The quality of drinking water is a critical factor for public health and the environment. Inland drinking water reservoirs are essential sources of freshwater supply for many communities around the world. However, these reservoirs are susceptible to various forms of contamination, including the presence of muddy water, which can pose significant challenges for water treatment facilities and lead to serious health risks for consumers. In addition, such reservoirs are also used for recreational purposes which supports the local economy. In this work, we show as a proof-of-concept that muddy water mapping can be accomplished with machine learning-based semantic segmentation constituting an extra source of sediment-laden water information. Among others, such an approach can solve issues including (i) the presence/absence, frequency and spatial extent of pollutants (ii) generalization and expansion to unknown reservoirs (assuming a curated global dataset) (iii) indications about the presence of other pollutants since it acts as their proxy. Our train/test approach is based on 13 Sentinel-2 (S-2) scenes from inland/coastal waters around Europe while treating the data as tabular. Atmospheric corrections are applied and compared based on spectral signatures. Muddy water and non-muddy water samples are taken according to expert knowledge, S-2 scene classification layer, and a combination of normalized difference indices (NDTI and MNDWI) and are evaluated based on their spectral signature statistics. Finally, a Random Forest model is trained, fine-tuned and evaluated using standard classification metrics. The experiments have shown that muddy water can be detected with high enough discrimination capacity, opening the door to more advanced image-based machine learning techniques.