Estimation of Snow Depth from AMSR-2 Based on an AutoML method over the Qinghai-Tibet Plateau

Li, Xuan; Xu, Fan; Zhang, Chen; Zhang, Yanli

doi:10.5194/egusphere-2025-2875

Preprints

https://doi.org/10.5194/egusphere-2025-2875

Preprints

15 Jul 2025

| 15 Jul 2025

Estimation of Snow Depth from AMSR-2 Based on an AutoML method over the Qinghai-Tibet Plateau

Xuan Li, Fan Xu, Chen Zhang, and Yanli Zhang

Abstract. Snow depth (SD) is a crucial parameter for describing the spatiotemporal variations of snow cover, and passive microwave SD products (10–25 km) are widely used for monitoring SD changes. However, as one of the three major snow-covered regions in China, the Qinghai-Tibet Plateau (QTP) has complex terrain and rapid change in snow cover with strong spatial heterogeneity, making it difficult for coarse-resolution SD products to accurately describe its spatiotemporal characteristics. This study proposes a high spatial resolution (500 m) SD estimation method based on AMSR-2 brightness temperature (BT) data and an Automated Machine Learning (AutoML). Firstly, using Pearson correlation coefficient, 19 key factors influencing SD, including AMSR-2 BT, slope, and surface roughness, were selected as input data (independent variables) for AutoML. Meanwhile, a passive microwave downscaled SD data and ground-based SD measurements were introduced as dependent variables for AutoML. Then, the AutoML model was trained separately for four different types of snow cover surfaces (forest, grassland, water, and bare land). Finally, through the ten folds cross validation method, the optimal machine learning model for SD estimation under each type of underlying surface coverage was selected, thus sequential SD datasets were obtained for ten-year snow cover periods of the QTP from 2012 to 2021. Results show that the estimated SD values are consistent with ground-based observations (R=0.81), and the accuracy is high with an RMSE of 3.65 cm. 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.

Received: 17 Jun 2025 – Discussion started: 15 Jul 2025

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Journal article(s) based on this preprint

22 May 2026

Estimation of snow depth from AMSR-2 based on an AutoML method over the Qinghai-Tibet Plateau

Xuan Li, Fan Xu, Chen Zhang, Tao Che, Liyun Dai, and Yanli Zhang

The Cryosphere, 20, 2977–2997, https://doi.org/10.5194/tc-20-2977-2026,https://doi.org/10.5194/tc-20-2977-2026, 2026

Short summary

Xuan Li, Fan Xu, Chen Zhang, and Yanli Zhang

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-2875', Anonymous Referee #1, 16 Aug 2025
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:
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.

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.’

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.

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.

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.
Citation: https://doi.org/10.5194/egusphere-2025-2875-RC1
- AC1: 'Reply on RC1', Xuan Li, 02 Oct 2025
  
  Dear reviewer,
  Thank you sincerely for your attention to, and review of, this article. We also appreciate your constructive feedback, which has given us valuable insights into how we can improve our research. We have carefully considered each of your comments and provided detailed responses. For the areas that you suggested needed supplementing and improvement, we have also provided relevant explanations and supporting materials. The specific modification instructions and responses are provided in the attached document for your reference.
  
  Citation: https://doi.org/10.5194/egusphere-2025-2875-AC1
RC2:
'Comment on egusphere-2025-2875', Anonymous Referee #2, 20 Aug 2025

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.

Citation: https://doi.org/10.5194/egusphere-2025-2875-RC2
- AC2: 'Reply on RC2', Xuan Li, 02 Oct 2025
  
  Dear reviewer,
  Thank you sincerely for your attention to, and review of, this article. We also appreciate your constructive feedback, which has given us valuable insights into how we can improve our research. We have carefully considered each of your comments and provided detailed responses. For the areas that you suggested needed supplementing and improvement, we have also provided relevant explanations and supporting materials. The specific modification instructions and responses are provided in the attached document for your reference.
  
  Citation: https://doi.org/10.5194/egusphere-2025-2875-AC2

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (12 Dec 2025) by Valentina Radic

AR by Xuan Li on behalf of the Authors (12 Dec 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (12 Dec 2025) by Valentina Radic

RR by Anonymous Referee #2 (24 Dec 2025)

RR by Anonymous Referee #1 (29 Dec 2025)

Suggestions for revision or reasons for rejection

The authors have provided a general response to my concerns; however, several major issues remain inadequately addressed:
1. Innovation and Contribution: The authors state that the novelty of the study lies in “(1) the adoption of …, and (2) the first application of the PyCaret AutoML framework…” However, this description falls short of clearly articulating a significant and unique contribution to the field of snow remote sensing. Merely being the first to apply a particular AutoML tool does not, by itself, constitute scientific novelty—especially when numerous recent studies have achieved highly accurate snow depth retrievals, some even outperforming the results presented here. Given the competitive landscape of snow remote sensing research, the manuscript must better justify why this work advances the field beyond incremental technical implementation. I encourage the authors to explicitly restate the core scientific innovation and its unique value to the community.
2. Motivation and Research Gap: The revised text in Lines 77–85 does not convincingly establish the study’s motivation. While machine learning methods are indeed diverse, the authors have not clearly defined the specific scientific or methodological gap their snow depth retrieval algorithm addresses. The justification should focus on “what problem remains unsolved” in current approaches and how this work provides a meaningful solution. Please reframe the motivation around a well-defined research gap.
3. Reference Dataset (Che_AMSR2_NSD) and Its Uncertainties: My previous concern regarding the use of Che_AMSR2_NSD as a reference (“true”) snow depth dataset has not been addressed. If this product serves as the ground truth in model training and validation, the authors must discuss its known uncertainties (e.g., spatial representativeness, retrieval errors, temporal mismatch) and explain how these may affect the stability, reliability, and generalizability of their retrieval model.
4. Uncertainty in Snow Cover Data: The manuscript mentions a daily cloud-free snow cover dataset that fuses MODIS and passive microwave-derived snow depth (based on Huang et al.’s data). However, it still lacks any quantitative assessment of the uncertainty associated with snow cover identification from Huang’s dataset. Please provide a clear evaluation—ideally with error metrics or sensitivity analyses—of how uncertainties in this snow cover input propagate into your final results and affect their interpretation.
5. Please add additional tese to quantify the contribution of each variable used in model training stage and assess the influence of snow cover properties.

Minor Correction:
Please correct “SMER2” to “AMSR2” throughout the manuscript.

Hide

ED: Reconsider after major revisions (further review by editor and referees) (10 Jan 2026) by Valentina Radic

Dear Authors,

Your manuscript has now undergone a second round of review. The reviewers acknowledge the revisions you have made to improve the manuscript. While the first reviewer raised only a few minor issues, the second reviewer was more critical and provided additional comments that need to be addressed before the manuscript can be considered for publication (see the compiled comments below).

I therefore ask that you revise the manuscript accordingly and submit a revised version together with a detailed, point-by-point response to all reviewer comments.

Best regards,
Valentina

--
Reviewer 1:

1.The last section of the introduction is excessively long and should be made brief.
2. Please Harmonize reported performance metrics in Abstract and Conclusion. For example, R and R2 are both used.
3 “10% off cross-validation” in Figure 2 is “10-fold cross-validation”？ Please standardize the terminology.
4 The feature selection is based on correlation coefficients， why not the importance score?
5 What the influence of land cover use on SD estimation? The manuscript uses CNLUCC 2020 for the entire 2012–2021 period, and the discussion should explain this.

Reviewer 2:

Please ask author to polish their manuscript by a native english speaker.

The authors have provided a general response to my concerns; however, several major issues remain inadequately addressed:
1. Innovation and Contribution: The authors state that the novelty of the study lies in “(1) the adoption of …, and (2) the first application of the PyCaret AutoML framework…” However, this description falls short of clearly articulating a significant and unique contribution to the field of snow remote sensing. Merely being the first to apply a particular AutoML tool does not, by itself, constitute scientific novelty—especially when numerous recent studies have achieved highly accurate snow depth retrievals, some even outperforming the results presented here. Given the competitive landscape of snow remote sensing research, the manuscript must better justify why this work advances the field beyond incremental technical implementation. I encourage the authors to explicitly restate the core scientific innovation and its unique value to the community.
2. Motivation and Research Gap: The revised text in Lines 77–85 does not convincingly establish the study’s motivation. While machine learning methods are indeed diverse, the authors have not clearly defined the specific scientific or methodological gap their snow depth retrieval algorithm addresses. The justification should focus on “what problem remains unsolved” in current approaches and how this work provides a meaningful solution. Please reframe the motivation around a well-defined research gap.
3. Reference Dataset (Che_AMSR2_NSD) and Its Uncertainties: My previous concern regarding the use of Che_AMSR2_NSD as a reference (“true”) snow depth dataset has not been addressed. If this product serves as the ground truth in model training and validation, the authors must discuss its known uncertainties (e.g., spatial representativeness, retrieval errors, temporal mismatch) and explain how these may affect the stability, reliability, and generalizability of their retrieval model.
4. Uncertainty in Snow Cover Data: The manuscript mentions a daily cloud-free snow cover dataset that fuses MODIS and passive microwave-derived snow depth (based on Huang et al.’s data). However, it still lacks any quantitative assessment of the uncertainty associated with snow cover identification from Huang’s dataset. Please provide a clear evaluation—ideally with error metrics or sensitivity analyses—of how uncertainties in this snow cover input propagate into your final results and affect their interpretation.
5. Please add additional tese to quantify the contribution of each variable used in model training stage and assess the influence of snow cover properties.

Minor Correction:
Please correct “SMER2” to “AMSR2” throughout the manuscript.

Hide

AR by Xuan Li on behalf of the Authors (19 Feb 2026) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (09 Mar 2026) by Valentina Radic

AR by Xuan Li on behalf of the Authors (11 Mar 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (30 Mar 2026) by Valentina Radic

AR by Xuan Li on behalf of the Authors (02 Apr 2026) Manuscript

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-2875', Anonymous Referee #1, 16 Aug 2025
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:
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.

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.’

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.

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.

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.
Citation: https://doi.org/10.5194/egusphere-2025-2875-RC1
- AC1: 'Reply on RC1', Xuan Li, 02 Oct 2025
  
  Dear reviewer,
  Thank you sincerely for your attention to, and review of, this article. We also appreciate your constructive feedback, which has given us valuable insights into how we can improve our research. We have carefully considered each of your comments and provided detailed responses. For the areas that you suggested needed supplementing and improvement, we have also provided relevant explanations and supporting materials. The specific modification instructions and responses are provided in the attached document for your reference.
  
  Citation: https://doi.org/10.5194/egusphere-2025-2875-AC1
RC2:
'Comment on egusphere-2025-2875', Anonymous Referee #2, 20 Aug 2025

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.

Citation: https://doi.org/10.5194/egusphere-2025-2875-RC2
- AC2: 'Reply on RC2', Xuan Li, 02 Oct 2025
  
  Dear reviewer,
  Thank you sincerely for your attention to, and review of, this article. We also appreciate your constructive feedback, which has given us valuable insights into how we can improve our research. We have carefully considered each of your comments and provided detailed responses. For the areas that you suggested needed supplementing and improvement, we have also provided relevant explanations and supporting materials. The specific modification instructions and responses are provided in the attached document for your reference.
  
  Citation: https://doi.org/10.5194/egusphere-2025-2875-AC2

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (12 Dec 2025) by Valentina Radic

AR by Xuan Li on behalf of the Authors (12 Dec 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (12 Dec 2025) by Valentina Radic

RR by Anonymous Referee #2 (24 Dec 2025)

RR by Anonymous Referee #1 (29 Dec 2025)

Suggestions for revision or reasons for rejection

Hide

ED: Reconsider after major revisions (further review by editor and referees) (10 Jan 2026) by Valentina Radic

Hide

AR by Xuan Li on behalf of the Authors (19 Feb 2026) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (09 Mar 2026) by Valentina Radic

AR by Xuan Li on behalf of the Authors (11 Mar 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (30 Mar 2026) by Valentina Radic

AR by Xuan Li on behalf of the Authors (02 Apr 2026) Manuscript

Journal article(s) based on this preprint

22 May 2026

Estimation of snow depth from AMSR-2 based on an AutoML method over the Qinghai-Tibet Plateau

Xuan Li, Fan Xu, Chen Zhang, Tao Che, Liyun Dai, and Yanli Zhang

The Cryosphere, 20, 2977–2997, https://doi.org/10.5194/tc-20-2977-2026,https://doi.org/10.5194/tc-20-2977-2026, 2026

Short summary

Xuan Li, Fan Xu, Chen Zhang, and Yanli Zhang

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Mar 2026	195	147	3	345
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Short summary

The microwaves used in current products to measure snow depth don't pick up on the small changes in snow depth on the Qinghai-Tibet Plateau. To deal with these issues, our study suggests a new way of doing things. This uses AutoML, data from AMSR-2 and other geographical information. Our results show that the method we used is good for checking how snow cover changes in mountain areas.


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