AUTHOR=Martínez-Vicente Víctor , Evers-King Hayley , Roy Shovonlal , Kostadinov Tihomir S. , Tarran Glen A. , Graff Jason R. , Brewin Robert J. W. , Dall'Olmo Giorgio , Jackson Tom , Hickman Anna E. , Röttgers Rüdiger , Krasemann Hajo , Marañón Emilio , Platt Trevor , Sathyendranath Shubha 

TITLE=Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 4 - 2017

YEAR=2017

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2017.00378

DOI=10.3389/fmars.2017.00378

ISSN=2296-7745

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.