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

Understanding and attributing changes to water quality is essential to the study and management of coastal ecosystems and the ecological functions they sustain (e.g., primary productivity, predation, and submerged aquatic vegetation growth). However, describing patterns of water clarity—a key aspect of water quality—over meaningful scales in space and time is challenged by high spatial and temporal variability due to natural and anthropogenic processes. Regionally tuned satellite algorithms can provide a more complete understanding of coastal water clarity changes and drivers. In this study, we used open‐access satellite data and low‐cost in situ methods to improve estimates of water clarity in an optically complex coastal water body. Specifically, we created a remote sensing water clarity product by compiling Landsat‐8 and Sentinel‐2 reflectance data with long‐term Secchi depth measurements at 12 sites over 8 years in a shallow turbid coastal lagoon system in Virginia, USA. Our satellite‐based model explained ∼33% of the variation in in situ water clarity. Our approach increases the spatiotemporal coverage of in situ water clarity data and improves estimates from bio‐optical algorithms that overpredicted water clarity. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future. Plain Language Summary: Water quality affects coastal ocean ecosystems and their ecological functions, including primary productivity, predation, and aquatic vegetation growth. An important component of water quality, water clarity, has historically been measured by tools such as Secchi disks. They are black and white painted disks that are lowered into the water column to estimate the depth at which they are no longer visible from the surface. They are a reliable, repeatable, and low‐cost tool; thus, they have contributed to extensive long‐term records of overall water quality. However, they produce gaps in space and time. Here, we created a remote sensing product to fill these gaps using open‐access high resolution satellite data and low‐cost in situ methods. Our product allows for the retrieval of water clarity data across an entire water body and at times field measurements are not available. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future. Key Points: Coupled satellite estimates with in situ observations increased the spatiotemporal coverage of water clarity estimation in a coastal ocean lagoon systemDemonstrated an accessible approach for modifying bio‐optical algorithms in optically complex coastal watersModel improved water clarity estimates, reduced biases, and decreased errors associated with Landsat‐8/Sentinel‐2 differences [ABSTRACT FROM AUTHOR]