Burullus lake is one of the coastal lakes in northern Egypt. Burullus lake is connected to seven drains through its western, eastern, and southern shores. The discharge of untreated wastewater (domestic, industrial, and agricultural) from these drains caused degradation for the water quality of the lake. Therefore, it is needed to develop a methodology to monitor water quality parameters at a low cost. This study is a trial to estimate water quality parameters from remote sensing data (reflectance data) by developing statistical regression models. Sentinel-2 reflectance data are compared to field measurements. The field measurements include transparency (SDT), chlorophyll-a concentration (Chl-a), total nitrogen (TN), total phosphorus (TP), and salinity. The coefficient of determination (R2) and normalized root mean square error (NRMSE) are calculated to evaluate the goodness of fit between field measurements and reflectance values. The results show that the optimum bands and bands ratios to estimate SDT, Chl-a, TN, TP, and Salinity are B6/B7, B7/B8, B8A, B8/B3, and B3/B8, respectively. The developed regression model is acceptable to be used for detecting water parameters and the produced R2 and NRMSE are within the range from 0.52 to 0.83 and from 0.12 to 0.34, respectively. The distribution maps of water quality parameters were built using the regression equations by writing these equations in the raster calculator as raster calculation expression. The results of this study show that both optical and non-optical water quality parameters are reasonably correlated with Sentinel-2 reflectance data as a low-cost data source.