Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects. Issue 2 (7th February 2022)

• Record Type: Journal Article
• Authors: Chen, Jinyue; Chen, Shuisen; Fu, Rao; Li, Dan; Jiang, Hao; Wang, Chongyang; Peng, Yongshi; Jia, Kai; Hicks, Brendan J.
• Is Part Of: Earth's future. Volume 10:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-07
• Subjects: remote sensing big data -- water extraction -- quantitative estimation of water quality -- machine learning -- data fusion -- numerical simulation; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2021EF002289
• Languages: English
• ISSNs: 2328-4277

Abstract

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. Plain Language Summary: The intensifying conflict between people and water use generates a great demand to monitor water volume and water quality, with the key scientific issues of water extraction and water quality estimation. Specialized technology, called remote sensing big data, has extracted water body and estimated water quality worldwide in a broad‐scale, rapid, and economical way. In order to understand the current status, challenges, and future prospects of remote sensing big data for water environment monitoring, we review image data source and model evaluation metrics, state‐of‐the‐art methods for water extraction and water quality estimation. We concluded that remote sensing big data have great potential to accurately extract water and estimate optically active water matters. However, the lack of remote sensing images caused by clouds and rainy weather, the low reflectance of water, the storage and processing of remote sensing big data, and the estimation of non‐optically active water parameters are four major restricting factors. Data‐driven methods based on remote sensing big data, machine learning and cloud computing provide the promising applications for intensive long‐term water dynamics and water quality estimation. Further, physically driven methods based on radiative transfer equation and data assimilation provide potential solutions for the quantitative estimation of water quality. Future remote sensing big data efforts for the water environment should take these results into account. Key Points: Progress of remote sensing big data for water environment monitoring are reviewed The state‐of‐the‐art methods of water extraction and water quality estimation are classified, evaluated and discussed We summarized three bottlenecks and five promising roadmaps of remote sensing big data for water environment monitoring