Long-term distribution patterns of remotely sensed water quality parameters in Chesapeake Bay

Chesapeake Bay is the largest and one of the most productive estuaries in the U.S., where long-term monitoring and assessment of its water quality are necessary to understand trends and events in order to support management decisions. Significant progress has been made during the past decade in developing remote sensing algorithms for estimating two key water quality parameters, chlorophyll-a concentration (Chla, mg m−3) and diffuse light attenuation coefficient at 490 nm (Kd (490), m−1), from satellite ocean color measurements in oceanic, coastal, and estuarine waters. Yet deriving a robust Chla data product for Chesapeake Bay still remains a challenge because of its complex optical properties. Here, a recently developed algorithm approach (Red–Green Chlorophyll Index or RGCI, based on red–green remote-sensing reflectance (Rrs (λ)) ratios) was tested, validated, and applied to Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to establish a 14-year (September 1997 to December 2011) Chla Environmental Data Record (EDR). The new approach showed significant improvement over the traditional blue–green Rrs (λ) band-ratio algorithms (e.g., OC4, OC3M), with consistent performance for MODIS (mean relative error = 40.9%, mean ratio = 1.09) and SeaWiFS (MRE = 45.8%, mean ratio = 1.09) for Chla ranging between 1 and 50 mg m−3. Anomaly and EOF analyses revealed strong spatial gradients, seasonality, and climate-driven inter-annual changes in the satellite-based Chla EDR. These changes were highly correlated with satellite-based Kd (490) EDR, leading to the development of a Water Quality Decision Matrix (WQDM) and providing support to on-going nutrient reduction management programs for this estuary.