Development of a remote sensing algorithm for cyanobacterial phycocyanin pigment in the Baltic Sea using neural network approach

摘要

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.