Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects

作者：Jinyue Chen, Shuisen Chen, Rao Fu, Dan Li, Hao Jiang, Chongyang Wang, Yongshi Peng, Kai Jia, Brendan J. Hicks · 发表于：Earth s Future · 年份：2022 · DOI：10.1029/2021ef002289 · 被引用次数：312 · 研究领域：Flood Risk Assessment and Management、Hydrological Forecasting Using AI、Hydrology and Watershed Management Studies

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 mode...