{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:21:10Z","timestamp":1783578070145,"version":"3.55.0"},"reference-count":133,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AgroMissionHub"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology\u2019s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.<\/jats:p>","DOI":"10.3390\/rs16050838","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T07:56:02Z","timestamp":1709106962000},"page":"838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency"],"prefix":"10.3390","volume":"16","author":[{"given":"Jingcheng","family":"Zhang","sequence":"first","affiliation":[{"name":"Precision Agriculture Laboratory, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuncai","family":"Hu","sequence":"additional","affiliation":[{"name":"Precision Agriculture Laboratory, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[{"name":"Inner Mongolia Key Laboratory of Soil Quality and Nutrient Resources, Key Laboratory of Agricultural Ecological Security and Green Development at Universities of Inner Mongolia Autonomous, Hohhot 010018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6362-3174","authenticated-orcid":false,"given":"Kadeghe G.","family":"Fue","sequence":"additional","affiliation":[{"name":"Electronics and Precision Agriculture Lab (EPAL), Department of Agricultural Engineering, Sokoine University of Agriculture, Morogoro 30007, Tanzania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0686-6783","authenticated-orcid":false,"given":"Kang","family":"Yu","sequence":"additional","affiliation":[{"name":"Precision Agriculture Laboratory, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany"},{"name":"World Agricultural Systems Center, Technical University of Munich, 85354 Freising, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Anas, M., Liao, F., Verma, K.K., Sarwar, M.A., Mahmood, A., Chen, Z.-L., Li, Q., Zeng, X.-P., Liu, Y., and Li, Y.-R. 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