Abstract
The Baltic Sea is a challenging study site from an optically point of view. Its partly highly absorbing waters are mainly associated with the presence of coloured dissolved organic matter and often accompanied by non-algae absorbing particles. In addition, the Baltic Sea area is characterised by massive annual surface blooms of cyanobacteria. In Europe, the Baltic Sea is a very specific and important case study with intense user interest. In the framework of different research projects as the “Ocean Colour Climate Change Initiative”, the “SEOM OC Extreme Case 2 Waters”, and partly “MyOcean”, we aim to develop an optimised, error-characterised, regional ocean colour processor applicable to several satellite sensors, like MODIS, MERIS, VIIRS, and OLCI. The procedure, which is used to determine inherent optical properties and different water constituents’ concentrations from remote sensing reflectance, is an artificial Neural Network (NN). We provide first results of comparisons of in-situ data with different ocean colour products.
