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Water quality monitoring in the Baltic Sea is of high ecological importance for all its neighbouring countries. They are
highly interested in a regular monitoring of water quality parameters of their regional zones. A special attention is paid to
the occurrence and dissemination of algae blooms. Among the appearing blooms the possibly toxicological or harmful
cyanobacteria cultures are a special case of investigation, due to their specific optical properties and due to the negative
influence on the ecological state of the aquatic system. Satellite remote sensing, with its high temporal and spatial
resolution opportunities, allows the frequent observations of large areas of the Baltic Sea with special focus on its two
seasonal algae blooms. For a better monitoring of the cyanobacteria dominated summer blooms, adapted algorithms are
needed which take into account the special optical properties of blue-green algae. Chlorophyll-a standard algorithms
typically fail in a correct recognition of these occurrences.
To significantly improve the opportunities of observation and propagation of the cyanobacteria blooms, the Marine
Remote Sensing group of DLR has started the development of a model based inversion algorithm that includes a four
component bio-optical water model for Case2 waters, which extends the commonly calculated parameter set chlorophyll,
Suspended Matter and CDOM with an additional parameter for the estimation of phycocyanin absorption. It was
necessary to carry out detailed optical laboratory measurements with different cyanobacteria cultures, occurring in the
Baltic Sea, for the generation of a specific bio-optical model.
The inversion of satellite remote sensing data is based on an artificial Neural Network technique. This is a model based
multivariate non-linear inversion approach. The specifically designed Neural Network is trained with a comprehensive
dataset of simulated reflectance values taking into account the laboratory obtained specific optical properties of the algae
species, according to the wavelengths of MERIS VIS/NIR bands. The input to the inversion neural network are
atmospheric corrected (Level2) MERIS bottom of atmosphere reflectances as well as viewing geometries of the sensor
from which the output maps for chlorophyll concentration, Suspended Matter concentration, CDOM absorption and
phycocyanin absorption are generated.
The paper demonstrates the theoretical basis and development of the algorithm together with a number of example
results obtained from MERIS scenes in the Baltic Sea. Furthermore it compares the phycocyanin-algorithm with the
standard DLR PCI algorithm based on the related inversion technique "Principal Component Analysis" and discusses the
different inversion approaches.
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