气候变化领域集成服务门户(CCPortal): Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing

CCPortal

DOI	10.1016/j.earscirev.2020.103187
	Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing
	Sagan V.; Peterson K.T.; Maimaitijiang M.; Sidike P.; Sloan J.; Greeling B.A.; Maalouf S.; Adams C.
发表日期	2020
ISSN	0012-8701
卷号	205
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.
英文关键词	Cloud computing; Deep learning; Long short-term memory neural network; Remote sensing.; Water quality
语种	英语
scopus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
来源期刊	EARTH-SCIENCE REVIEWS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/209630
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
推荐引用方式 GB/T 7714	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],2020,205.
APA	Sagan V..,Peterson K.T..,Maimaitijiang M..,Sidike P..,Sloan J..,...&Adams C..(2020).Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing.EARTH-SCIENCE REVIEWS,205.
MLA	Sagan V.,et al."Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing".EARTH-SCIENCE REVIEWS 205(2020).