Efficient Pollutants Assessment of Larger Water-Bodies using Sentinel-2 MSI

Water bodies are considered a living heritage from which our ancestors retained a healthy environment. However, rapid urbanization and anthropogenic activities such as industrial waste discharge, sewage, and other household garbage directly into freshwater began to have an impact on the biological health of wetland ecosystems. In terms of analyzing water quality, satellite data products-based water quality monitoring is extremely helpful in drawing conclusions about water quality based on its spectral reflectance behavior. The current research focuses on developing a method for indirectly quantifying the presence of pollutants in larger bodies of water. In multispectral remote sensing, nitrate, phosphates, and ammonia are considered optically inactive parameters. Chlorophyll-a (CHL-a) and turbidity, on the other hand, are regarded as optically active components with adequate spectral reflectance properties. The water quality was estimated by establishing an empirical relationship between optically active and inactive parameters of the water-body. A linear relationship is established based on the CHL-a concentration using a two-band ratio model CHL - an a (VNIR/red) and a three-band ratio model CHL - an a [(1/red- 1/VNIR) *VNIR]. As a result, the relevant in-situ samples for quantifying the pollutants are collected and measured. According to the findings, multispectral remote sensing data for monitoring CHL-a can be used to indirectly quantify the pollutants like nitrate, phosphates, and ammonia. In terms of analyzing water quality, satellite data products-based water quality monitoring is extremely helpful in drawing conclusions about water quality based on its spectral reflectance behavior. The water quality was estimated by establishing an empirical relationship between optically active and inactive parameters of the water-body. CHL-a concentration was used as an indicator to establish the expected relationship between pollutants, the level of CHL-a was estimated using a remote sensing method. The values in remote sensing were derived from the object's spectral reflectance properties. Remote Sensing method was utilized to estimate the chlorophyll concentration on Kollimedu lake using Sentinel-2 data by using two band model and a three band model.