Modeling Coastal Water Clarity Using Landsat‐8 and Sentinel‐2

Understanding and attributing changes to water quality is essential the study management of coastal ecosystems ecological functions they sustain (e.g., primary productivity, predation, submerged aquatic vegetation growth). However, describing patterns clarity—a key aspect quality—over meaningful scales in space time challenged by high spatial temporal variability due natural anthropogenic processes. Regionally tuned satellite algorithms can provide a more complete understanding clarity drivers. In this study, we used open-access data low-cost situ methods improve estimates an optically complex body. Specifically, created remote sensing product compiling Landsat-8 Sentinel-2 reflectance with long-term Secchi depth measurements at 12 sites over 8 years shallow turbid lagoon system Virginia, USA. Our satellite-based model explained ∼33% variation clarity. approach increases spatiotemporal coverage improves from bio-optical that overpredicted This could lead better drivers predict how will change future.