Remote Sensing of Water Quality in Coastal Zones on the Example of MERIS Data in the Baltic Sea

Kurzfassung

Knowledge of the actual ecological state is of vital importance for costal zone management. Due to the optical complexity of coastal waters compared to the open ocean, more sophisticated instruments and algorithms are necessary to meet the challenge. Satellite remote sensing technology allows the assessment of various physical, biological and ecological parameters of water bodies on global and regional scales. Based on an existing automated precursor processing chain for MOS–data, an operational remote sensing interpretation algorithm specifically adapted to the properties of the Baltic Sea has been developed. It allows in near real time the computation of concentration maps of phytoplankton, sediment and yellow substance. The used models and inversion method will be reviewed and demonstrated on the investigation of a time series of available MERIS data from 2003/2004. A time series of pigment concentrations of the area will be demonstrated and put a first statistical analysis. Recent results from the validation process confirm good accuracy. In this context it is evident, that automation and standardization of value added product derivation is an important basis to support near real-time request from the users side. The BMBF funded “MERIS Application and Regional Products Project” (MAPP), which is linked to the ENVISAT-AO ID1413 Generation of MERIS level 3 products for European Multidisciplinary Regional applications (GEMEL3) will be discussed as key research activities to develop and promote operational use of satellite data for monitoring and understanding processes in the Baltic Sea. Based on these results future activities and perspectives for contributions to operational monitoring systems including user support will be presented. Therefore, future work aims at the automation of the calculation within the scope of a fully automated processing chain. Corresponding modules are currently developed at the Remote Sensing Technology Institute (IMF) and German Remote Sensing Data Center (DFD).