Eutrophication and the subsequent effects are one of the major ecological and economical problems in the Baltic Sea
region. Two seasonal blooms, one dominated by green algae in spring and one dominated by blue-green algae in
summer, form the phytoplankton cycle in the biggest brackish sea in the world. Anthropogenic nutrient input
amplifies the phytoplankton growth. Cyanobacteria cultures dominating the summer blooms are not only capable of
fixing atmospheric nitrogen and thereby play an important role in the nitrogen cycle, but are also potentially toxic.
Dependent on a high water temperature, cyanobacteria also have a potential use as bio-indicator for climate change.
Therefor, monitoring the occurrence and extent of different phytoplankton species is of high importance for
understanding the ecosystem and human influence on it, as well as to examine possibilities of early warning
systems. With its high CDOM concentrations, the Baltic Sea is a region with very specific optical properties, which
demand for special regional algorithms, that take these properties into account. The German Aerospace Center
(DLR) in Berlin has developed a new model-based inversion algorithm using neural network technique to derive
four important water constituent parameters from MERIS satellite scenes over the Baltic Sea. Chlorophyll
concentration as a proxy for green algae, phycocyanin absorption as a proxy for cyanobacteria, CDOM absorption
and sediment scattering as further important parameters for the assessment of water quality. The algorithm shows
good compliance with in-situ measured data from ships-of-opportunity, monitoring network data and a field
campaign. Using atmospherically corrected MERIS reduced or full resolution scenes, an immediate calculation of
analysis maps is possible by the implementation in an existing software environment
