{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:11:58Z","timestamp":1775470318685,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,15]],"date-time":"2021-02-15T00:00:00Z","timestamp":1613347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["EXC 2070 \u2013 390732324"],"award-info":[{"award-number":["EXC 2070 \u2013 390732324"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil\u2013plant\u2013atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR\u2019s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.<\/jats:p>","DOI":"10.3390\/rs13040710","type":"journal-article","created":{"date-parts":[[2021,2,15]],"date-time":"2021-02-15T22:58:01Z","timestamp":1613429881000},"page":"710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1349-7189","authenticated-orcid":false,"given":"Jordan Steven","family":"Bates","sequence":"first","affiliation":[{"name":"Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum J\u00fclich GmbH, 52428 J\u00fclich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0812-8570","authenticated-orcid":false,"given":"Carsten","family":"Montzka","sequence":"additional","affiliation":[{"name":"Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum J\u00fclich GmbH, 52428 J\u00fclich, Germany"}]},{"given":"Marius","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum J\u00fclich GmbH, 52428 J\u00fclich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8562-2073","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"Jonard","sequence":"additional","affiliation":[{"name":"Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum J\u00fclich GmbH, 52428 J\u00fclich, Germany"},{"name":"Earth and Life Institute, Universit\u00e9 catholique de Louvain, 1348 Louvain-la-Neuve, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.agrformet.2018.11.033","article-title":"Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives","volume":"265","author":"Yan","year":"2019","journal-title":"Agric. 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