Surface water quality is degraded by the presence of numerous types of pollution produced by anthropogenic activities. Hence, surface water quality monitoring and assessment is essential. Conventional approaches of surface water quality monitoring are costly, time-consuming, and labor-intensive. On the other hand, remote sensing is an effective tool for monitoring surface water quality. Satellite images should be atmospherically corrected prior to using them in the estimation of surface water quality parameters (SWQPs). Therefore, The purpose of this study is to evaluate the outputs from several atmospheric correction methods, such as Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), Fast Line of sight Atmospheric Analysis of Hypercubes (FLAASH), and Atmospheric Correction for OLI lite (ACOLITE) in order to estimate total dissolved solids concentrations (TDS) over the study area of the whole province of New Brunswick, Canada. A TDS acquisition model was calibrated and validated in order to obtain TDS concentrations from atmospherically corrected Operational Land Imager (OLI) data. The results obtained from the TDS retrieval model demonstrated that the DOS method provided the most suitable remote sensing reflectance values for coastal blue, red, and shortwave infrared-2 spectral bands with a coefficient of determination (����=0.76), Root Mean Square Error (RMSE=0.76 mg/l), and significant value (P-valu
