A novel approach to estimate non-algal particle absorption for improved retrieval of pigment concentrations in coastal waters

Abstract

Phytoplankton play a central role in ocean biogeochemistry and understanding their composition and variability is critical for monitoring marine ecosystem dynamics. Continuous spectrophotometric measurements (using a Seabird Sci. AC-S) along ship tracks enable high-resolution measurements of particle spectral absorption ($a_p\left(\lambda \right)$), and beam attenuation spectra ($c_p\left(\lambda \right)$), across large spatial and temporal scales. Here, we present a new method to estimate non-algal particle absorption ($a_{\mathrm{NAP}}\left(\lambda \right)$) from AC-S data, using $c_p\left(660\right)$ to derive both $a_{\mathrm{NAP}}\left(400\right)$ and the spectral slope of non-algal particles ($S_{\mathrm{NAP}}$). This method allows the estimation of the total spectra of $a_{\mathrm{NAP}}\left(\lambda \right)$ directly from $c_p\left(660\right)$, and consequently the derivation of phytoplankton absorption ($a_{\mathrm{ph}}\left(\lambda \right)$), retrieved by subtraction from AC-S $a_p\left(\lambda \right)$. Through published Gaussian decomposition approach (Chase et al., Methods in Oceanography 2013; 7:110–124), 11 associated phytoplankton pigments absorption peaks ($a_{\text{gaus}}\left(\lambda \right)$) are derived directly from $a_{\mathrm{ph}}\left(\lambda \right)$ and related with pigment concentrations from this dataset, building new $a_{\text{gaus}}\left(\lambda \right)$-pigments relationships and new coefficients. This new approach leads to improved decomposition between $a_{\mathrm{NAP}}\left(\lambda \right)$ and $a_{\mathrm{ph}}\left(\lambda \right)$ components using AC-S data, leading to a better quantification of phytoplankton composition and an improvement in the retrieval of pigments such as chlorophylls a, b and c. The improvement has been quantified in waters characterized by different optical conditions, from blue waters dominated by phytoplankton to coastal waters rich in suspended particles matter (SPM). The new method provides the greatest improvement in areas with high $a_{\mathrm{NAP}}\left(\lambda \right)$, almost halving the bias for stations characterized by these complex environmental conditions.