{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:26:50Z","timestamp":1776076010615,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42061658"],"award-info":[{"award-number":["42061658"]}]},{"name":"National Natural Science Foundation of China","award":["2020AB005"],"award-info":[{"award-number":["2020AB005"]}]},{"name":"Plan for Tackling Key Scientific and Technological Problems in Key Fields of Production and Construction Corps","award":["42061658"],"award-info":[{"award-number":["42061658"]}]},{"name":"Plan for Tackling Key Scientific and Technological Problems in Key Fields of Production and Construction Corps","award":["2020AB005"],"award-info":[{"award-number":["2020AB005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use of a UAV equipped with a hyperspectral spectrometer for monitoring cotton leaf nitrogen content (LNC) by analyzing spectral reflectance (SR) data collected by the UAV flying at altitudes of 60, 80, and 100 m. The experiments performed included two cotton varieties and six N treatments, with applications ranging from 0 to 480 kg ha\u22121. The results showed the following: (i) With the increase in UAV flight altitude, SR at 500\u2013550 nm increases. In the near-infrared range, SR decreases with the increase in UAV flight altitude. The unique characteristics of vegetation comprise a decrease in the \u201cgreen peak\u201d, a \u201cred valley\u201d increase, and a redshift appearing in the \u201cred edge\u201d position. (ii) We completed the unsupervised classification of images and found that after classification, the SR was significantly correlated to the cotton LNC in both the visible and near-infrared regions. Before classification, the relationship between spectral data and LNC was not significant. (iii) Fusion modeling showed improved performance when UAV data were collected at three different heights. The model established by multiple linear regression (MLR) had the best performance of those tested in this study, where the model-adjusted the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) reached 0.96, 1.12, and 1.57, respectively. This was followed by support vector regression (SVR), for which the adjusted_R2, RMSE, and MAE reached 0.71, 1.48, and 1.08, respectively. The worst performance was found for principal component regression (PCR), for which the adjusted_R2, RMSE, and MAE reached 0.59, 1.74, and 1.36, respectively. Therefore, we can conclude that taking UAV hyperspectral images at multiple heights results in a more comprehensive reflection of canopy information and, thus, has greater potential for monitoring cotton LNC.<\/jats:p>","DOI":"10.3390\/rs14112576","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"2576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops"],"prefix":"10.3390","volume":"14","author":[{"given":"Caixia","family":"Yin","sequence":"first","affiliation":[{"name":"College of Agriculture, Shihezi University, Shihezi 832003, China"}]},{"given":"Xin","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Agriculture, Shihezi University, Shihezi 832003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3533-9966","authenticated-orcid":false,"given":"Lifu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Agriculture, Shihezi University, Shihezi 832003, China"},{"name":"Institute of Aerospace Information Innovation, Chinese Academy of Sciences, Beijing 100080, China"}]},{"given":"Lulu","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Agriculture, Shihezi University, Shihezi 832003, China"}]},{"given":"Huihan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Agriculture, Shihezi University, Shihezi 832003, China"}]},{"given":"Linshan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Aerospace Information Innovation, Chinese Academy of Sciences, Beijing 100080, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-8865","authenticated-orcid":false,"given":"Ze","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Agriculture, Shihezi University, Shihezi 832003, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.fcr.2008.11.004","article-title":"Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry","volume":"111","author":"Stroppiana","year":"2009","journal-title":"Field Crops Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.scitotenv.2012.11.050","article-title":"Denitrification and N2O:N2 production in temperate grasslands: Processes, measurements, modelling and mitigating negative impacts","volume":"465","author":"Saggar","year":"2013","journal-title":"Sci. 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