REMOTE SENSING OF CYANOBACTERIA AND GREEN ALGAE IN THE BALTIC SEA

摘要

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.