Please see the attachement.

Dear authors,

High-quality urban surface property dataset is vital for high-resolution urban climate modeling, this study use a series of mature methods to generate a global spatially continuous dataset (named as: U-Surf) from multisourced remote sensing observations and products, which contains radiative, morphological, and thermal properties. Overall, the methodological framework is complete and the generated products are valuable. However, there are still some issues should be solved:

1. The method descriptions should be totally strengthened.

• For example, in Section 2.2.1, I don’t know how to use the ASTER and Sentinel-2 imagery (the yearly-composited imagery or all available imagery) to calculate the single or time-series emissivity and albedo.
• As for the Eq. (4), how to determine the parameter of w_f, which follows the normal distribution? Equal distribution?
• As for the albedo and emissivity model in Eq. (2-3), how to consider their uncertainty? How do these models perform on a global scale?

Similarly, some details should be added in the Section 2.2.2 and 2.2.3.

1. My major concern is the quality of the U-Surf. Although the authors emphasized that “validating urban surface parameters on the global scale is extremely challenging primarily due to the lack of globally consistent measurement networks”, I don’t think the Table 3 can support the accuracy analysis of the U-Surf. My concerns come from that 1) the synthesized data products only part of the parameters of the retrieved models in Section 2.2, i.e., how to quantify the transformed error of synthesized data in the retrieved models; 2) as a user of the U-Surf, I also want to know its absolute accuracy not that its better than the previous data. I hope that the authors can strength the accuracy assessment.
2. The comparisons and analysis in the Figure 4 and 5 are interesting, meanwhile, I hope that authors can add some quantitative statistics. For example, in the line of 453-454, the author stated “the Global South (Latin America, Africa, and parts of Asia) generally shows lower values for these parameters and higher pervious surface fractions”. If the further statistics and analysis can be added, it may be interesting.
3. The descriptions about the TBD, HD, MD and LD should be strengthen, which has been mentioned several times in the results section.

In summary, the U-Surf dataset provides important support in urban climate modeling, and shows great advantages over the previous dataset (such as: CLMU and J2010), which meets the high-quality of the ESSD journal. I hope that above comments will help to improve the quality of this article.