Description: In this study temporal variations of coccolithophore blooms are investigated using satellite data. Eight years, from 2003 to 2010, of data of SCIAMACHY, a hyper-spectral satellite sensor on-board ENVISAT, were processed by the PhytoDOAS method to 5 monitor the biomass of coccolithophores in three selected regions. These regions are characterized by frequent occurrence of large coccolithophore blooms. The retrieval results, shown as monthly mean time-series, 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). The 10 inter-annual variations of the phytoplankton bloom cycles and their maximum monthly mean values have been compared in the three selected regions to the variations of the geophysical parameters: sea-surface temperature (SST), mixed-layer depth (MLD) and surface wind speed, which are known to affect phytoplankton dynamics. For each region the anomalies and linear trends of the monitored parameters over the period of this 15 study have been computed. The patterns of total phytoplankton biomass and specific dynamics of coccolithophores chlorophyll-a in the selected regions are discussed in relation to other studies. The PhytoDOAS results are consistent with the two other ocean color products and support the reported dependencies of coccolithophore biomass’ dynamics to the compared geophysical variables. This suggests, that PhytoDOAS 20 is a valid method for retrieving coccolithophore biomass and for monitoring its bloom developments in the global oceans. Future applications of time-series studies using the PhytoDOAS data set are proposed, also using the new upcoming generations of hyper-spectral satellite sensors with improved spatial resolution.

Description: We are proposing the development of an algorithm, using the combination of data from OCLI (Sentinel-3) and Sentinel-5P sensors, which derives globally pyhtoplankton groups (phytoplankton functional types-PFT) biomass.. The information of the total biomass will be achieved by standard processing of the Chlorophyll-a (chl-a) concentration using satellite data from multispectral imaging instruments (firstly SeaWiFS, MODIS and MERIS merged within the GlobColour data set, later OLCI data). The percentage of the main phytoplankton types on the total biomass will be retrieved by the analysis of characteristic absorption features in hyperspectral satellite measurements (firstly SCIAMCHY, later Sentinel-5-P) using the PhytoDOAS method by Bracher et al. (2009) and improved by Sadeghi et al. (2011). Thus, a synergistic product from information of multi- and hyperspectral satellite instruments which complements one another will be developed. The two instruments of the Sentinel mission will enbale a data product of weekly to monthly temporal and 7 km by 7 km spatial reoslution, as opposed to the SCIAMACHY/Globcolour product (starting in 2002 until today) which will be limited to a monthly and 0.5 degree resolution. The application of the algorithm is for assessing the spatial and temporal variability of specific phytoplankton types’ biomass on global scale in order to engross the understanding of the role of different phytoplankton types in the world ocean’s ecosystem and to improve estimates on the contribution of different phytoplankton types to the global carbon cycle. The concept of the algorithm development, including its uncertainity determined via validaton with in-situ phytoplankton data and sensitivity studies using the coupled atmospheric-oceanic radiative transfer model SCIATRAN (Rozanov et al. 2002, Blum et al. 2012) and examples for its application are given in the presentation.