This work is of great importance in multiple fields, such as earth science, geography, terrestrial ecosystem. These dataset could be very useful in many places. But some shortcomings in methods, figures, and tables need  careful clarification and revisement, here are comments:

1. Fig 1 is too colorful to demonstrate the core idea. Also, there is no legend to show what are different colors indicating. The relationships between different boxes are confusing too. Suggesting simplifying the figure by summarizing the main and key steps using icon and/or key words, do not put every step and all the datasets in this single figure; be consistent in color, size, and font. More detailed techniques can be in new figure.
2. Table 2. how was the newly added forest- and wetland-related subcategories defined in the new system? What are the quantitative standards for closed vs open forest? Need more justification. These subclasses will of great importance in understanding the diversity of forest and wetland ecosystem, but only if the definition and classification of these subclasses are reasonable and practical.
3. 2.3 How was the GLC_FCS30D dataset used as the training dataset for the 10m global cover mapping in this study? They are in different spatial resolutions, also, there are uncertainties in the GLC_FCS30D, not to mention the GLC_FCS30D does not cover 2023. How were all the uncertainties in the training dataset evaluated? Without solid evaluation, these training dataset cannot be high-confidence.
4. Line 277, how as the percentiles be quantified, by date? by quality or what? So was the VV VH percentiles in Line 280.
5. 4 Line 297-300 add a figure to show how was the hierarchical land cover constructed.
6. Line 307 what are the 5 × 5 geographical tiles indicating? how large is the tile, and why choosing 5X 5?
7. Fig 3 is a map not maps.
8. Table 3. why only 10 types evaluated, what about the other 20 types? what are the bottom line of OA. Kappa mean, why they are different from others?

This study developed a novel global 10-m land-cover dataset with a fine classification system. The data performs well and is of great value to high-resolution land-cover applications. Here are some of my concerns:

1. As highlighted in previous studies (Wang et al, 2023; Xu et al, 2024), Imp-ESRI_LC exhibits extensive patches of the impervious surface and lacks spatial details, which can also be found in Figure 6. This raises concerns about the MaxBound_imp (the union of several impervious surface products), which appears to include substantial areas of inner-city vegetation. If all training samples for natural land cover types are collected outside the MaxBound_imp, could this lead to the omission of inner-city vegetation types like grass and avenue trees?

See: Wang, Y., Xu, Y., Xu, X., Jiang, X., Mo, Y., Cui, H., Zhu, S., and Wu, H.: Evaluation of six global high- resolution global land cover products over China, International Journal of Digital Earth.

Xu, P., Tsendbazar, N.-E., Herold, M., de Bruin, S., Koopmans, M., Birch, T., Carter, S., Fritz, S., Lesiv, M., Mazur, E., Pickens, A., Potapov, P., Stolle, F., Tyukavina, A., Van De Kerchove, R., and Zanaga, D.: Comparative validation of recent 10 m-resolution global land cover maps, Remote Sensing of Environment.

2. Line 307, how many the samples for urban, rural and natural surfaces? The author just mentioned the ratio of these three land cover types.

3.Line 330, why divide the non-wetlands into water body, forest, grassland, bare land, and others but not the remaining 8 basic land cover types?

4. Line 356 and Line 357, the manuscript references both "LCMAP_V" and "LCMAP_AL", are these two distinct datasets?

5. Line 436, P.A. is complementary to the omission error, not the commission error.

6. Why was the land cover type “Impervious surface” written as “Developed” in Table 5? These two have different definitions. 

7. The confusion matrix (Table 5) shows that GLC_FCS10 misclassifies a large proportion of actual vegetation types as developed land (44 cropland, 69 forest and 164 grass/shrub reference samples are misclassified as developed land by GLC_FCS10), resulting in low UA for developed land, while only 4 developed land reference samples are misclassified elsewhere. This contradicts the statement in Line 433-435.

The manuscript presents a new 10 m global land cover product, developed using a hierarchical methodology. This is a valuable and well-constructed contribution to global land cover mapping. The product is validated with over 56,000 samples and the LCMAP_Val dataset, which strengthens the credibility of the reported accuracy. However, several questions need further clarification to enhance the methodological rigor:

1. Line 152: Why are impervious surfaces and wetlands treated separately from other land cover classes? While it is understandable that impervious surfaces are structurally different from natural land covers, the rationale for treating wetlands separately is less clear. Wetlands typically consist of a mix of vegetation and soil types, which may overlap with other land cover categories. Could this separation introduce additional uncertainty or classification confusion by inadvertently including other vegetation types? Please provide a clearer justification for this decision, especially compared to classifying all types together.
2. Line 167: In the context of 10-meter resolution imagery, how are closed forests and open forests defined and differentiated? Given the spatial resolution, the criteria used to distinguish these two types may significantly influence classification reliability.
3. Line 191: Regarding the use of MaxBound, does it only constrain the region from which training samples are selected, or does it also limit the area where land cover classification is applied? Please clarify its role in both processes.
4. Lines 277 and 289: It appears that you apply a percentile-based compositing method to mitigate the impact of clouds and shadows, thereby avoiding the direct use of full time series satellite data. However, this method does not fully eliminate inter-tile differences, particularly in areas with high cloud frequency. Consequently, mosaic seams may still be visible. Could the authors clarify whether any additional techniques were applied to address these residual seams?
5. Line 345: What happens if a specific land cover type is absent from the training samples within a 3×3 tile region due to limited sample size? Would this omission lead to that type being entirely excluded from prediction in the region? If so, how does the method ensure completeness of classification in regions with rare or underrepresented classes?
6. Line 408: Why is the confusion matrix reported for only 16 land cover types, while the final product contains 30 classes? Please explain the rationale behind this evaluation subset and whether accuracy metrics for the remaining classes are available.