This study was dedicated to improve the PhytoDOAS method, which was established to distinguish major phytoplankton groups using hyper-spectral satellite data. Through this work the method was improved to detect also coccolithophores, another important taxonomic group, besides diatoms and cyanobacteria from SCIAMACHY data. Instead of the usual approach of the PhytoDOAS single-target fit, a simultaneous fit of a certain set of phytoplankton functional types (PFTs) was implemented within a wider wavelength fit-window, called multi-target fit. The improved method was successfully tested through detecting reported blooms of coccolithophores, as well as by comparison of the globally retrieved coccolithophores with the global distribution of Particulate Inorganic Carbon (PIC). The improved PhytoDOAS was exploited by analyzing eight years of SCIAMACHY data to investigate the temporal variations of coccolithophore blooms in a selected region within the North Atlantic, which is characterized by the frequent occurrence of intensive coccolithophore blooms. These data were compared to satellite total phytoplankton biomass, PIC conc., sea-surface temperature, surface wind speed and modeled mixed-layer depth (MLD) in order to investigate the bloom dynamics based on variations in regional climate conditions. The results show that coccolithophore blooms follow the first total chl-max and are in accordance with the PIC data. All three variables respond to the dynamics in wind speed, sea surface temperature and mixed layer depth. Overall the result prove, that PhytoDOAS is a valid method for retrieving coccolithophores' biomass and for monitoring bloom developments in the global ocean.