| Summary: | The continuous water quality monitoring (WQM) of watersheds and the existing water supplies is a crucial step in realizing sustainable water development and management. However, the conventional approaches are time-consuming, labor intensive, and do not give spatial–temporal variations of the water quality indices. The advancements in remote sensing techniques have enabled WQM over larger temporal and spatial scales. This study used satellite images and an empirical multivariate regression model (EMRM) to estimate chlorophyll-a (Chl-a), total suspended solids (TSS), and turbidity. Furthermore, ordinary Kriging was applied to generate spatial maps showing the distribution of water quality parameters (WQPs). For all the samples, turbidity was estimated with an R2 and Pearson correlation coefficient (r) of 0.763 and 0.818, respectively while TSS estimation gave respective R2 and r values of 0.809 and 0.721. Chl-a was estimated with accuracies of R2 and r of 0.803 and 0.731, respectively. Based on the results, this study concluded that WQPs provide a spatial–temporal view of the water quality in time and space that can be retrieved from satellite data products with reasonable accuracy.
HIGHLIGHTS
Remote sensing could avail a cost-effective option for the continuous monitoring of watersheds and water resources.;
Satellite-derived data could inform water quality monitoring decisions.;
Ordinary Kriging enabled the development of water quality spatial distribution maps for the water supply reservoir.;
An empirical multivariate regression modeling (EMRM) approach is used for the development of model coefficients.;
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