Nearly ten years (July 2002 to April 2012) of SCIAMACHY data, a hyper-spectral satellite sensor on-board ENVISAT, were processed by the improved, multi-target, PhytoDOAS method to monitor the biomass of coccolithophores besides diatoms and cyanobacteria. Data have been evaluated with other coccolithphore related satellite products and modeled coccolithophore distributions derived from the NASA Ocean Biogeochemical Model. The retrieval's sensitivity was assessed by using the coupled oceanic-atmospheric radiative transfer model SCIATRAN. Temporal variations of coccolithophores were investigated using satellite data in three selected regions characterized by frequent occurrence of large coccolithophore blooms. Monthly mean data were compared to related satellite products, including the total surface phytoplankton, i.e., total chlorophyll-a (from GlobColour merged data) and the particulate inorganic carbon (from MODIS-Aqua). In addition, the inter-annual variations of the phytoplankton bloom cycles and their maximum monthly mean values were compared in the three selected regions to the variations of the following geophysical parameters: sea-surface temperature (SST), mixed-layer depth (MLD) and surface wind speed, which are known to affect phytoplankton dynamics. PhytoDOAS data are consistent with the two other ocean color products and support the reported dependencies of coccolithophore biomass' dynamics to the compared geophysical variables. These results suggest that multi-target PhytoDOAS is a valid method for retrieving coccolithophores' biomass and for monitoring their bloom developments in the global oceans.