Testing a Hyperspectral, Bio‐Optical Approach to Identification of Phytoplankton Community Composition in the Chesapeake Bay Estuary

• Published in: Earth and space science (Hoboken, N.J.) Vol. 11; no. 5
• Main Authors: McKibben, S. M., Schollaert Uz, S., Palacios, Sherry L.
• Format: Journal Article
• Language: English
• Published: Hoboken John Wiley & Sons, Inc 01.05.2024; American Geophysical Union (AGU)
• Subjects: Algae; Algorithms; Aquatic ecosystems; Biogeochemistry; Community composition; Cyanobacteria; Dissolved organic matter; Ecological function; Estuaries; hyperspectral; Libraries; Marine ecosystems; Microorganisms; ocean color; optically complex; Optics; Performance assessment; Phytoplankton; phytoplankton community composition; phytoplankton diversity; Pigments; Plankton; Remote sensing; remote sensing reflectance; Sensors; Taxonomy; Upper ocean; Water quality; Chesapeake Bay
• ISSN: 2333-5084; 2333-5084
• DOI: 10.1029/2023EA003244

The multi‐to hyperspectral evolution of satellite ocean color sensors is anticipated to enable satellite‐based identification of phytoplankton biodiversity, a key factor in aquatic ecosystem functioning and upper ocean biogeochemistry. 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 is evaluated in the Chesapeake Bay estuary on the U.S. East Coast. PHYDOTax is among relatively few optical‐based PCC differentiation approaches available for optically complex waters, but it has not yet been evaluated beyond the California coastal regime where it was developed. Study goals include: (a) testing the approach in a turbid estuary including novel incorporation of colored dissolved organic matter (CDOM) and non‐algal particles (NAP), and (b) performance assessment with both synthetic mixture and field data sets. Algorithm skill was robust on synthetic mixtures. Using field data, cryptophyte and/or cyanophyte phytoplankton groups were predicted, but diatom and dinoflagellate detection was not conclusive. For one field data set, small but significant improvements were observed in predicted PCC groups when tested with incorporation of CDOM and NAP into the algorithm, but not for the second field data set. Sensitivity to three hyperspectral‐relevant spectral resolutions (1, 5, 10 nm) was low for all field and synthetic data. PHYDOTax can identify some phytoplankton groups in the estuary using hyperspectral, field‐collected measurements, but validation‐quality data with broad temporospatial coverage are needed to determine whether the approach is robust enough for science applications. Key Points A hyperspectral, bio‐optical approach to identifying phytoplankton types from remote sensing reflectance is tested in a turbid estuary In field data, cyanophytes and cryptophytes were typically detected but diatom and dinoflagellate detection was not conclusive Work adds support to further testing this approach in optically complex waters to assess its potential for science applications