{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T19:59:58Z","timestamp":1770235198833,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,16]],"date-time":"2019-10-16T00:00:00Z","timestamp":1571184000000},"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":["2017YFB0504202"],"award-info":[{"award-number":["2017YFB0504202"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41622107"],"award-info":[{"award-number":["41622107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The central government guides local science and technology development projects","award":["Ecological Remote Sensing Monitoring and Wetland Restoration in the Yangtze River Basin"],"award-info":[{"award-number":["Ecological Remote Sensing Monitoring and Wetland Restoration in the Yangtze River Basin"]}]},{"name":"Special projects for technological innovation in Hubei","award":["2018ABA078"],"award-info":[{"award-number":["2018ABA078"]}]},{"name":"Open Fund of Key Laboratory of Ministry of Education for Spatial Data Mining and Information Sharing","award":["2018LSDMIS05"],"award-info":[{"award-number":["2018LSDMIS05"]}]},{"name":"Open Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University","award":["18R02"],"award-info":[{"award-number":["18R02"]}]},{"name":"Open fund of 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 formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. \u201cAction Plan for Prevention and Control of Water Pollution\u201d issued by the State Council shows Chinese government\u2019s high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R2 = 0.978), and test dataset (adjusted_R2 = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations.<\/jats:p>","DOI":"10.3390\/rs11202402","type":"journal-article","created":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T04:46:06Z","timestamp":1571287566000},"page":"2402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Lifei","family":"Wei","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"}]},{"given":"Can","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}]},{"given":"Zhengxiang","family":"Wang","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"}]},{"given":"Zhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}]},{"given":"Xiaocheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Liqin","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Printing and Packaging, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1016\/j.proenv.2012.01.179","article-title":"Urban river pollution control and remediation","volume":"13","author":"Wang","year":"2012","journal-title":"Procedia Environ. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1007\/s11356-013-2484-1","article-title":"Toxicity bioassays for water from black-odor rivers in Wenzhou, China","volume":"22","author":"He","year":"2015","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_3","first-page":"106","article-title":"Summary on ecological treatment of urban river","volume":"6","author":"Xue","year":"2008","journal-title":"Sci. Soil Water Conserv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1126\/science.1156401","article-title":"Spreading dead zones and consequences for marine ecosystems","volume":"321","author":"Diaz","year":"2008","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.envres.2008.02.001","article-title":"Biomonitoring of Lake Garda: Identification of ciliate species and symbiotic algae responsible for the \u201cblack-spot\u201d bloom during the summer of 2004","volume":"107","author":"Pucciarelli","year":"2008","journal-title":"Environ. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1016\/S1001-0742(12)60325-8","article-title":"Effects of physical and chemical characteristics of surface sediments in the formation of shallow lake algae-induced black bloom","volume":"25","author":"Shen","year":"2013","journal-title":"J. Environ. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, G., Luo, J., Zhang, C., Jiang, L., Tian, L., and Guangping, C. (2018). Characteristics and influencing factors of spatial differentiation of urban black and odorous waters in China. Sustainability, 10.","DOI":"10.3390\/su10124747"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1016\/j.jenvman.2011.01.014","article-title":"Allocation of supplementary aeration stations in the Chicago waterway system for dissolved oxygen improvement","volume":"92","author":"Alp","year":"2011","journal-title":"J. Environ. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"185","DOI":"10.2166\/wst.1999.0295","article-title":"Evaluation of a taste and odor incident on the Ohio River","volume":"40","author":"Noblet","year":"1999","journal-title":"Water Sci. Technol."},{"key":"ref_10","first-page":"42","article-title":"Analysis of formation and mechanisms of black and smelly river water in island cities","volume":"9","author":"Peng","year":"2018","journal-title":"Meteorol. Environ. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/j.1551-8833.1963.tb01010.x","article-title":"Studies on actinomycetes and their odors","volume":"55","author":"Romano","year":"1963","journal-title":"J. Am. Water Work. Assoc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S0146-6380(98)00028-X","article-title":"Dissolved organic matter and its optical properties in a blackwater tributary of the upper Orinoco river, Venezuela","volume":"28","author":"Battin","year":"1998","journal-title":"Org. Geochem."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Berthon, J.-F., and Zibordi, G. (2010). Optically black waters in the northern Baltic Sea. Geophys. Res. Lett., 37.","DOI":"10.1029\/2010GL043227"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1016\/j.watres.2009.02.016","article-title":"Occurrence of dissolved and particle-bound taste and odor compounds in Swiss lake waters","volume":"43","author":"Peter","year":"2009","journal-title":"Water Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Salem, S.I., Strand, M.H., Higa, H., Kim, H., Kazuhiro, K., Oki, K., Oki, T., Salem, S.I., Strand, M.H., and Higa, H. (2017). Evaluation of MERIS chlorophyll-a retrieval processors in a complex turbid lake Kasumigaura over a 10-year mission. Remote Sens., 9.","DOI":"10.3390\/rs9101022"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.scitotenv.2014.02.113","article-title":"Optical characterization of black water blooms in eutrophic waters","volume":"482\u2013483","author":"Duan","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_17","first-page":"57","article-title":"Remote sensing identification of urban black-odor water bodies based on high-resolution images: A case study in Nanjing","volume":"39","author":"Shuang","year":"2018","journal-title":"Environ. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lei, Z., Bing, Z., Junsheng, L., Qian, S., Fangfang, Z., and Ganlin, W. (2014). A study on retrieval algorithm of black water aggregation in Taihu Lake based on HJ-1 satellite images. IOP Conference Series: Earth and Environmental Science, IOP Publishing.","DOI":"10.1088\/1755-1315\/17\/1\/012100"},{"key":"ref_19","unstructured":"Ministry of Housing and Urban-Rural Development of China (2015). The Guideline for Urban Black and Odorous Water Treatment, (In Chinese)."},{"key":"ref_20","unstructured":"Nemerow, N.L. (1991). Stream, Lake, Estuary, and Ocean Pollution, Van Nostrand Reinhold Publishing Co."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1007\/s10661-015-4563-x","article-title":"Development of a hybrid pollution index for heavy metals in marine and estuarine sediments","volume":"187","author":"Brady","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.ecolecon.2006.07.020","article-title":"Interactions between economic growth and environmental quality in Shenzhen, China\u2019s first special economic zone","volume":"62","author":"Liu","year":"2007","journal-title":"Ecol. Econ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Arroyo-Mora, J., Kalacska, M., Inamdar, D., Soffer, R., Lucanus, O., Gorman, J., Naprstek, T., Schaaf, E., Ifimov, G., and Elmer, K. (2019). Implementation of a UAV\u2013hyperspectral pushbroom imager for ecological monitoring. Drones, 3.","DOI":"10.3390\/drones3010012"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2006.07.016","article-title":"Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data","volume":"106","author":"Houborg","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"Mueller, J.L., Fargion, G.S., McClain, C.R., Mueller, J.L., Morel, A., Frouin, R., Davis, C., Arnone, R., Carder, K., and Steward, R.G. (2003). Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume III: Radiometric Measurements and Data Analysis Protocols, NASA Goddard Space Flight Center."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Niroumand-Jadidi, M., Pahlevan, N., and Vitti, A. (2019). Mapping substrate types and compositions in shallow streams. Remote Sens., 11.","DOI":"10.3390\/rs11030262"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1117\/12.266436","article-title":"Remote sensing reflectance and inherent optical properties of oceanic waters derived from above-water measurements","volume":"Volume 2963","author":"Lee","year":"1997","journal-title":"Ocean Optics XIII"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7442","DOI":"10.1364\/AO.38.007442","article-title":"Estimation of the remote-sensing reflectance from above-surface measurements","volume":"38","author":"Mobley","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1016\/S0967-0645(97)00062-3","article-title":"A comparison of the SeaWiFS chlorophyll and CZCS pigment algorithms using optical data from the 1992 JGOFS Equatorial Pacific Time Series","volume":"44","author":"Rhea","year":"1997","journal-title":"Deep Sea Res. Part II Top. Stud. Oceanogr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10391","DOI":"10.3390\/ijerph120910391","article-title":"Estimation of chlorophyll-a concentration and the trophic state of the Barra Bonita hydroelectric reservoir using OLI\/Landsat-8 images","volume":"12","author":"Watanabe","year":"2015","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MGRS.2018.2867592","article-title":"Mini-UAV-borne hyperspectral remote sensing: From observation and processing to applications","volume":"6","author":"Zhong","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.rse.2004.01.014","article-title":"Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data","volume":"90","author":"Kindel","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.1080\/014311699211994","article-title":"The use of the empirical line method to calibrate remotely sensed data to reflectance","volume":"20","author":"Smith","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1111\/2041-210X.12833","article-title":"Unmanned aircraft system advances health mapping of fragile polar vegetation","volume":"8","author":"Lucieer","year":"2017","journal-title":"Methods Ecol. Evol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.rse.2018.09.022","article-title":"Multiple Optimal Depth Predictors Analysis (MODPA) for river bathymetry: Findings from spectroradiometry, simulations, and satellite imagery","volume":"218","author":"Vitti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Niroumand-Jadidi, M., and Vitti, A. (2017). Reconstruction of river boundaries at sub-pixel resolution: Estimation and spatial allocation of water fractions. ISPRS Int. J. Geo Inf., 6.","DOI":"10.3390\/ijgi6120383"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/S0048-9697(00)00687-2","article-title":"A semi-operative approach to lake water quality retrieval from remote sensing data","volume":"268","author":"Pulliainen","year":"2001","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1002\/esp.1787","article-title":"Spectrally based remote sensing of river bathymetry","volume":"34","author":"Legleiter","year":"2009","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"361","DOI":"10.5194\/isprs-archives-XLI-B8-361-2016","article-title":"optimal band ratio analysis of worldview-3 imagery for bathymetry of shallow rivers (case study: Sarca River, Italy)","volume":"XLI-B8","author":"Vitti","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.geomorph.2016.04.006","article-title":"Spectrally based mapping of riverbed composition","volume":"264","author":"Legleiter","year":"2016","journal-title":"Geomorphology"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bekhet, H.A., and Yasmin, T. (2013). Exploring EKC, trends of growth patterns and air pollutants concentration level in Malaysia: A nemerow index approach. IOP Conference Series: Earth and Environmental Science, IOP Publishing.","DOI":"10.1088\/1755-1315\/16\/1\/012015"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7286","DOI":"10.3390\/ijerph110707286","article-title":"Heavy metal contamination assessment and partition for industrial and mining gathering areas","volume":"11","author":"Guan","year":"2014","journal-title":"IJERPH"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.marpolbul.2017.06.013","article-title":"Distribution of heavy metals and environmental assessment of surface sediment of typical estuaries in eastern China","volume":"121","author":"Bi","year":"2017","journal-title":"Mar. Pollut. Bull."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_45","unstructured":"Wang, H., Meng, Y., Yin, P., and Hua, J. (2016, January 27\u201329). A model-driven method for quality reviews detection: An ensemble model of feature selection. Proceedings of the Wuhan International Conference on E-Business, Wuhan, China."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1504\/IJGCRSIS.2015.074722","article-title":"A multi-class boosting method for learning from imbalanced data","volume":"4","author":"Yuan","year":"2015","journal-title":"IJGCRSIS"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wei, L., Yuan, Z., Yu, M., Huang, C., and Cao, L. (2019). Estimation of arsenic content in soil based on laboratory and field reflectance spectroscopy. Sensors, 19.","DOI":"10.3390\/s19183904"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zou, Y., Ding, Y., Tang, J., Guo, F., and Peng, L. (2019). FKRR-MVSF: A fuzzy kernel ridge regression model for identifying DNA-binding proteins by multi-view sequence features via Chou\u2019s five-step rule. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20174175"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"103456","DOI":"10.1016\/j.compbiomed.2019.103456","article-title":"Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep learning and gradient-boosted trees outperform other models","volume":"114","author":"Ebrahimi","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_50","unstructured":"Huang, Z., and Yi, K. (2019). Communication-efficient weighted sampling and quantile summary for GBDT. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2019.02.022","article-title":"Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau","volume":"225","author":"Wei","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hafeez, S., Wong, M., Ho, H., Nazeer, M., Nichol, J., Abbas, S., Tang, D., Lee, K., and Pun, L. (2019). Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: A case study of Hong Kong. Remote Sens., 11.","DOI":"10.3390\/rs11060617"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, Z., Kawamura, K., Sakuno, Y., Fan, X., Gong, Z., and Lim, J. (2017). Retrieval of chlorophyll-a and total suspended solids using iterative stepwise elimination partial least squares (ISE-PLS) regression based on field hyperspectral measurements in irrigation ponds in Higashihiroshima, Japan. Remote Sens., 9.","DOI":"10.3390\/rs9030264"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shen, X., Cao, L., Chen, D., Sun, Y., Wang, G., and Ruan, H. (2018). Prediction of forest structural parameters using airborne full-waveform LiDAR and hyperspectral data in subtropical forests. Remote Sens., 10.","DOI":"10.3390\/rs10111729"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:26:59Z","timestamp":1760189219000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,16]]},"references-count":54,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11202402"],"URL":"https:\/\/doi.org\/10.3390\/rs11202402","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,16]]}}}