence":"first","affiliation":[{"name":"Group of Crop Science, Institute of Agricultural Sciences, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4343-0476","authenticated-orcid":false,"given":"Helge","family":"Aasen","sequence":"additional","affiliation":[{"name":"Group of Crop Science, Institute of Agricultural Sciences, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia Science Park, 46980 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Argento","sequence":"additional","affiliation":[{"name":"Group of Crop Science, Institute of Agricultural Sciences, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7753-9643","authenticated-orcid":false,"given":"Achim","family":"Walter","sequence":"additional","affiliation":[{"name":"Group of Crop Science, Institute of Agricultural Sciences, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0000-7491","authenticated-orcid":false,"given":"Frank","family":"Liebisch","sequence":"additional","affiliation":[{"name":"Group of Crop Science, Institute of Agricultural Sciences, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland"},{"name":"Water Protection and Substance Flows, Department Agroecology and Environment, Agroscope, 8046 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10823","DOI":"10.3390\/s130810823","article-title":"A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances","volume":"13","year":"2013","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1579\/0044-7447-31.2.64","article-title":"Reactive Nitrogen and The World: 200 Years of Change","volume":"31","author":"Galloway","year":"2002","journal-title":"Ambio"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.2307\/1310177","article-title":"Plant Responses to Multiple Environmental Factors Physiological Ecology Provides Tools for Studying How Interacting Environmental Resources Control Plant Growth","volume":"37","author":"Chapin","year":"1987","journal-title":"BioScience"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S78","DOI":"10.1016\/j.rse.2008.10.018","article-title":"Characterizing Canopy Biochemistry from Imaging Spectroscopy and Its Application to Ecosystem Studies","volume":"113","author":"Kokaly","year":"2009","journal-title":"Remote Sens. 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