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In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 \u03bcg L\u22121, 0.189 mg L\u22121, 0.053 mg L\u22121, and 0.767 mg L\u22121, and the mean absolute errors were 1.272 \u03bcg L\u22121, 0.632 mg L\u22121, 0.045 mg L\u22121, and 0.674 mg L\u22121, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.<\/jats:p>","DOI":"10.3390\/rs14143272","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T07:51:56Z","timestamp":1657180316000},"page":"3272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms\u2014A Case Study of the Zhanghe River, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Yi","family":"Xiao","sequence":"first","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0099-0759","authenticated-orcid":false,"given":"Yahui","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3926-2609","authenticated-orcid":false,"given":"Guodong","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Shi","sequence":"additional","affiliation":[{"name":"Wuhu Ecological Environment Bureau, Wuhu 241000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanghua","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9761-5292","authenticated-orcid":false,"given":"Yongshuo","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1038\/nclimate2525","article-title":"The environmental impact of climate change adaptation on land use and water quality","volume":"5","author":"Fezzi","year":"2015","journal-title":"Nat. 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