Light absorption spectra of naturally mixed phytoplankton assemblages for retrieval of phytoplankton group composition in coastal oceans

Abstract

Phytoplankton group composition is complex and highly variable in coastal waters. Given that different taxonomic groups have different pigment signatures, which in turn impact the light absorption spectra of phytoplankton, the absorption spectral-based approach has the potential for distinguishing phytoplankton groups. Using a large dataset of in situ surface observations of concurrent HPLC (high-performance liquid chromatography) pigments and phytoplankton absorption spectra collected from 2015 to 2018 in Chinese coastal oceans, in situ phytoplankton group composition was obtained from chemotaxonomic analysis (CHEMTAX). By using the linear additive principle on phytoplankton absorption spectra and CHEMTAX results, the chlorophyll-specific absorption spectra of eight phytoplankton groups were reconstructed, including prasinophytes, dinoflagellates, cryptophytes, chlorophytes, cyanobacteria, diatoms, chrysophytes, and prymnesiophytes. These chlorophyllspecific absorption spectra were subsequently used as inputs to a spectral-based inversion model for estimating phytoplankton group composition from the phytoplankton absorption coefficient. The optimal band selection and initial guesses of the phytoplankton group composition, derived from correlation and HCA (hierarchical cluster analysis) analyses, were included in the model inversion to improve the accuracy of retrievals. The performance of the proposed model was validated using an independent dataset, showing accurate estimates of chlorophyll a (Chl a) concentrations for seven phytoplankton groups (0.371 ≤ r ≤ 0.721, p < 0.05), apart from chrysophytes. Our results suggest that the absorption spectral-based approach is able to discriminate phytoplankton group composition quantitatively, which has implications for retrieving Chl a concentrations of phytoplankton groups from hyperspectral platforms and satellites.

Environmental niches and seasonal succession of phytoplankton assemblages in a subarctic coastal bay: Applications to remote sensing estimates

“…Moreover, analysis of the spectral shape of a phy and the Chla-specific a phy also revealed significant differences in the seasonal domain (Araujo and Beĺanger, 2022). Taking these results into consideration, the phytoplankton absorption can be an asset to assess the major phytoplankton assemblages in BSI, as demonstrated for diverse locations by other studies (e.g., Hoepffner and Sathyendranath, 1991;Hoepffner and Sathyendranath, 1993;Devred et al, 2006;Oliveira et al, 2021;Sun et al, 2022).…”

AIGD-PFT: The first AI-driven Global Daily gap-free 4 km Phytoplankton Functional Type products from 1998 to 2023

“…Spectral-based methods directly construct relationships between remote sensing reflectance, or absorption spectra, scattering spectra, and the concentrations of different groups, incorporating spectral transformation strategies (such as Principal Component Analysis (Xi et al, 2020), differential spectra (Bracher et al, 2009), etc.) to improve inversion accuracy (Sun et al, 2022). Considering that marine ecological environmental variables (temperature, nutrients, etc.)…”