Abstract
Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments.
Highlights
There are many changes in the water body that take place when flowing water stops at the lowest elevation point on land
Sentinel-2 instrument is made of 12 spectral bands with a 10 m resolution of visible bands (VI), Sentinel-2 instrument is made of 12 spectral bands with a 10 m resolution of visible bands (VI), 20 m resolution of vegetation red edge (VRE) bands, and short-wave infrared (SWIR) bands, in addition to three bands related to coastal aerosols and water vapor of 60 m resolution
The implemented methodologies, as well as the comprehensive assessments, answered the questions concerning the feasibility of using a linear empirical approach to estimate the designated water quality parameters across temporal remote sensing data
Summary
There are many changes in the water body that take place when flowing water stops at the lowest elevation point on land. Undesirable materials, like carbon dioxide and methane, are released into the dam water This procedure can take a decade or so, in the tropics it may take many decades or even centuries for most of the organic matter to molder [5]. The Environmental Protection Agency (EPA) has been developing and offering an application that monitors the water surface and provides a reasonable water quality parameter and is available for normal users. Sentinel-2 provides data with a high spatial resolution and has been used in conjunction with developed models to detect chlorophyll and dissolved organic matter. A recent study on Sentinel-2 shows that the most accurate algorithm to acquire the highest reflectance from dissolved organic matter (DOM) comes from bond 5 and bond 3. Sentinel-2 provides data with high spatial resolution and has developed models for detecting such parameters. Regression analysis is practiced in the current research to define the relationship between the actual and the estimated water quality parameters
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