Testing a hyperspectral, bio-optical approach for identification of phytoplankton groups in the Chesapeake Bay

<p>The multi- to hyperspectral evolution of satellite ocean color sensors is advancing space-based coastal ocean science and applications. Among anticipated new capabilities is satellite-based identification of phytoplankton groups. In this work the bio-optical Phytoplankton Detection with Optics (PHYDOTax) approach for deriving taxonomic class-level phytoplankton community composition (PCC, e.g. diatoms, dinoflagellates) from hyperspectral information (<= 10 nm spectral resolution) is evaluated in the Chesapeake Bay on the East Coast of the United States. PHYDOTax is among relatively few regionally customizable, optical PCC differentiation approaches available for optically complex water, but these features have not been tested beyond the California coastal regime where it was initially developed. Study goals include: 1) regional parameterization to an enclosed estuary, including novel addition of colored dissolved organic matter (CDOM) and non algal particles (NAP) to the algorithm, and 2) performance assessment using field-based remote sensing reflectance and pigment data from two cruise campaigns. Algorithm testing was conducted at spectral resolution settings relevant to hyperspectral sensors (e.g. 1nm, 5nm, 10nm) and with and without incorporation of CDOM and NAP. Statistical performance was typically robust for cryptophyte and cyanophyte phytoplankton groups with variable to poor results for dinoflagellate and diatom groups. Small, but significant, differences were observed in algorithm output at varied spectral resolutions, but no significant differences were observed in runs with or without CDOM and NAP. Based on these datasets, PHYDOTax is able to differentiate some phytoplankton groups in an estuary. The approach warrants further investigation with in estuaries and other optically complex regimes.</p>