Remote sensing of coccolithophore blooms in selected oceanic regions using the PhytoDOAS method applied to hyper-spectral satellite data

摘要

In this study temporal variations of coccolithophore blooms are investigatedusing satellite data. Eight years (from 2003 to 2010) of data of SCIAMACHY, ahyper-spectral satellite sensor on-board ENVISAT, were processed by thePhytoDOAS method to monitor the biomass of coccolithophores in three selectedregions. These regions are characterized by frequent occurrence of largecoccolithophore blooms. The retrieval results, shown as monthly mean timeseries, were compared to related satellite products, including the totalsurface phytoplankton, i.e. total chlorophyll a (from GlobColour mergeddata) and the particulate inorganic carbon (from MODIS-Aqua). Theinter-annual variations of the phytoplankton bloom cycles and their maximummonthly mean values have been compared in the three selected regions to thevariations of the geophysical parameters: sea-surface temperature (SST),mixed-layer depth (MLD) and surface wind-speed, which are known to affectphytoplankton dynamics. For each region, the anomalies and linear trends ofthe monitored parameters over the period of this study have been computed.The patterns of total phytoplankton biomass and specific dynamics ofcoccolithophore chlorophyll a in the selected regions are discussed inrelation to other studies. The PhytoDOAS results are consistent with the twoother ocean color products and support the reported dependencies ofcoccolithophore biomass dynamics on the compared geophysical variables. Thissuggests that PhytoDOAS is a valid method for retrieving coccolithophorebiomass and for monitoring its bloom developments in the global oceans.Future applications of time series studies using the PhytoDOAS data set areproposed, also using the new upcoming generations of hyper-spectral satellitesensors with improved spatial resolution.