Seagrasses provide many ecosystem services such as habitat provisioning, biodiversity maintenance, food security, coastal protection, and carbon sequestration. With the projected temperature extremes and sea level rise due to climate change, these important ecosystems are highly threatened. Conserving these important ecosystems requires accurate and efficient mapping of its distribution and trajectories of change. Unfortunately, the spectral similarities between the seagrass and optically deep water pixels in the satellite images, or dark pixel confusion, causes potential classification errors. Within the context of the Global Seagrass Watch project, funded by DLR and supported by the GEO-GEE program, we develop a novel open method within the Google Earth Engine platform to identify and mask out these optically deep water pixels on open Sentinel-2 satellite data. This method yields less confusion and results in a more accurate seagrass detection which could benefit scientists focused on seagrass-related climate science.