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.
Hieronymi, Martin; Bi, Shun; Behr, Daniel (2025). Sentinel-3 OLCI daily averaged earth observation data of water constituents (Version 2). World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/AquaINFRA_Sentinel3_v2
SQA - Scientific Quality Assurance 'approved by author'
Result Date
2025-09-22
Technical Quality Assurance (TQA)
TQA - Technical Quality Assurance 'approved by WDCC'
Description
1. Number of data sets is correct and > 0: passed; 2. Size of every data set is > 0: passed; 3. The data sets and corresponding metadata are accessible: passed; 4. The data sizes are controlled and correct: passed; 5. The spatial-temporal coverage description (metadata) is consistent to the data: passed; 6. The format is correct: passed; 7. Variable description and data are consistent: passed
Method
WDCC-TQA checklist
Method Description
Checks performed by WDCC. The list of TQA metrics are documented in the 'WDCC User Guide for Data Publication' Chapter 8.1.1
[1] DOIHieronymi, Martin; Müller, Dagmar; Doerffer, Roland. (2017). The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters. doi:10.3389/fmars.2017.00140
[2] DOIBi, Shun; Hieronymi, Martin. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. doi:10.1002/lno.12606
[3] DOIHieronymi, Martin; Bi, Shun; Müller, Dagmar; Schütt, Eike M.; Behr, Daniel; Brockmann, Carsten; Lebreton, Carole; Steinmetz, François; Stelzer, Kerstin; Vanhellemont, Quinten. (2023). Ocean color atmospheric correction methods in view of usability for different optical water types. doi:10.3389/fmars.2023.1129876
[4] DOIHieronymi, Martin; Behr, Daniel; Bi, Shun; Röttgers, Rüdiger. (2025). Optical complexity of North Sea, Baltic Sea, and adjacent coastal and inland waters derived from satellite data. doi:10.5194/essd-2025-443