DOI: 10.26050/wdcc/aquainfra_sentinel3_v2
title: Sentinel-3 OLCI daily averaged earth observation data of water constituents (Version 2)

description: Project: AquaINFRA - The AquaINFRA project (https://aquainfra.eu https://aquainfra.eu/work-packages/use-cases) aims to develop a virtual environment equipped with FAIR multi-disciplinary data and services to support marine and freshwater scientists and stakeholders restoring healthy oceans, seas, coastal and inland waters. The AquaINFRA virtual environment will enable the target stakeholders to store, share, access, analyse and process research data and other research digital objects from their own discipline, across research infrastructures, disciplines and national borders leveraging on EOSC (https://eosc.eu/roadmap/virtual-research-environment-vre-for-freshwater-research/ https://eosc.eu/roadmap/data-discovery-and-access-system-platform-for-freshwater-research/) and the other existing operational dataspaces. Besides supporting the ongoing development of the EOSC as an overarching research infrastructure, AquaINFRA is addressing the specific need for enabling researchers from the marine and freshwater communities to work and collaborate across those two domains.

A specific goal of AquaINFRA will be to develop an EOSC based research infrastructure combining the marine and freshwater domains, which will include the development of a cross domain and cross-country search (https://hcdc.hereon.de/datasearch/) and discovery mechanism as well as building services for spatio-temporal analysis and modelling through Virtual Research Environments. A set of strategic use cases including a Pan-European use case as well as more focused use cases in the Baltic Sea, the North Sea and the Mediterranean will provide the setting for co-designing and testing services in the targeted research communities.

The AquaINFRA project results are expected to contribute to the utilisation of EOSC as an overarching research infrastructure enabling collaboration across the domains of marine and freshwater scientists and stakeholders working on restoring of healthy oceans, seas, coastal and inland waters.
Summary: Satellite remote sensing enables global monitoring of water quality in freshwater and marine ecosystems. However, consistent data quality is a challenge due to variations in the performance of used algorithms for different waters. In this exemplary dataset, we use a novel approach for atmospheric correction and retrieval for water quality characteristics in inland waters, coastal areas, and the open sea. Copernicus Sentinel-3 OLCI satellite images are processed with the Atmospheric Correction for Optical Water Types, A4O [Hieronymi et al. in prep & 2023], and the water algorithm OLCI Neural Network Swarm, ONNS [Hieronymi et al., 2017]. ONNS derives inherent optical properties (IOPs) from which the concentrations of water constituents are estimated. In addition, the results of an Optical Water Type (OWT) classification based on A4O reflectances are provided [Bi and Hieronymi, 2024]. All available satellite data of a day for the region of interest are merged in a common grid at approximately original resolution.
An overview of the variables in the dataset can be found in the Additional Information; a detailed description of the contents and background, as well as an optical analysis of the waters, can be found in Hieronymi et al. [2025]. 
Version 2 of the dataset has the license and some metadata corrected. Data itself remains unchanged.