{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T03:46:58Z","timestamp":1786160818020,"version":"3.56.0"},"reference-count":47,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB2102902"],"award-info":[{"award-number":["2019YFB2102902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR","award":["KF-2019-04-006"],"award-info":[{"award-number":["KF-2019-04-006"]}]},{"name":"the \u201cNatural Science Foundation Key projects of Hubei Province\u201d under Grant","award":["2020CFA005"],"award-info":[{"award-number":["2020CFA005"]}]},{"name":"Central Government Guides Local Science and Technology Development Projects","award":["2019ZYYD050"],"award-info":[{"award-number":["2019ZYYD050"]}]},{"name":"Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring","award":["2020-2"],"award-info":[{"award-number":["2020-2"]}]},{"name":"the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University","award":["18R02"],"award-info":[{"award-number":["18R02"]}]},{"name":"Key Laboratory of Agricultural Remote Sensing of the Ministry of Agriculture","award":["20170007"],"award-info":[{"award-number":["20170007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.<\/jats:p>","DOI":"10.3390\/rs13193928","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3928","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9879-3648","authenticated-orcid":false,"given":"Qikai","family":"Lu","sequence":"first","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Si","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifei","family":"Wei","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihong","family":"Xia","sequence":"additional","affiliation":[{"name":"Wuhan Regional Climate Center, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Ye","sequence":"additional","affiliation":[{"name":"Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xia","sequence":"additional","affiliation":[{"name":"Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102058","DOI":"10.1016\/j.hal.2021.102058","article-title":"Coastal Eutrophication in China: Trend, Sources, and Ecological Effects","volume":"107","author":"Wang","year":"2021","journal-title":"Harmful Algae"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111826","DOI":"10.1016\/j.jenvman.2020.111826","article-title":"Stochastic Trophic Level Index Model: A New Method for Evaluating Eutrophication State","volume":"280","author":"Ding","year":"2021","journal-title":"J. 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