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Validation and intercomparison of ocean color algorithms for estimating particulate organic carbon in the oceans

Evers-King, H. L., Martinez-Vicente, V., Brewin, R. J. W., Dall'Olmo, G., Hickman, A. E., Jackson, T., Kostadinov, T. S., Krasemann, H., Loisel, H., Röttgers, R., Roy, S., Stramski, D., Thomalla, S., Platt, T. and Sathyendranath, S. (2017) Validation and intercomparison of ocean color algorithms for estimating particulate organic carbon in the oceans. Frontiers in Marine Science, 4. 251. ISSN 2296-7745

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To link to this item DOI: 10.3389/fmars.2017.00251

Abstract/Summary

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-colour 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 colour data requires algorithms that are well validated, with uncertainties characterised. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean colour data provided by the Ocean Colour 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 Stramski et al. (2008) and Loisel et al. (2002)) 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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
ID Code:71517
Publisher:Frontiers

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