Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

Abstract

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice of a particular sensor to be used in any RS application depends on various factors including cost, and spatial, spectral, and temporal resolutions of the images. Some of the known satellite sensors used in the literature and reviewed in this paper include the Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager.

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

“…TDS is a known physical WQP [3,13,14], categorized by the United States Environmental Protection Agency (US EPA) in its secondary drinking water regulations (SDWRs) with an allowable limit in drinking water set to 500 mg/L [3,12,15]. These SDWRs are described as nonenforceable federal guidelines that cause cosmetic and aesthetic effects.…”

“…The effect of temperature on the density of the lake, therefore, largely influences its stratification [21]. An increase in water temperature can vary the biochemical rates of reaction of organisms, bringing about thermal stratification and reduction in lake mixing [14,22].…”

“…TDS analysis is necessary and key as it offers a better understanding of the quality of water, particularly the groundwater, and the effect of seawater intrusion compared with EC analysis. Field measurement of WQPs such as TDS is, however, more difficult, cost-intensive, and time-consuming [14,[30][31][32] as compared with EC measurement, as more equipment and time are required for its measurement [27]. EC measurement is quite easy and economical and can be carried out in situ using portable water quality checker devices [27].…”

“…Machine learning (ML) or artificial intelligence (AI) techniques, however, use implicit algorithms to capture both linear and nonlinear relationships compared to empirical regressions [14,[34][35][36] to solve intricate problems [37], and can therefore be applied for effective estimation of the TDS from EC and temperature. AI models have been strongly recommended in recent years for the prediction of WQPs and have been applied in the accurate estimation of water quality index (WQI) compared to conventional models [38,39].…”

Mid-Term Monitoring of Suspended Sediment Plumes of Greek Rivers Using Moderate Resolution Imaging Spectroradiometer (MODIS) Imagery

“…Total suspended matter identification algorithms for Remote Sensing, either empirical or semi-analytical, have been developed by various researchers [43,[68][69][70]. Semi-analytical models decouple reflectance to the inherent optical properties (IOPs) of absorption and backscattering, while most of the empirical models make use of regression calibrations between water sample analyses collected in situ and remotely sensed data (match-ups).…”