Optical complexity of North Sea, Baltic Sea, and adjacent coastal and inland waters derived from Sentinel-3 OLCI satellite data

Abstract

Despite advances in remote sensing, consistent monitoring of water quality across freshwater-marine systems remains challenging due to methodological fragmentation. Here, we provide an overview of an exemplary dataset on water quality characteristics in inland waters, coasts, and the open sea estimated from optical satellite data (https://doi.org/10.26050/WDCC/AquaINFRA_Sentinel3_v2, Hieronymi et al., 2025). Specifically, this is Sentinel-3 OLCI (Ocean and Land Colour Instrument) data for the entire North Sea and Baltic Sea region for the period June to September 2023. The dataset includes daily aggregated observational data with a spatial resolution of approximately 300 m of reflectance at the top-of-atmosphere and for cloud-free water areas remote-sensing reflectance, inherent optical properties of the water, and an estimation of the concentrations of water constituents, e.g. related to the aquatic carbon content. These are the results of the novel A4O atmospheric correction and the ONNS water algorithm. The dataset serves as a prototype for understanding the processing chain and interdependencies, but also for developing a high degree of connectivity for answering various scientific questions; we do not perform an actual validation of the 73 individual parameters in the dataset. The challenges of a validation covering all water types are illustrated using one parameter, the particulate organic carbon concentration in water. The aim of this work is to show how fragmentation in water quality monitoring along the aquatic continuum from lakes, rivers to the sea can be overcome by applying an optical water type-specific and neural network-based processing scheme for Copernicus satellite data. Emphasis of this work is on analysing the optical complexity of remote-sensing reflectance in the North Sea, Baltic Sea, coastal, and inland waters. Results of a new optical water type classification show that almost all (99.7 %) remote-sensing reflectance spectra delivered by A4O are classifiable and that, based on this data, the region exhibits the full range of optical diversity of natural water bodies. The dataset can serve as a blueprint for a holistic view of the aquatic environment and is a step towards an observation-based digital twin component of the complex system.