Evaluation of SENTINEL-2 products-based algorithms in estimating water pollutants of the River Niger in Bamako

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Monitoring surface water quality on a spatio-temporal scale is very important to restrict the entry of polluting components into water bodies, particularly rivers. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. Our study aims to test the use of Sentienl-2 multispectral imaging sensors in estimating three important water quality parameters: chlorophyll-a, Colored Dissolved Organic Matter and Total Suspended Matter in Niger River. Sentinel-2 satellite data were acquired in 2020. Atmospheric correction was performed using Sen2cor from the Sentinel toolbox to obtain a geometrically corrected Sentinel-2 multispectral image. We selected multiple water-body indices from the literature based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points in order to obtain an optimal water body index for estimating water quality parameters (WQP) in Niger River. Five regression functions were used in this study: linear regression, exponential, logarithmic, power and polynomial regression. The performance and accuracy of these regression models were evaluated by correlating spectral reflectance band ratio against the in situ water quality parameters (WQP) concentrations. Polynomial regression gave a higher performance and accuracy based on their R values. The best spectral index was selected to assess the spatio-temporal distribution of water pollutants in the Niger River in Bamako and its surroundings. The results showed that the polynomial regression of 6th degree provided the best fit had the best spectral band ratio and in situ Chl-a, CDOM and TSM concentrations which were respectively achieved with band index of B2/B3 (R2 = 0.78), B3/B6 (R 2 = 0.79) and B3/B4 (R 2 = 0.63). Therefore, the best band ratio was selected to evaluate Chl-a spatio-temporal in Niger River water in Bamako. The results of this study showed seasonal variability of the water pollutants in the Niger River. This highlights the potential of the Sentinel-2 products for water quality analysis. As a conclusion, the Sentinel-2 images could be helpful for precise water quality control of Niger River in Bamako.