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Aiming to monitor the water quality accurately, it is necessary to extract suitable features and establish a universal inversion model for key water quality parameters. In this paper, we describe a spectral- and spatial-feature-integrated ensemble learning method for urban river network water quality grading. We proposed an in situ sampling method for urban river networks. Factor and correlation analyses were applied to extract the spectral features. Moreover, we analyzed the maximum allowed bandwidth for feature bands. We demonstrated that spatial features can improve the accuracy of water quality grading using kernel canonical correlation analysis (KCCA). Based on the spectral and spatial features, an ensemble learning model was established for total phosphorus (TP) and ammonia nitrogen (NH3-N). Both models were evaluated by means of fivefold validation. Furthermore, we proposed an unmanned aerial vehicle (UAV)-borne water quality multispectral remote sensing application process for urban river networks. Based on the process, we tested the model in practice. The experiment confirmed that our model can improve the grading accuracy by 30% compared to other machine learning models that use only spectral features. Our research can extend the application field of water quality remote sensing to complex urban river networks.<\/jats:p>","DOI":"10.3390\/rs13224591","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T20:46:47Z","timestamp":1637009207000},"page":"4591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1653-3314","authenticated-orcid":false,"given":"Xiaoteng","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9319-1640","authenticated-orcid":false,"given":"Chun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Akram","family":"Akbar","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Yun","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Tongfang Surveying Engineering and Technology Co., Ltd., Shanghai 201900, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6343","DOI":"10.1007\/s10668-019-00482-6","article-title":"Anthropogenic reactive nitrogen releases and gray water footprints in urban water pollution evaluation: The case of Shenzhen City, China","volume":"22","author":"Wang","year":"2019","journal-title":"Environ. 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