Identifying phytoplankton pigments from absorption spectra – a regional approach based on data from Case-2 waters

Introduction Determining chlorophyll-a from optical measurements and using it as a proxy for total phytoplankton biomass has long been a key approach for quantifying phytoplankton via remote sensing. However, as different phytoplankton functional types play distinct ecological roles, it is useful to differentiate between them. Some groups can be identified by unique photoactive pigments known as marker pigments, which are typically measured using High-Performance Liquid Chromatography (HPLC) - a method that needs expensive equipment and standards. Extracting information on the presence and concentration of marker pigments directly from field-measured spectral absorption data would enable faster and more cost-effective phytoplankton analysis. Methods In this study, three approaches were used to estimate pigment concentrations: a chlorophyll-a-based (Chl-a) model, Gaussian decomposition, and a partial least squares (PLS) model. To amplify the spectral features from pigments, the second and fourth derivatives of the absorption spectra were incorporated into the PLS model. The analysis is based on data from the Baltic Sea and Estonian lakes, both optically complex waters with relatively high concentrations of colored dissolved organic matter. Results and Discussion Results show that different models are better at distinguishing the various pigments. Overall, the Gaussian decomposition model has outperformed the Chl-a model, although it distinguished fewer pigments. Meanwhile, the PLS model showed promising results for detecting zeaxanthin (R 2 = 0.98, MAPD = 10.3%), a key pigment for identifying cyanobacteria.