The authors introduced a new classification system and produced a detailed land cover map of the Tibet Plateau (TP) area in 2022, which is significant for climate change studies. The method and results are well-presented. However, there are some questions or issues.

1. Lines 242–245 mentioned that the reason for not using time series is the dense cloud cover in southeastern TP. Could you provide a quantification of the cloud coverage in this region?

2. Lines 131–141. The Landsat NDVI time series from 2013 to 2022 was used to assist in selecting samples. I also noted that the study selected dense samples in the southeastern TP. Could the cloud cover in southeastern TP affect the Landsat time series from 2013 to 2022 and subsequently impact the accuracy of the sample selection?

3. In Fig. 3, the NDVI time series for evergreen needle-leaved forest, evergreen broadleaved forest, and evergreen shrubland look very similar. Can the NDVI time series effectively distinguish between these land cover types?

4. What is the proportion of samples that are directly visually interpreted from Google Earth images, samples using NDVI time series as auxiliary, and samples using only NDVI time series without Google Earth images?

5. How can you eliminate the impact of land cover changes that may have occurred between 2013 and 2022 on the Landsat time series used in the sample selection?

6. According to Table A2, impervious surfaces or built-up areas are considered as bareland in the classification system. However, I noticed that built-up areas in cities such as Xining and Lhasa are incorrectly classified as cultivated vegetation and other land cover types in your product. I also noticed that the barelands in your training samples do not seem to include built-up area samples.

7. Lines 159–161. "Interannual" refers to two or more years, but you have only selected images from one year. Did you mean "Annual"?

8. You used almost all bands from Sentinel-2 with four additional indices. The information provided by some of the bands may be duplicated. For example, the wavelengths of B8 and B8A are close. Is it sufficient to use only one of them?

9. In the comparison with other products, these products are from different years. Due to land cover changes, comparison across years will introduce some error. Using validation samples in 2022 is also unfair to products of other years. These issues need to be discussed.

10. Are there plans to update the product annually or any other future research plans?

11. Table 1, VV and VH are backscatter coefficients, not reflectivities. And it needs to be clarified with the direction of transmission and reception.

12. Typo in table 2, "DNSI" -> "NDSI".

General comments:

This manuscript ‘A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types’ produced a 10 m resolution TP land cover map to address the issue of low spatial resolution and incomplete vegetation type coverage in the existing TP land cover dataset. The generated TP_LC10-2022 product will be a valuable data for the study of this region, but the method employed in the manuscript lacks innovation. In addition, there are still many areas that need improvement.

Specific comments:

First, it is recommended to separate the data and methods sections. The current structure is somewhat confusing. It is suggested to merge sections 2.1 to 2.2 under the main heading "2. Study Area and Data." Also, starting from section 2.3 to 2.3.5, it is suggested to be included in Section 3 as "3. Methodology."

Secondly, in section 2.3.1, why did the authors state "The advantages of our classification system are as follows"? Here, the authors should introduce the basis for constructing this classification system, rather than directly discussing its advantages. Moreover, the content of this section seems more like it is introducing the basis for constructing the classification system. Moreover, what exactly is the content "Discriminability in Remote Sensing Imagery" trying to state? Is this part related to the classification system? Furthermore, didn't the authors developed their product using Sentinel data? How come 0.3m of Google Earth imagery was involved here?

Thirdly, why were the training and validation sets configured as 4:1? Typically, they are set to 7:3.

Fourth, the content within Section 2.3.3, from "Interannual remote sensing" to "The median compositing method in GEE was applied to process all bands of Sentinel-1 and Sentinel-2," is suggested to be included in Section 2.2.1. The corresponding preprocessing is suggested to describe in the presentation of the Sentinel-2 data.

Fifth, Table 2 requires an explanation for the choice of features, particularly why all bands of Sentinel-2 are utilized. For example, Band 9 is more commonly used for atmospheric monitoring applications and has a resolution of only 60 meters, so why is it included? Additionally, is it truly beneficial to resample coarse-resolution data (kilometer-scale) like CHIRPS and ERA5 to 10 meters and include them as classification features? Such coarse-resolution data may increase the "mosaic effect" of the classification results. But the most surprising thing is that the authors have clearly indicated that the temporal and phenological features can significantly help to distinguish different categories! However, it is surprising that the authors only used the median composited bands as input features instead of using temporal features. Especially for the distinction between deciduous and evergreen vegetation, is it really possible to do it by median composits alone? Furthermore, as can be seen in Figure 3, the distinction between shrubs and woodlands is quite difficult, even when relying on temporal features. Therefore, I am skeptical about the classification accuracy of these last features.

Finally, does this paper introduce any methodological innovations? Please endeavor to highlight the novel aspects of this study.