The authors present a 500-m snow depth estimation method using an automated machine learning approach derived from AMSR-2 data, demonstrating good accuracy against ground-based observations. While the work shows potential, several significant issues must be addressed before the manuscript can be considered for publication in this journal.

Major Comments:

1. The introduction lacks a comprehensive review of snow depth retrieval algorithm development. Please expand this section to include a detailed discussion of recent progress in the field, citing both foundational and current works to contextualize your research.
2. The manuscript does not clearly articulate the innovation of the proposed method or its contribution to snow remote sensing research. Compared to existing snow depth retrieval studies, the work appears incremental. Please revise to explicitly highlight the novelty of your approach and its unique contributions to the field.’
3. The motivation for this work is not well-defined. The stated aim in Line 74, “to address the issues in ML models mentioned above,” is insufficient, as these issues have been partially addressed in prior publications. Clearly define the specific research gap your snow depth retrieval algorithm addresses to justify the study. Without a compelling motivation, the manuscript risks rejection.
4. Section 2.2.2 introduces the Che_AMSR2_NSD snow depth product at 500-m resolution derived from AMSR-2 data. It is unclear why a new method was developed when this product exists. Additionally, if Che_AMSR2_NSD is used as a reference dataset (i.e., “true” snow depth), discuss its uncertainties and their potential impact on the stability and reliability of your retrieval model.
5. The daily cloud-free snow cover dataset, combining MODIS and passive microwave-derived snow depth data, is mentioned. However, the manuscript does not evaluate the uncertainty in snow cover identification due to Huang’s snow cover data. Please provide a quantitative assessment and discussion of this uncertainty and its implications for your results.

Minors:

Lines 35–40: Provide references for claims about SD retrieval challenges.

Lines 41–73: Expand the literature review to include key international studies.

Line 83: Remove "in" for grammatical correctness.

Section 3.1.1: Justify the use of the 23-GHz band given its sensitivity to water vapor.

Lines 213–215: Clarify why all AMSR-2 bands and band differences were selected as input features.

Section 4.1.1: The correlation coefficient is not a robust metric for variable selection. Consider alternative methods (e.g., feature importance from ML models).

Figure 9: Revise captions for clarity (subfigures c and d are unclear).

Section 4.3: The SD-temperature relationship is well-known. Emphasize new insights (e.g., regional variability, model sensitivity) rather than restating basics.

General comments:

This paper proposed a high spatial resolution (500 meters) snow depth estimation method based on AMSR-2 brightness temperature data and automated machine learning (AutoML), significantly improving the accuracy of snow depth monitoring in the complex terrain areas of the Tibetan Plateau. 

Through the Pearson correlation coefficient, 19 key influencing factors were selected, including AMSR-2 brightness temperature, slope, and surface roughness. The method comprehensively considers the impact of various geographic and topographic factors on snow depth, enhancing the model's robustness and accuracy. But the authors ignore influence of the snow properties. How to asses the influence?

However, the paper still needs to improve the writing style and enhance the readability and standardization of the charts, and the details need to check carefully. In conclusion, we suggest a major revision.

Specific comments:

1 Line 20 “Compared with Landsat-8, the estimated SD spatial distribution is consistent with the snow cover extent on optical images, which can provide reliable data for monitoring snow cover changes in mountainous regions”. Sd consistent with SCE, and the logic is not proper.

2 Line 27 "Roof of the World,"

Please remove “,”.    It would be better use ‘’ instead of “”, which is the Chinese usage. Please check the whole manuscript.

3 Line 62 Support Vector Machine (SVR), SVM would be better than SVR.

4 In the last part of Introduction, the author need the add the objectives of your work and the contribution or potential usage to this field.

5 The art-to state progress of SD estimation is not enough, and the author need to improve it.  

6 What is the amount of SD measurement from climate stations and line measurement?  Line measurement is not a proper name. The site is mainly distributed in the middle and the east part and rarely distributed in the west part. What is the influence of limited materials on the remote sensing inversion.

7 Lin 109 Table1 application: usage.  Please sperate the usage of auxiliary data.

8 Line 114-116 Please sperate the long sentence.

9 How to deal with the different spatial resolution between different data source.

10 The width of flow line is not consistent.

11 This study marks the introduction of the PyCaret automated machine learning framework into snow depth estimation. It automates data processing and model selection, reducing human intervention while enhancing the efficiency of model selection and parameter optimization. Figure 3 is too simple and needs to improve. How to solve the overfitting?

12 Line 235-240: The introduction of SD estimation in the past should move the Introducion.

Figure 4 the color of meteorological stations and downscaled snow depth sample are too close, which are hard to tell from each other.

13 The discussion lack the comparison of your work and others and please cite more reference.  

14 The conclusion needs the quantitative data to support the conclusion.

15 Please check the reference to meet the requirements of magazine.