Although there are many online platforms and data distribution systems for Landsat, I appreciate OpenGeoHub’s effort in producing their own version of Landsat analysis-ready data. All processing code and metadata availability observe open-access principles, and this benefits a lot the community in reusing their data. The inclusion of precalculated annual and long-term indices offers an opportunity for improved environmental modeling and mapping. Also, this is a dataset that will evolve in time with new mapped years and the potential inclusion or refinement of the current list of products. That said, I don’t have any major objection to its publication, but I think we would greatly benefit from further clarification, which can also improve the quality of the data and the paper.

1. Why did the authors produce bimonthly products rather than monthly or even 16-day intervals? One of the greatest advantages of Landsat over Sentinel and other EO data is the long time-series archive. Good data can be fetched from 1984 since Landsat-5. As this is an evolving data cube, please consider producing at least a monthly time series, as this would allow better integration with other products like climate data. This also helps in capturing temporal changes in terrestrial ecosystems.
2. Please clarify if the ARCO bimonthly bands represent a true measurement from a scene or a synthetic value based on statistics, like an average. If it is a synthetic value, do you expect it will reduce the capacity to assess fine spatiotemporal changes in the landscape, as the synthetic image mixes different pixels with distinct temporal and spatial indexes?
3. Bare soil fraction: There are several papers indicating that NDVI alone cannot separate bare soil pixels well, as dry vegetation has a very similar spectral profile compared to soils. The classification must use a combination of indices and in some cases, land use masks. Please check the methods of Rogge et al. (2017), Diel et al. (2017), Safanelli et al. (2020), and Heiden et al. (2022).

Rogge, D., Bauer, A., Zeidler, J., Mueller, A., Esch, T., & Heiden, U. (2018). Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014). In Remote Sensing of Environment (Vol. 205, pp. 1–17). Elsevier BV. https://doi.org/10.1016/j.rse.2017.11.004.

Diek, S., Fornallaz, F., Schaepman, M. E., & De Jong, R. (2017). Barest Pixel Composite for Agricultural Areas Using Landsat Time Series. In Remote Sensing (Vol. 9, Issue 12, p. 1245). MDPI AG. https://doi.org/10.3390/rs9121245.

Safanelli, J. L., Chabrillat, S., Ben-Dor, E., & Demattê, J. A. M. (2020). Multispectral Models from Bare Soil Composites for Mapping Topsoil Properties over Europe. In Remote Sensing (Vol. 12, Issue 9, p. 1369). MDPI AG. https://doi.org/10.3390/rs12091369.

Heiden, U., d’Angelo, P., Schwind, P., Karlshöfer, P., Müller, R., Zepp, S., Wiesmeier, M., & Reinartz, P. (2022). Soil Reflectance Composites—Improved Thresholding and Performance Evaluation. In Remote Sensing (Vol. 14, Issue 18, p. 4526). MDPI AG. https://doi.org/10.3390/rs14184526.

General comments

The availability of analysable large geodata is of great importance for lowering the inhibition threshold for potential users and thus enabling informed and comprehensible decisions and political action. Against this background, the work represents an important contribution that I recommend for publication, apart from minor suggestions for changes (see below). Against the background of Data-Fitness-For-Use/Data-Fitness-For-Purpose assessment approaches (Lacagnina et al., 2022; Pôças et al., 2014; Wentz & Shimizu, 2018; Yang et al., 2013) and the associated trustworthiness of geodata (products) (Lokers et al., 2016), however, I would ask the authors also to inform how metadata or additional information can help potential users to assess the suitability of the datasets for further use.

Specific comments

• Page 2, Line 28: Spelling error: Copernicus Data Spac Ecosystem ⇒Copernicus Data Space Ecosystem
• Figure 1: Spelling error: Per-pixel count of available value ⇒Per-pixel count of available values
• Figure 2:  To make the manuscript easier to read, I suggest placing the figure at the beginning of shapter 2.3 and briefly explaining the basic methodological process with reference to the corresponding subsections.
• Page 4, Line 20:  Could you elaborate on your perception/definition of the term (spatial) “plausibility” and differentiate it from “accuracy/uncertainty”?
• Page 11, Line 20: Could you add a reference for “typical CRC values for each tillage type”?
• Page 12: Could you provide a kind of principle workflow for both modelling experiments?
• Section 2.4.4: You may consider deleting section 2.4.4 or integrating elements into the results section. For example, the explanation seems somewhat contrived “These visual representations complement the statistical analysis by highlighting spatial patterns that may”. In addition, the paragrapgh on page 12 and line 5 can be used as an introduction to the results section.
• Page 13, lines 13–26: Although in my view there is no need to list the formulas of the validation metrics (F1-score, CCC), references should at least be mentioned.
• Page 36, lines 22–34: It is not entirely clear to me why emphasis is placed on the supposed limitations of the Bare Soil Composite (BSC). In principle, BSC represent a filtered view of the Landsat and Sentinel-2 time series with a focus on agricultural areas in order to identify stable soil patterns. The “accusation” of regional applicability also does not reflect the complexity of digital soil mapping (DSM), as the transferability of DSM approaches depends on many factors such as the representativeness of soil samples, suitable explanatory variables, or DSM models that take into account the spatial variability of soil landscapes (e.g., Broeg et al., 2024). In this respect, BSF products face the same challenge. More relevant would be a discussion of differences in the generation of BSC and BSF products. This concerns, for example, approaches to temporal-dynamic filtering taking phenology into account (Zepp et al., 2023), which would be a nice feature of your products in the future.
• Page 38, lines 20-21: This result is in line with Zepp et al., 2023.
• Page38, section 4.3: Both use cases represent current topics. I would therefore welcome it if the discussion referred to a few relevant works. 
• Page 39, lines 6ff Could you support your conclusions on the feature importance and selection together with
  
  scientific references?

References

• Broeg, T., Don, A., Gocht, A., Scholten, T., Taghizadeh-Mehrjardi, R., & Erasmi, S. (2024). Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland. Geoderma, 444, 116850. https://doi.org/10.1016/j.geoderma.2024.116850
• Lacagnina, C., David, R., Nikiforova, A., Kuusniemi, M.-E., Cappiello, C., Biehlmaier, O., Wright, L., Schubert, C., Bertino, A., Thiemann, H., & Dennis, R. (2022). Towards a data quality framework for EOSC authorship community (tech. rep.). EOSC Association. https://doi.org/10.5281/zenodo.7515816
  
  Lokers, R., Knapen, R., Janssen, S., van Randen, Y., & Jansen, J. (2016). Analysis of Big Data technologies for use in agro-environmental science. Environmental Modelling & Software, 84, 494–504. https://doi.org/10.1016/j.envsoft.2016.07.017
• Pôças, I., Gonçalves, J., Marcos, B., Alonso, J., Castro, P., & Honrado, J. P. (2014). Evaluating the fitness for use of spatial data sets to promote quality in ecological assessment and monitoring. International Journal of Geographical Information Science, 28(11), 2356–2371. https://doi.org/10.1080/13658816.2014.924627
• Wentz, E. A., & Shimizu, M. (2018). Measuring spatial data fitness-for-use through multiple criteria decision making. Annals of the American Association of Geographers, 108(4),1150–1167. https://doi.org/10.1080/24694452.2017.1411246
• Yang, X., Blower, J. D., Bastin, L., Lush, V., Zabala, A., Masó, J., Cornford, D., Díaz, P., & Lumsden, J. (2013). An integrated view of data quality in Earth observation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1983), 20120072. https://doi.org/10.1098/rsta.2012.0072
• Zepp, S., Heiden, U., Bachmann, M., Möller, M., Wiesmeier, M., & VanWesemael, B. (2023). Optimized bare soil compositing for soil organic carbon prediction of topsoil croplands in Bavaria using Landsat. ISPRS Journal of Photogrammetry and Remote Sensing, 202, 287–302. https://doi.org/10.1016/j.isprsjprs.2023.06.003