Optical distinguishability of phytoplankton species and its implications for hyperspectral remote sensing discrimination potential

Abstract

Different phytoplankton types play distinct roles in marine ecosystems, biogeochemical processes, and responses to climate change. Traditionally, phytoplankton classification has heavily relied on chemical analysis methods based on phytoplankton pigments, such as High-Performance Liquid Chromatography (HPLC) analysis. This approach limits the classification resolution to the phylum level of phytoplankton, making it difficult to refine classification to the genus or species level. With the observation of the hyperspectral ocean satellite PACE (Plankton, Aerosol, Cloud, ocean Ecosystem mission) louched by NASA in February 2024, there is potential to achieve finer classification of phytoplankton based on differences in spectral characteristics. This study cultivates various phytoplankton species in the laboratory to observe their light absorption properties (e.g., specific absorption coefficients spectra under unit concentration), investigating the spectral differences between different phyla and among species within the Dinoflagellates and Diatoms. Based on the observed absorption and scattering properties of each phytoplankton species, we simulated the remote sensing reflectance of different species under various ocean color components, examining the potential of hyperspectral remote sensing discrimination of phytoplankton types, and analyzing the impact of Chlorophyll a (Chla), colored dissolved organic matter (CDOM), and non-algal particles (NAP) concentrations on the remote sensing discrimination. The results show significant differences in absorption spectra between different groups of phytoplankton (i.e., Diatoms, Dinoflagellates, Xanthophytes, Coccolithophores, Chlorophytes, Cyanobacteria, Cryptophytes). Among species within the Dinoflagellate group, there are also significant spectral differences, while species within the Diatom group exhibit relatively small variations in their spectral shapes. As Chla concentration increases, the potential for remote sensing discrimination of phytoplankton species also increases; conversely, lower Chla concentrations pose greater challenges for remote sensing disscrimiantion. Other ocean color components, such as increased CDOM or NAP concentrations, interfere with the spectral characteristics of phytoplankton in the blue-green spectral domain. Using hierarchical clustering for phytoplankton classification, the results indicate that Cyanobacteria and Chlorophytes can be well distinguished from other group at lower NAP concentrations, while Diatoms, Cryptophytes, and Xanthophytes are not easily distinguishable from each other. Differentiating between species within the same group using remote sensing data presents significant challenges. This study provides a comprehensive investigation into the optical characteristics of different phytoplankton types, laying a foundation for their remote sensing classification and deepening the understanding of the potential of hyperspectral remote sensing for detailed phytoplankton classification.