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dc.contributor.authorBREWIN Roberten_GB
dc.contributor.authorSATHYENDRANATH Shubhaen_GB
dc.contributor.authorMUELLER Dagmaren_GB
dc.contributor.authorBROCKMANN Carstenen_GB
dc.contributor.authorDESCHAMPS Pierre-Yvesen_GB
dc.contributor.authorDEVRED Emmanuelen_GB
dc.contributor.authorDOERFFER Rolanden_GB
dc.contributor.authorFOMFERRA Normanen_GB
dc.contributor.authorFRANZ Bryanen_GB
dc.contributor.authorGRANT M.en_GB
dc.contributor.authorGROOM Steveen_GB
dc.contributor.authorHORSEMAN Andrewen_GB
dc.contributor.authorHU Chuanminen_GB
dc.contributor.authorKRASEMANN Hajoen_GB
dc.contributor.authorLEE Zhongpingen_GB
dc.contributor.authorMARITORENA Stephaneen_GB
dc.contributor.authorMELIN Fredericen_GB
dc.contributor.authorPETERS Marcoen_GB
dc.contributor.authorPLATT Trevoren_GB
dc.contributor.authorREGNER Peteren_GB
dc.contributor.authorSMYTH Timen_GB
dc.contributor.authorSTEINMETZ Francoisen_GB
dc.contributor.authorSWINTON Johnen_GB
dc.contributor.authorWERDELL P.j.en_GB
dc.contributor.authorWHITE Georgeen_GB
dc.identifier.citationREMOTE SENSING OF ENVIRONMENT vol. 162 p. 271-294en_GB
dc.description.abstractSatellite-derived remote-sensing reflectance (Rrs) just above the sea surface can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scales for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper, we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Data set (NOMAD). Using in situ Rrs as input to the models, the performance of eleven semi-analytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, are ranked for spectrally resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking is tested using a Monto-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflect either their immunity to scale errors or instrument noise in Rrs data, or simply that data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggest the performance of some semi-analytical algorithms at retrieving chlorophyll were comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also performed with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.en_GB
dc.description.sponsorshipJRC.H.1-Water Resourcesen_GB
dc.titleThe Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithmsen_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Sustainable Resources

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