{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:02:19Z","timestamp":1762956139611,"version":"build-2065373602"},"reference-count":109,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,9,3]],"date-time":"2019-09-03T00:00:00Z","timestamp":1567468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["0315532A"],"award-info":[{"award-number":["0315532A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A sufficient nitrogen (N) supply is mandatory for healthy crop growth, but negative consequences of N losses into the environment are known. Hence, deeply understanding and monitoring crop growth for an optimized N management is advisable. In this context, remote sensing facilitates the capturing of crop traits. While several studies on estimating biomass from spectral and structural data can be found, N is so far only estimated from spectral features. It is well known that N is negatively related to dry biomass, which, in turn, can be estimated from crop height. Based on this indirect link, the present study aims at estimating N concentration at field scale in a two-step model: first, using crop height to estimate biomass, and second, using the modeled biomass to estimate N concentration. For comparison, N concentration was estimated from spectral data. The data was captured on a spring barley field experiment in two growing seasons. Crop surface height was measured with a terrestrial laser scanner, seven vegetation indices were calculated from field spectrometer measurements, and dry biomass and N concentration were destructively sampled. In the validation, better results were obtained with the models based on structural data (R2 &lt; 0.85) than on spectral data (R2 &lt; 0.70). A brief look at the N concentration of different plant organs showed stronger dependencies on structural data (R2: 0.40\u20130.81) than on spectral data (R2: 0.18\u20130.68). Overall, this first study shows the potential of crop-specific across\u2011season two-step models based on structural data for estimating crop N concentration at field scale. The validity of the models for in-season estimations requires further research.<\/jats:p>","DOI":"10.3390\/rs11172066","type":"journal-article","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T08:28:13Z","timestamp":1567585693000},"page":"2066","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Estimating Nitrogen from Structural Crop Traits at Field Scale\u2014A Novel Approach Versus Spectral Vegetation Indices"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2978-6188","authenticated-orcid":false,"given":"Nora","family":"Tilly","sequence":"first","affiliation":[{"name":"Institute of Geography, GIS &amp; RS Group, University of Cologne, D-50923 Cologne, Germany"}]},{"given":"Georg","family":"Bareth","sequence":"additional","affiliation":[{"name":"Institute of Geography, GIS &amp; RS Group, University of Cologne, D-50923 Cologne, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1111\/aab.12045","article-title":"Do plants need nitrate? the mechanisms by which nitrogen form affects plants","volume":"163","author":"Andrews","year":"2013","journal-title":"Ann. 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