Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing

By V. Sagan, K. Peterson, M. Maimaitijiang, Paheding Sidike, J. Sloan, Benjamin A. Greeling, Samar Maalouf, C. Adams. 2020

542 citations recorded by the source corpus.

Abstract 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.

Subfields: Industrial and Manufacturing Engineering, Oceanography, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/1da8e09c0fd83b2344c3e3e2e7ac63eb261024be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Water Science and Technology

1. Mengyuan Zhu
2. Hongqiang Ren
3. Jiawei Wang
4. Xiao Yang
5. Lin Ye
6. Bin Wu
7. Linyu Zhang

Citations in the corpus

• A review of the application of machine learning in water quality evaluation (2022, methodology): “However, some water quality variables, such as the concentration of pathogens, cannot be directly measured by remote sensing, as they are not optically active or lack high-spatial-resolution hyperspectral data, but can be estimated indirectly using other measurable data [29].”