Refinement of Phytoplankton Functional Type Algorithms for VIIRS Ocean Color Data on the Northeast U.S. Continental Shelf

Phytoplankton are incredibly diverse in morphology, size, and function, and play an integral role in marine ecosystems and biogeochemical processes. Accurate long-term monitoring of phytoplankton community composition is essential for the management of sustainable fisheries and ocean health, and in understanding the ecological impacts of climate change. Satellite ocean color remote sensing enables the study of phytoplankton distribution and dynamics at spatial and temporal resolutions unachievable by traditional sampling methods. A variety of algorithms have been developed to obtain information on phytoplankton functional types (PFTs) and size classes (PSCs) from ocean color data on a global scale. However, local variation in inherent optical properties (IOPs), particularly in coastal and shelf systems, necessitates optimization and validation of these approaches based on in situ observations of optical and phytoplankton community variability. Here, several PFT algorithms are refined for the Northeast U.S. Continental Shelf (NES) region using coincident measurements of inherent and apparent optical properties (AOPs), phytoplankton pigments, and imaging flow cytometry. Data utilized are compiled from NASA’s SeaWiFS Bio-optical Archive and Storage System (SeaBASS) repository and collected on Northeast Fisheries Science Center’s (NEFSC) quarterly Ecosystem Monitoring (EcoMon) surveys. Optimized PFT imagery from NOAA’s Visible Infrared Imaging Radiometer Suite (VIIRS) sensor will be explored for spatial and temporal variability and placed in context of the finer details of community composition revealed by the in situ phytoplankton imaging and hyperspectral optics.