Abstract
The differences among phytoplankton carbon ($C_{phy}$) predictions from six ocean colour algorithms are investigated by comparison with \textit{in situ} estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Colour Climate Change Initiative merged product. The matching \textit{in situ} data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and \textit{in situ} data provides a relatively large matching dataset (N$>$500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in $C_{phy}$. Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions ($B$ \textless 0.15 mg\,Chl\,m$^{-3}$), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from -35\% to +150\% and RMSD (unbiased) from 5 to 10 mg\,C\,m$^{-3}$ among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscattering-based algorithms produce different reasults at the clearest waters and these differences are discussed in terms of the different algorithms used for $b_{bp}$ retrieval.
Highlights
One of the standard products from ocean-color remote sensing is the concentration of chlorophylla (B) in the surface layers of the ocean, which is an estimation of phytoplankton abundance
These include methods based on particle back-scattering coefficient at a single wavelength (Behrenfeld et al, 2005; Martínez-Vicente et al, 2013); empirical relationships based on chlorophyll concentration (Sathyendranath et al, 2009; Marañón et al, 2014); and methods based on allometric considerations combined with either the spectral slope of the particle back-scattering spectrum (Kostadinov et al, 2009, 2016) or with the phytoplankton absorption characteristics (Roy et al, 2017)
The methods based on allometric structure (Kostadinov et al, 2009, 2016; Roy et al, 2017), on the other hand, have the advantage of being able to target the whole of the phytoplankton community, and partition phytoplankton carbon among any user-defined size-intervals
Summary
One of the standard products from ocean-color remote sensing is the concentration of chlorophylla (B) in the surface layers of the ocean, which is an estimation of phytoplankton abundance. A handful of algorithms have been proposed for deriving phytoplankton carbon from satellite data These include methods based on particle back-scattering coefficient (bbp) at a single wavelength (Behrenfeld et al, 2005; Martínez-Vicente et al, 2013); empirical relationships based on chlorophyll concentration (Sathyendranath et al, 2009; Marañón et al, 2014); and methods based on allometric considerations combined with either the spectral slope of the particle back-scattering spectrum (Kostadinov et al, 2009, 2016) or with the phytoplankton absorption characteristics (Roy et al, 2017). The methods based on allometric structure (Kostadinov et al, 2009, 2016; Roy et al, 2017), on the other hand, have the advantage of being able to target the whole of the phytoplankton community, and partition phytoplankton carbon among any user-defined size-intervals Comparison of these algorithms is not straightforward, because of the differences in approaches used and the products obtained. Another difficulty lies with having access to in situ data in sufficient quantity and comprehensive enough for algorithm assessment
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