Modifications in submerged aquatic vegetation (SAV) spatial and temporal abundance patterns indicate changes in marine environmental conditions or physical disturbances and need to be monitored. Vegetation percent cover (%cover) is recognized as one of the key parameters in SAV monitoring. Coastal waters of the Baltic Sea are often turbid and contain high amount of colored dissolved organic matter. These factors significantly reduce the water depth, where benthic parameters can be detected by remote sensing. Field campaigns were carried out in a low-transparency Pärnu Bay area to assess to what extent multispectral Sentinel-2 (S2) satellite can be used for SAV %cover mapping in such waters. An average depth restriction for S2 benthic vegetation detection remained near 1.5 to 2.0 m. Empirical and physics-based methods were applied to S2 imagery to compare their performance for SAV %cover retrieval. Both methods identified similar %cover patterns. Model validation results showed that R² of the best-performing models remained between 0.56 and 0.66 and root-mean-square error between 22.11 and 28.06. As physics-based inversion models do not require extensive set of training data for model calibration, those can be used for retrospective time series analysis across multitemporal images.