The Gulf of Finland is an area of the Baltic Sea well known for frequent upwelling events (Kahru et al., 1995, Myrberg and Andrejev, 2003, Lehmann and Myrberg, 2008 and Myrberg et al., 2008). Satellite SST data have shown that during the strongest upwelling events along the northern and southern coasts of the Gulf of Finland, the upwelled XL184 clinical trial water can cover remarkably large areas, corresponding to about 40% and 20%, respectively, of the

total surface area of the Gulf (which is about 29 500 km2) (Uiboupin & Laanemets 2009). During upwelling events the surface phytoplankton community is transported offshore and replaced by species normally resident in the upper part of the thermocline (Kanoshina et al., 2003, Vahtera et al., 2005 and Lips and Lips, 2010). Numerical simulations by Zhurbas et al. (2008) and field measurements by Lips et al. (2009) have shown that in the narrow, elongated Gulf of Finland, upwelling along one coast is accompanied by downwelling along the opposite coast, i.e. two longshore baroclinic jets and RG7420 order their related thermohaline fronts develop simultaneously.

The instability of a longshore baroclinic jet leads to the increasing development of filaments and eddies, and thus coastal offshore mixing, resulting in a substantial horizontal variability of the surface layer temperature, upwelled nutrients and phytoplankton/chlorophyll. The spatio-temporal variability of hydrographic and biological-chemical parameters can be regularly monitored from autonomous ship-of-opportunity measurements

that collect temperature, salinity and chlorophyl a fluorescence data, as well as water samples for nutrient and phytoplankton analysis, along fixed transects in the Baltic Sea ( Rantajärvi et al., 1998, Lips and Lips, 2008 and Petersen et al., 2008). However, for obtaining information about the phytoplankton Succinyl-CoA abundance/biomass, and surface distribution over large sea areas, remote sensing imagery is invaluable. The Baltic Sea (including the Gulf of Finland) comprises optically complex case 2 waters that are dominated by coloured dissolved organic matter, and it is therefore a considerable challenge to produce accurate estimates of water quality parameters from remote sensing imagery ( Schroeder et al., 2007a, Sorensen et al., 2007 and Kratzer et al., 2008). This optical complexity affects satellite Chl a retrievals, so it is important to validate the algorithm using in situ measurements. Satellite imagery with sufficient temporal resolution is regularly available from MERIS (Medium Resolution Imaging Spectrometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) for the Baltic Sea region. MERIS was designed to monitor coastal waters ( Doerffer et al.