%0 journal article
%@ 2296-7745
%A Evers-King, H.,Martinez-Vicente, V.,Brewin, R.J.W.,Dall´Òlmo, G.,Hickman, A.E.,Jackson, T.,Kostadinov, T.S.,Krasemann, H.,Loisel, H.,Roettgers, R.,Roy, S.,Stramski, D.,Thomalla, S.,Platt, T.,Sathyendranath, S.

%D 2017
%J Frontiers in Marine Science
%N 
%P 251 
%R doi:10.3389/fmars.2017.00251
%T Validation and Intercomparison of Ocean Color Algorithms for Estimating Particulate Organic Carbon in the Oceans
%U https://doi.org/10.3389/fmars.2017.00251
 
%X Particulate Organic Carbon (POC) plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-color signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean color data requires algorithms that are well validated, with uncertainties characterized. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean color data provided by the Ocean Color Climate Change Initiative (OC-CCI) and validated against the largest database of in situ POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of Loisel et al., 2002; Stramski et al., 2008) and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.