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.

General comments

Overall: The present manuscript describes an end-to-end processing chain for generating regional ocean color products from a processing scheme that incorporates flexible choices for atmospheric correction and bio-optical algorithms through a classification scheme developed in a previous manuscript. This comprehensive approach is a realization of how optical water type classification schemes can be fully utilized in a processing chain, as both integrating intermediate products for blending, and as stand-alone products in of themselves.  The developed scheme covers a wide variety of waters, from inland systems to coastal and open oceans of the Baltic/North Atlantic region. The developed scheme addresses problems of choosing algorithms across such diversity of systems.

The presentation of the overall manuscript is reasonable. However, notable omissions hinder important aspects of the manuscript.  First, the OWT scheme, which is the central 'glue' to the processing chain, is not visualized and referenced in a previous manuscript. The OWTs are never shown or clearly explained as to what they represent.  A new section should be devoted to this.  What are the centroids and distributions of the 3 variables - this could be shown in 3-d space. What do the Rrs shapes look like, what are the spectral/optical characteristics, why are there 'a' and 'b' subdivisions - are these variations?  I think it is crucial for a table and/or figure showing the OWTs, as these are referenced throughout the paper.

Secondly, an overarching figure showing the scheme would also be very helpful.  The 'algorithm' is really a fully developed scheme that incorporates A/C with classification and production of in-water properties as products.  Using the term 'algorithm' to describe the full processing chain is somewhat mis-leading, as many readers may associate 'algorithm' with a formula for generating a specific bio-optical product from Rrs.

Lastly, the final optical products produced are not validated with any field data, but are qualitatively assessed.  Operationally, extra processing effort (=time and computing power) is required for such a processing chain, which is justifiable if this leads to product improvement. This lack of verification without comparisons with 'validation' data leaves this question dangling. While this has been demonstrated in other earlier OWT-based studies, it is left unanswered here.

Specific Comments:

L29: PACE now expands wavelength range into the UV

L44:  What is meant by the 'view'?  Is this a geometric view from satellite, or a philosophical view of ocean color?

L98-104:  Not familiar with the geographic landmarks mentioned and they are not indicated on the map

L124: so, 4 months of image data used in total?

L134:  What is meant by the 'algorithm'?  It sounds like at least part inclusion of an atmospheric correction? Should this be the 'processing scheme' or 'algorithm scheme'? Also, The 'used' algorithm could be phrased better...the 'developed' algorithm or 'proposed algorithm'?  A schematic figure showing the processing chain and/or evolution of the 'algorithm' would be a useful (critical!) figure for this section.

L153: the 'used processing chain' is a bit awkward phrasing...alternative suggestions: the 'developed processing chain'...the 'implemented processing chain'...the 'presented'. This section is a bit confusing because now it appears that daily averaging is part of the 'algorithm', along with in-water production generation.  I really think this needs to be clarified what components are involved in the 'algorithm' - A/C correction, water typing, averaging, product generation (a schematic would help here).

L155-160: Unclear how many pixels were used for the developing the OWT scheme.

L165-238: Elements described here could be better visualized if included in an overarching schematic.

L252: 'already favoured'->'promoted' or 'advanced'?

L270: Comment: the OWT scheme is based on satellite data (it was unclear up to this point).

L275: This is an important section and its a bit unclear how or what the premise of the OWT scheme is. The statement that 'Rrs is not compared directly..' and '3 optical variables' is critical, but the 3 variables should immediately be listed as it wasn't totally clear what those were in the following sentences.  Recommend listing them directly as 1), 2) and 3) here.  It appears that its OWT_area, AVW, and the NDI? Its unclear how many OWTs there are, and what their characteristics are in terms of the 3 parameters.  A figure or table would be helpful

L293: Need references for 'fuzzy logic approach' or more description of its use.

L326: Its unclear what is meant by 'the variability is broad' - environmental, optical, both? This whole sentence is a bit ambigous and phrasing is unclear: the sentence continues with 'possible problems of the water algorithms used', which now switches topic from optical variability (leading to?) higher uncertainties in optical products from applied algorithms (that is how I interpret this, which could be wrong).

L327: 'ambiguous variability' is ambiguous in itself.   Do the authors mean, unpredictable Rrs shapes resulting from errors in atmospheric correction?

L328: '...with A4O having a possibly erroneous tendency towards more blue water (i.e. OWT 1)': this statement is unclear - I'm not sure what this means. Can you clarify and rephrase?

L329: ' The other AC methods would probably result...' - this sounds like conjecture - was this observed?

L337: What is the reason for ascribing OWTs labeled with 'a' and 'b'?

L344: Use of the word 'visible' twice in the same sentence for different reasons is confusing - suggest rephrasing.

L345: The underlying assumptions meaning that algorithms assume optically deep conditions?

L347-348: By 'too blue', is the inference that pixels are erroneously classified?  Its unclear what the OWTs are representing without a table, figure or other explanation.

L360: A frequency map of OWTs would illustrate the permanence or variability of areas in relation to OWTs (this could be a sub-figure to Fig. 10 or 11).

L383: 'not shown' implies this was tested, but the statement says '..it would be expected..' - this needs to be clarified as to whether in fact the authors did try this, or this is speculation or based on some other result which would need references in that case.

L390: Its not evident to the reader at this point what any OWTs look like or represent.  This needs to be presented as a section devoted solely to the characteristics of the OWTs earlier in the manuscript.

L396: This statement needs some references.

L414-415: Are these global numbers or over the study area?

L435: What does 'guaranteed' mean? This is a strong statement, and would recommend to modify.

L430-437: not sure if anything new or relevant from this section