the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Aug 2025
Optical complexity of North Sea, Baltic Sea, and adjacent coastal and inland waters derived from 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. Specifically, this is Sentinel-3 OLCI 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 aim 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 reflectances delivered by A4O are classifiable and that the region exhibits the full range of optical water diversity. 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.
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Status: open (extended)
- AC1: 'Comment on essd-2025-443 - Information about changes in the underlying dataset', Martin Hieronymi, 25 Sep 2025 reply
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RC1: 'Comment on essd-2025-443', Anonymous Referee #1, 06 Nov 2025
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The manuscript documents a daily aggregated, Level‑3 dataset produced by merging all available Sentinel‑3 OLCI observations from both S3A and S3B overpasses for each day across the North Sea and Baltic Sea during June to September 2023. The processing chain uses the A4O atmospheric correction followed by the ONNS neural network water processor. The data product includes remote‑sensing reflectance at 16 OLCI bands, a suite of inherent optical properties, concentrations such as chlorophyll, TSM, DOC and POC derived from IOPs, the Forel‑Ule color index, optical water type classifications, and several quality and context flags including cloud masks, adjacency risk, glint risk, bright pixel flags, and a whitecap fraction parameterization. The paper states that this is a prototype release, that no full validation of the many variables is provided here, and that the code will only be released in the medium term. The archived dataset is now at WDCC with a DOI, CC‑BY 4.0 license, and a variables document.
The dataset targets a well known gap in ocean color for optically complex waters at the land sea interface where standard processing is often limited. Using both S3A and S3B improves daily spatial coverage, and a single pipeline across inland and coastal waters is attractive for monitoring and for synoptic biogeochemical analyses. Coupling an OWT framework to both atmospheric correction and water property retrieval is methodologically coherent, and providing OWT outputs alongside geophysical variables is useful for quality screening and for science use. As an ESSD submission, the core value is the accessible, gridded Level‑3 product with metadata, DOIs, and a usage context. These positive aspects are clear in the paper.
The manuscript explicitly states that publication of the code is only planned in the medium term and does not include a Code availability section. ESSD allows data‑only descriptions, yet it strongly encourages deposition of software and algorithms in FAIR repositories and requires a Code availability section when code is part of the work. For complex EO processing pipelines that strongly condition the resulting data, ESSD policy emphasizes transparency and reproducibility as core principles. In its current form, the work falls short of these expectations because independent users cannot reproduce the dataset or verify implementation details of A4O or the specific ONNS configuration used. At minimum, a versioned, citable container image or repository with the exact A4O and ONNS code paths, trained weights, and runtime environment is needed, together with a Code availability section that points to those DOIs.
The retrieval chain uses neural networks and an AC method. Small choices in training data, preprocessing, and band handling materially change IOPs and derived concentrations. Without the code or at least a fully specified ATBD with the exact trained model artifacts, an independent group cannot regenerate the L3 product from S3 Level‑1 data. That limits reuse and undermines the central ESSD promise of transparent, reusable data products.
The ONNS basis is documented in Frontiers in Marine Science and is citable, which is a strength. However, the present chain departs from the 2017 ONNS in key ways. The paper indicates that concentrations now come from ONNS‑derived IOPs rather than directly from class‑specific networks, and that the OWT scheme used here is the newer Bi and Hieronymi framework. Those choices are reasonable, yet they change the forward model and error propagation, so they must be documented with enough specificity to be reproducible. The A4O method has been compared against other ACs, but a full methodological description plus code or trained models are still not publicly archived.
The manuscript is explicit that it does not perform a full validation of the many variables. For an ESSD data description that is acceptable only if adequate demonstration of fitness for purpose is provided and if uncertainties and quality information are delivered in a way that users can apply. Here, the validation evidence is mostly qualitative, which is a weakness. The paper even notes a possibly erroneous blueward tendency of A4O in some conditions and that reflectance magnitude is often underestimated, which is significant because Rrs is the driver for all IOP and concentration products. Users need at least some quantitative, OWT‑stratified matchup statistics versus in situ Rrs and against IOP and concentration measurements, with uncertainty budgets that follow accepted EO data record practice. A concise validation plan can be staged, but the first ESSD version requires some validation.
The variables list in the paper and on WDCC is helpful, but several names and units would benefit from alignment with existing community standards. For NetCDF, CF conventions recommend using standard_name attributes where possible and consistent units and descriptive long_name fields. For ocean color, ESA CCI and NASA ocean color products provide a de facto vocabulary, for example RRS for remote‑sensing reflectance, CHLOR_A for chlorophyll, K_490 for diffuse attenuation at 490 nm, APH for phytoplankton absorption, ADG for CDOM‑plus‑detritus absorption, and BBP for particulate backscattering. The present ONNS variable names such as ONNS_a_g_440, ONNS_b_p_510, and the use of the term Gelbstoff for CDOM are understandable in context but may confuse users who expect CF‑style names and common ocean color acronyms.
On reflectance terminology, the manuscript lists A4O_Rrs_n as normalized remote‑sensing reflectance. In ocean color there is potential confusion between fully normalized water‑leaving radiance nLw, remote‑sensing reflectance Rrs, and various normalization schemes. The paper should define exactly what normalization means in A4O, how it differs from standard Rrs, and why the units remain sr‑1. That definition should also be embedded in the NetCDF metadata so that users do not misinterpret the quantity.
The provision of flags is welcome, including cloud masks, cloud risk near edges, adjacency, glint risk, bright pixels, and a special flag for very high biomass or floating algae. The inclusion of a whitecap fraction parameter (A4O_A_wc) is scientifically useful because whitecaps increase broadband water‑leaving signal and can bias retrievals if not handled. The whitecap parameterization is cited to satellite‑based work, which is appropriate. What is missing is a clear, file‑embedded description of how users should combine these flags for robust quality screening and what the recommended filters are for computing spatial or temporal aggregates. Given that the paper acknowledges artifacts near clouds and adjacency and a blueward bias in some regions, the dataset should come with a documented, conservative quality mask and a short tutorial for users.
The dataset is built from both Sentinel‑3A and Sentinel‑3B OLCI sensors merged to daily Level‑3. That is effectively a dual‑sensor product within a single mission. The title reads as derived from satellite data, which could be interpreted as multisensor across missions. The abstract clarifies that the source is Sentinel‑3 OLCI, and the methods section explicitly states S3A and S3B. To avoid misunderstanding, I recommend reflecting the instrument in the title or at least stating prominently on first mention that this is an OLCI‑only product that merges S3A and S3B.
Summary
ESSD requires a Data availability section and encourages authors to archive software and provide a Code availability section. The paper satisfies data availability through the WDCC DOI. It does not yet satisfy the spirit of ESSD reproducibility for algorithmic data products, because the code is not accessible and the AC is not documented at the ATBD level. ESSD explicitly invites authors to deposit code and even supports literate programming submissions to maximize transparency. This manuscript should follow that guidance for acceptance. The dataset fills a scientific gap but the present paper is not ready for acceptance because reproducibility and quantitative validation are not yet sufficient, and because naming, terminology, and user guidance need revision for broad reuse. If the authors release the processing code, add validation and/or uncertainty descriptions, align variable metadata with CF and common ocean color practice, clarify scope, and document flagging rules, I would recommend acceptance after those changes.
Citation: https://doi.org/10.5194/essd-2025-443-RC1
Data sets
Sentinel-3 OLCI daily averaged earth observation data of water constituents Martin Hieronymi et al. https://doi.org/10.26050/WDCC/AquaINFRA_Sentinel3
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For your information, we created a new version (2) of the satellite dataset and made it freely available at WDCC with new DOI. This was necessary to comply with the ESSD data license policy. The data is available without restriction under CC-BY 4.0. While reproducing the data, we made minor modifications to some metadata, including adding a reference to the description of the dataset in this ESSD article. The actual data and all values remain unchanged. A change note will be included in the final paper.
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