{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T13:00:58Z","timestamp":1765976458730,"version":"build-2065373602"},"reference-count":75,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"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":["031B0734F"],"award-info":[{"award-number":["031B0734F"]}],"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>UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR\/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system\u2019s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.<\/jats:p>","DOI":"10.3390\/rs13091697","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T21:18:20Z","timestamp":1619558300000},"page":"1697","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Investigating the Potential of a Newly Developed UAV-Mounted VNIR\/SWIR Imaging System for Monitoring Crop Traits\u2014A Case Study for Winter Wheat"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1890-4839","authenticated-orcid":false,"given":"Alexander","family":"Jenal","sequence":"first","affiliation":[{"name":"GIS &amp; RS Group, Institute of Geography, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany"},{"name":"Application Center for Machine Learning and Sensor Technology AMLS, University of Applied Science Koblenz, Joseph-Rovan-Allee 2, 53424 Remagen, Germany"}]},{"given":"Hubert","family":"H\u00fcging","sequence":"additional","affiliation":[{"name":"INRES\u2014Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7790-847X","authenticated-orcid":false,"given":"Hella Ellen","family":"Ahrends","sequence":"additional","affiliation":[{"name":"Department of Agricultural Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Koetilantie 5, 00790 Helsinki, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1287-9705","authenticated-orcid":false,"given":"Andreas","family":"Bolten","sequence":"additional","affiliation":[{"name":"GIS &amp; RS Group, Institute of Geography, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany"}]},{"given":"Jens","family":"Bongartz","sequence":"additional","affiliation":[{"name":"Application Center for Machine Learning and Sensor Technology AMLS, University of Applied Science Koblenz, Joseph-Rovan-Allee 2, 53424 Remagen, Germany"}]},{"given":"Georg","family":"Bareth","sequence":"additional","affiliation":[{"name":"GIS &amp; RS Group, Institute of Geography, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/nature10452","article-title":"Solutions for a Cultivated Planet","volume":"478","author":"Foley","year":"2011","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Huang, S., Miao, Y., Yuan, F., Gnyp, M.L., Yao, Y., Cao, Q., Wang, H., Lenz-Wiedemann, V.I.S., and Bareth, G. 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