globalchange  > 气候变化与战略
DOI: 10.1016/j.earscirev.2020.103187
论文题名:
Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing
作者: Sagan V.; Peterson K.T.; Maimaitijiang M.; Sidike P.; Sloan J.; Greeling B.A.; Maalouf S.; Adams C.
刊名: Earth Science Reviews
ISSN: 00128252
出版年: 2020
卷: 205
语种: 英语
中文关键词: Cloud computing ; Deep learning ; Long short-term memory neural network ; Remote sensing. ; Water quality
英文关键词: artificial neural network ; decision making ; dissolved oxygen ; machine learning ; parameter estimation ; remote sensing ; satellite data ; satellite imagery ; water quality ; United States ; algae ; Chlorophyta
英文摘要: Given the recent advances in remote sensing analytics, cloud computing, and machine learning, it is imperative to evaluate capabilities of remote sensing for water quality monitoring in the context of water resources management and decision-making. The objectives of this review were to analyze recent advances in water quality remote sensing and determine limitations of current systems, estimation methods, and suggest future improvements. To that end, we collected over 200 sets of water quality data including blue-green algae phycocyanin (BGA-PC), chlorophyll-a (Chl-a), dissolved oxygen (DO), specific conductivity (SC), fluorescent dissolved organic matter (fDOM), turbidity, and pollution-sediments from 2016 to 2018. The water quality data, generated from laboratory analysis of grab samples and in-situ real-time monitoring sensors distributed in eight lakes and rivers in Midwestern United States, were paired with synchronous proximal spectra, tripod-mounted hyperspectral imagery, and satellite data. The results showed that both proximal and satellite-based sensors have great potential to provide accurate estimate of optically active parameters, and remote sensing of non-optically active parameters may be indirectly estimated but still remains a challenge. Data-driven empirical approaches, i.e., deep learning outperformed the other competing methods, providing promising possibility for operational use of remote sensing in water quality monitoring and decision-making. As the first-time review of deep neural networks for water quality estimation, the paper concludes that anomaly detection utilizing multi-sensor data fusion and virtual constellation in cloud-computing is the most promising means for predicting impending water pollution outbreaks such as algal blooms. © 2020 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/166240
Appears in Collections:气候变化与战略

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作者单位: Geospatial Institute, Saint Louis University, 3694 West Pine Mall, St. Louis, MO 63108, United States; Department of Earth & Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, United States; National Great Rivers Research and Education Center, East Alton, IL, United States; The U.S. Army Corps of Engineers, St. Louis District, 1222 Spruce Street, St. Louis, MO 63103, United States; Department of Civil Engineering, Saint Louis University, St. Louis, MO 63108, United States; Department of Electrical and Computer Engineering, Purdue University Northwest, Hammond, IN, United States

Recommended Citation:
Sagan V.,Peterson K.T.,Maimaitijiang M.,et al. Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing[J]. Earth Science Reviews,2020-01-01,205
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