Water Quality Monitoring Using Landsat 8 OLI in Pleasant Bay, Massachusetts, USA

Water quality monitoring is essential to assess and manage anthropogenic eutrophication, especially for coastal estuaries in heavily populated areas. Current monitoring techniques rely on in situ sampling, which can be expensive and limited in spatial and temporal coverage. Satellite remote sensing, using the Landsat 8 (Operational Land Imager, OLI) platform, has the potential to provide more extensive coverage than traditional methods. Coastal waters are optically more complex and often shallower and more enclosed than the open ocean, presenting conditions that pose challenges to remote sensing approaches. Here, we compared in situ data from 18 stations around Pleasant Bay, Massachusetts, USA from the years 2014–2021 to contemporaneous observations with Landsat 8 OLI. Satellite-derived estimates of chlorophyll-a and Secchi depth were acquired using various algorithms including the “Case-2 Regional/Coast Color” (C2RCC), “Case-2 Extreme” (C2X), l2gen processor, and a random forest machine learning algorithm. Based on our results, predictions of water quality indices from both C2RCC and random forest techniques can be a useful addition to existing water quality monitoring efforts, potentially expanding both spatial and temporal coverage of monitoring efforts.