A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges

Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 8; p. 1770
Main Authors Yang, Haibo, Kong, Jialin, Hu, Huihui, Du, Yao, Gao, Meiyan, Chen, Fei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2022
Subjects
Accuracy
Algorithms
applicability
Artificial intelligence
Atmospheric correction
Chemical oxygen demand
Chlorophyll
Dissolved organic matter
Phosphorus
Pollution monitoring
R&D
Radiation
Remote sensing
Research & development
resolution
Retrieval
retrieval methods
Satellites
Sensors
Suspended matter
Unmanned aerial vehicles
Water area
Water monitoring
Water pollution
Water quality
Water quality management
United States > US
China
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Summary:Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing technologies have been widely used to retrieve water quality data. With the development of hyper-spectral sensors, unmanned aerial vehicles (UAV) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval owing to various data availabilities and retrieval methodologies. This article presents the application of remote sensing for water quality retrieval, and mainly discusses the research progress in terms of data sources and retrieval modes. In particular, we summarize some retrieval algorithms for several specific water quality variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). We also discuss the significant challenges to atmospheric correction, remotely sensed data resolution, and retrieval model applicability in the domains of spatial, temporal and water complexity. Finally, we propose possible solutions to these challenges. The review can provide detailed references for future development and research in water quality retrieval.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14081770