Phytoplankton community (size fraction and pigment composition) response to environmental drivers in the river-dominated estuarine-coastal systems of the northern Gulf of Mexico from Sentinel-3 OLCI ocean color observations

Bingqing Liu1, Eurico J. D'Sa2, Kanchan Maiti3, Victor H Rivera-Monroy2 and Z. George Xue4, (1)The Water Institute of the Gulf, Baton Rouge, United States, (2)Louisiana State University, Baton Rouge, LA, United States, (3)Louisiana State University, Department of Oceanography and Coastal Sciences, Baton Rouge, LA, United States, (4)Louisiana State University, Oceanography and Coastal Sciences, Baton Rouge, LA, United States
Phytoplankton community plays an important role in the biogeochemical cycling of estuarine-coastal systems such as the northern Gulf of Mexico (nGoM) where elevated nutrients from the Mississippi River fuels eutrophication and frequent algal blooms. The phytoplankton community structure and size fractions are further modified at various temporal and spatial scales by freshwater inflows associated with variability in hydrological and meteorological forcing. Although ocean color remote sensing has been widely used to estimate Chl a (an indicator of phytoplankton biomass) in coastal waters, its application has been limited in smaller estuarine waterbodies due to low spatial resolution of current ocean color sensors (e.g., MODIS and VIIRS); furthermore, standard Chl a algorithms (e.g., OC3, using the blue/green band ratios) developed for open ocean waters are not suitable for optically-complex estuarine waters. In this study, a large in-situ dataset acquired in inland, estuarine and coastal waters of the nGoM were utilized to achieve a regional parameterization of the red-NIR ratio-based Chl a algorithm for the high-spatial and spectral resolution Sentinel 3 OLCI ocean color sensor; further, an adaptive scheme was used in combination with other standard Chl a algorithms (NN, OC4) to optimally extract Chl a in diverse water types ranging from turbid estuarine waters to clear oceanic waters in the nGOM. OLCI-Chl a maps generated from this adaptive scheme were then used to obtain OLCI-estimated phytoplankton absorption coefficients (aphy) based on a regionally tuned third order function of Chl a. Finally, a Non-Negative Least Squares (NNLS) pigment algorithm and Principle Component Analysis (PCA) were applied to satellite-derived aphy to retrieve phytoplankton pigment composition and size fraction from Sentinel 3-OLCI. The spatiotemporal response of phytoplankton taxonomy and size class revealed linkages to environmental variabilities with ecological implications in the nGOM.