Articles | Volume 12, issue 2
Ocean Sci., 12, 561–575, 2016
Ocean Sci., 12, 561–575, 2016

Research article 18 Apr 2016

Research article | 18 Apr 2016

Carbon-based phytoplankton size classes retrieved via ocean color estimates of the particle size distribution

Tihomir S. Kostadinov et al.

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Cited articles

Agawin, N. S. R., Duarte, C. M., and Agusti, S.: Nutrient and temperature control of the contribution of picoplankton to phytoplankton biomass and production, Limnol. Oceanogr., 45, 591–600, 2000.
Alvain, S., Moulin, C., Dandonneau, Y., and Loisel, H.: Seasonal distribution and succession of dominant phytoplankton groups in the global ocean: A satellite view, Global Biogeochem. Cy., 22, GB3001,, 2008.
Antoine, D., André, J. M., and Morel, A.: Oceanic primary production 2. Estimation at global scale from satellite (coastal zone color scanner) chlorophyll, Global Biogeochem. Cy., 10, 57–69, 1996.
Behrenfeld, M. J. and Falkowski, P. G.: A consumer's guide to phytoplankton primary productivity models, Limnol. Oceanogr., 42, 1479–1491, 1997a.
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from satellite-based chlorophyll concentration, Limnol. Oceanogr., 42, 1–20, 1997b.
Short summary
Recent advances in ocean color remote sensing have allowed the quantification of the particle size distribution (and thus volume) of suspended particles in surface waters, using their backscattering signature. Here, we leverage these developments and use volume-to-carbon allometric relationships to quantify phytoplankton carbon globally using SeaWiFS ocean color data. Phytoplankton carbon concentrations are partitioned among three size classes: picoplankton, nanoplankton and microplankton.