Open AccessRemote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects
Author(s) -
Chen Jinyue,
Chen Shuisen,
Fu Rao,
Li Dan,
Jiang Hao,
Wang Chongyang,
Peng Yongshi,
Jia Kai,
Hicks Brendan J.
Publication year - 2022
Publication title -
earth's future
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.641
H-Index - 39
ISSN - 2328-4277
DOI - 10.1029/2021ef002289
Subject(s) - cloud computing , computer science , water quality , big data , remote sensing , machine learning , data mining , ecology , biology , operating system , geology
Abstract Accurate water extraction and quantitative estimation of water quality are two key and challenging issues for remote sensing of water environment. Recent advances in remote sensing big data, cloud computing, and machine learning have promoted these two fields into a new era. This study reviews the operating framework and methods of remote sensing big data for water environment monitoring, with emphasis on water extraction and quantitative estimation of water quality. The following aspects were investigated in this study: (a) image data source and model evaluation metrics; (b) state‐of‐the‐art methods for water extraction, including threshold‐based methods, water indices, and machine learning‐based methods; (c) state‐of‐the‐art models for quantitative estimation of water quality, including empirical models, semi‐empirical/semi‐analytical models, and machine learning‐based models; (d) some shortcomings and three challenges of current remote sensing big data for water environment monitoring, namely the new data gap caused by massive heterogeneous data, inefficient water environment monitoring due to “low spatiotemporal resolution,” and low accuracy of water quality estimation models resulting from complex water composition and insufficient atmospheric correction methods for water bodies; and (e) five recommendations to solve these challenges, namely, using cloud computing and emerging sensors/platforms to monitor water changes in intensive time series, establishing models based on ensemble machine learning algorithms, exploring quantitative estimation models of water quality that couple physics and causality, identifying the missing elements in water environment assessments, and developing new governance models to meet the widespread applications of remote sensing of water environment. This review can help provide a potential roadmap and information support for researchers, practitioners, and management departments in the theoretical exploration and innovative application of remote sensing big data for water environment monitoring.