Study of Identification and Classification Models of Urban Black and Odorous Water Based on Field Measurements of Spectral Data

Urban Black and Odorous Water (BOW) has become an environmental problem in many cities in China. The use of satellite remote sensing technology to identify BOW is still in its infancy, and there are many problems that need further solutions. In order to monitor BOW by satellite, between 2016 and 201...

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Bibliographic Details
Published in:Water (Basel) 2022-04, Vol.14 (8), p.1254
Main Authors: Zhou, Yaming, Meng, Bin, Wang, Nan, Yin, Shoujing, Feng, Aiping, Zhao, Huan, Zhu, Li, Zhang, Rong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial satellites in remote sensing
Classification
Data collection
Environmental quality
Hirsch index
Identification
Methods
Optical properties
Reflectance
Reliability
Remote monitoring
Remote sensing
Satellite communications
Satellites
Sensors
Urban areas
Water
Water quality
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Summary:Urban Black and Odorous Water (BOW) has become an environmental problem in many cities in China. The use of satellite remote sensing technology to identify BOW is still in its infancy, and there are many problems that need further solutions. In order to monitor BOW by satellite, between 2016 and 2017, the reflectance of remote sensing and some other parameters of 173 samples were collected from multiple field water experiments first. The samples were located at the major BOW in the urban areas of four Chinese cities, and the differences in remote sensing reflectance of severe BOW (SBOW), moderate BOW (MBOW), and general water (GW) were analyzed. Based on field measurements of spectral data, six remote sensing classification or identification models of BOW were compared in terms of their correct identification rate and reliability. The results show that compared with the GW in the study area, the urban BOW has the lowest reflectance. The peaks and valleys were not obvious in the visible band, especially the remote sensing reflectance of heavy BOW, which fluctuated very little in the visible band. Compared with the other five models, the H Index model had the best identification correctness and reliability.
ISSN:2073-4441
2073-4441
DOI:10.3390/w14081254