Consistent merging of satellite ocean color data sets using a bio-optical model

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Modelling of European hake nurseries in the Mediterranean Sea: An ecological niche approach (2015, methodology): “This process allowed a substantial gain of habitat coverage, similar to the gain obtained by the merging of CHL data ( 20%, Maritorena and Siegel, 2005), in relation to differences in observing time and thus in cloud cover.”
Bio-optical provinces in the eastern Atlantic Ocean and their biogeographical relevance (2011, methodology): “The GlobColour TChl-a product is based on the merging of MERIS, SeaWiFS and MODIS level-2 data with the GSM model and algorithm, developed by Maritorena and Siegel (2005), over the whole globe with the best resolution of 4.”
Colored dissolved organic matter and its influence on the satellite‐based characterization of the ocean biosphere (2005, methodology): “…of view Sensor (SeaWiFS) [McClain et al., 2004] is used to determine surface chlorophyll concentrations (Chl) using the operational empirical bio-optical algorithm (OC4v4) [O’Reilly et al., 1998, 2000] and a semi-analytical algorithm (GSM) [Maritorena et al., 2002; Maritorena and Siegel, 2005].”
An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI) (2019, methodology): “Multi-mission Rrs data records have already been used as inputs to merging schemes [57] or even produced as merged products [58–60].”