Elevation Changes of A’nyemaqen Snow Mountain Revealed with Satellite Remote Sensing
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsASTER L1A V003, TanDEM-X and ICESat-2 data were used to investigate the temporal and spatial elevation change and evaluate the accuracy of A'nyemaqen Snow Mountain. Random Forest model was applied to quality the contribution of two climatic factors on elevation changes.
Minor Comments:
1. Add more works in the literature in the introduction of your work:
2. Improve the writing. For example, some sentences can be simplified for better readability.
3. The figures can be improved. For example, the fonts can be better in Figs. 2-4.
4. Add some comparison between your results with the previous studies in ASM.
5. The forecast shows the stable of the glacier, and add a simple analysis of the reason.
Comments on the Quality of English LanguageThe English is well written.
Author Response
Dear reviewer:
Re: Manuscript ID: remotesensing-3009027 and Title: Elevation Changes of A'nyemaqen Snow Mountain Revealed with Satellite Remote Sensing.
Thank you for your comments concerning our manuscript. Those comments are valuable and very helpful. We have read through comments carefully and have made corrections. Based on the instructions provided in your comments, we uploaded the file of the revised manuscript. The responses to the reviewer’s comments are marked in red and presented following.
We would love to thank you for allowing us to resubmit a revised copy of the manuscript and we highly appreciate your time and consideration
Q1: Add more works in the literature in the introduction of your work.
Response: Thank you for your careful review. We have added more describe on how we generate Digital Elevation Model (DEM) time series in abstract section (L14-L17):
‘To investigate spatial-temporal elevation change in ASM, 21-year Digital Elevation Model (DEM) time series was obtained using MicMac ASTER (MMASTER) algorithm with ASTER L1A V003 data. It covers the period from January 2002 to January 2023.’
Q2: Improve the writing. For example, some sentences can be simplified for better readability.
Response: Thank you for your careful review. We are very sorry for the mistakes in this manuscript and inconvenience they caused in your reading. The manuscript has been thoroughly revised. We hope it can meet the journal’s standard.
Q3: The figures can be improved. For example, the fonts can be better in Figs. 2-4.
Response: Thank you for your careful review. We have revised the Figs. 2-5 with clear labels and legends. It can be ensured that the manuscript meets the journal’s standards. We believe these revisions have improved the clarity and readability of the manuscript.
Q4: Add some comparison between your results with the previous studies in ASM.
Response: Thank you for your useful suggestion. We have added the comparison between our results and the previous studies in ASM as well as other glaciers in section 5.1 (L287-L289):
‘We estimated that ASM experienced a decrease at a rate of -0.38±0.16 m/a during 2002 to 2023. This is close to that of -0.42±0.07 m/a between 2000 and 2018, indicating the stable of ASM after 2018. ’
Q5: The forecast shows the stable of the glacier, and add a simple analysis of the reason.
Response: Thank you for your useful suggestion. We have added an analysis of forecasting results from a modeling perspective in L356-L360. We are sure these revisions can enhance the readability of the manuscript.
‘The ARIMA model predicts the future based solely on historical data, using only current and past information. It handles only univariate time series and cannot incorporate other factors. Therefore, there is some uncertainty in its long-term prediction results. Additionally, the time series must satisfy the assumption of smoothness, which leads to the forecasting results will not have particularly large fluctuations. ’
We hope that the revised manuscript is accepted for publication in the Remote Sensing.
Best regards.
Mr. Huai Lin
School of Geodesy and Geomatics, Wuhan University
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsUsing ASTER L1A V003 and TanDEM-X data, this paper employs a robust methodology to measure elevation changes between 2002 and 2023. The use of ARIMA modeling to predict future changes is also commendable. It provides significant insights into the elevation changes of the A'nyemaqen Snow Mountain, contributing to the field of remote sensing and glaciology.
Minor revisions:
1. The abbreviations in the title, abstract, figure and table captions can be avoided.
2. In several sections, the sentence structure is somewhat convoluted. For instance, in the abstract and the Section 3.1 Data section.
3. Add more describe on the significance of these datasets.
4. Adding more figures of elevation changes in the appendices.
5. Add more result comparisons between A'nyemaqen Snow Mountain and other regions of the Qinghai-Tibetan Plateau region.
6. Please revise the figures with clear labels, annotations and text.
Author Response
Dear reviewer:
Re: Manuscript ID: remotesensing-3009027 and Title: Elevation Changes of A'nyemaqen Snow Mountain Revealed with Satellite Remote Sensing.
Thank you for your comments concerning our manuscript. Those comments are valuable and very helpful. We have read through comments carefully and have made corrections. Based on the instructions provided in your comments, we uploaded the file of the revised manuscript. The responses to the reviewer’s comments are marked in red and presented following.
We would love to thank you for allowing us to resubmit a revised copy of the manuscript and we highly appreciate your time and consideration
Q1: The abbreviations in the title, abstract, figure and table captions can be avoided.
Response: Thank you for your careful review. We have made these adjustments to ensure that there are no abbreviations in the title, abstract, figure and table captions. We hope it can meet the journal’s standard.
Q2: In several sections, the sentence structure is somewhat convoluted. For instance, in the abstract and the Section 3.1 Data section.
Response: Thank you for your careful review. In response to the part of the sentence structure you mentioned that is complex, we have revised it accordingly as L23-L26. We believe these revisions have improved the clarity and readability of the manuscript.
‘With the elevation time series and climate data from ERA5 datasets, we applied random forest technique and found that the temperature is the main factor to elevation change in ASM. Furthermore, the response of elevation changes to temperature appeared with lag, and varied with location. ’
Q3: Add more describe on the significance of these datasets.
Response: Thank you for your useful suggestion. We have added more describe on the significance of ASTER and ICESat-2 datasets in L110-L112 and L130-L133. We hope it can enhance the readability of the manuscript.
‘ASTER remote sensing data covers wide areas with a revisit period of approximately 16 days, facilitating multiple captures of the same area to monitor land surface changes. Moreover, it is freely available.’
‘Studies have shown that the accuracy of ATL08 terrain height data can reach 0.70 m. Compared to ATL06, ATL08 product offers a broader coverage with nearly consistent accuracy in non-vegetated areas across both summer and winter.’
Q4: Adding more figures of elevation changes in the appendices.
Response: Thank you for your useful suggestion. We have added elevation change maps for January 2019, January 2015, January 2011 and January 2007 compared to January 2002 in appendices. We believe these revisions can better showcase our work.
Q5: Add more result comparisons between A'nyemaqen Snow Mountain and other regions of the Qinghai-Tibetan Plateau region.
Response: Thank you for your useful suggestion. We have added a new section 5.1 (L289-L293) in discussion section and made the comparison between our results other glaciers of the Qinghai-Tibetan Plateau region. We are sure these revisions can enhance the readability of the manuscript.
‘Compared with that of HMA, it is similar with that of 0.34±0.07 m/a in HMA. Moreover, highly spatial variation was observed across HMA. Positive elevation rate 0.14±0.10 m/a was observed in western Kunlun, followed by ASM and Tianshan -0.38±0.16 and -50±0.10 m/a, respectively. Furthermore, the largest decrease rate -1.07±0.10m/a was shown in Nyainqentanglha region.’
Q6: Please revise the figures with clear labels, annotations and text.
Response: Thank you for your careful review. We have revised the Figs. 2-5 with clear labels and legends. We hope it can meet the journal’s standards.
We hope that the revised manuscript is accepted for publication in the Remote Sensing.
Best regards.
Mr. Huai Lin
School of Geodesy and Geomatics, Wuhan University
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThis paper focuses on measuring elevation changes in A'nyemaqen Snow Mountain with a Digital Elevation Model (DEM) time series from 2002 to 2023. The utilization of the random forest technique to identify the main meteorological contributor to the elevation change is appropriate, presenting a practical approach to analyze the relationship between glaciers and climate.
The paper could become more appealing and comprehensible with slight enhancements, particularly in areas of technical description and data visualization.
1. In several sections, especially the abstract, simplifying some sentences with complex structures could help readers understand. The abstract should succinctly summarize the entire text.
2. Some figures, such as the corresponding seasonal elevation and temperature time series, could benefit from clearer legends and labels to help readers comprehend them quickly. Additionally, the color scheme of some figures, particularly the test set prediction results of the RF model, is not visually appealing enough.
3. Adding more discussion about the limitations of the methodology used to generate the DEM time series could help provide a comprehensive understanding and serve as a reference for future research.
4. While the data adequately supports the conclusions, presenting them in a more engaging and appealing way could better convey their significance and impact.
5. The sections including data introduction, methods are not detailed enough.
6. About the validation dataset, it will be better to explain the reason why choosing L3A Land and Vegetation Height V008 (ATL08) product, why not choosing Land Ice Along-Track Height Product (ATL06) product?
7. Please provide some more detailed information about ASTER DEMs, such as the numbers of DEMs from ASTER images.
8. About the data preprocessing (L208-L210), I think it should be in the methodology section.
9. The DEM verification should be carried out over glacier-free areas. It should be explained in the manuscript.
Comments on the Quality of English Language
There are some syntax errors in this manuscript.
Author Response
Dear reviewer:
Re: Manuscript ID: remotesensing-3009027 and Title: Elevation Changes of A'nyemaqen Snow Mountain Revealed with Satellite Remote Sensing.
Thank you for your comments concerning our manuscript. Those comments are valuable and very helpful. We have read through comments carefully and have made corrections. Based on the instructions provided in your comments, we uploaded the file of the revised manuscript. The responses to the reviewer’s comments are marked in red and presented following.
We would love to thank you for allowing us to resubmit a revised copy of the manuscript and we highly appreciate your time and consideration
Q1: In several sections, especially the abstract, simplifying some sentences with complex structures could help readers understand. The abstract should succinctly summarize the entire text.
Response: Thank you for your careful review. In response to the part of the sentence structure you mentioned that is complex, we have revised it accordingly as L23-L26. We believe these revisions have improved the clarity and readability of the manuscript.
‘With the elevation time series and climate data from ERA5 datasets, we applied random forest technique and found that the temperature is the main factor to elevation change in ASM. Furthermore, the response of elevation changes to temperature appeared with lag, and varied with location. ’
Q2: Some figures, such as the corresponding seasonal elevation and temperature time series, could benefit from clearer legends and labels to help readers comprehend them quickly. Additionally, the color scheme of some figures, particularly the test set prediction results of the RF model, is not visually appealing enough.
Response: Thank you for your careful review. We have revised the Figs. 2-5 with clear labels and legends. What’s more, the color scheme of Fig 5. has been adjusted as well. We hope it can meet the journal’s standards.
Q3: Adding more discussion about the limitations of the methodology used to generate the DEM time series could help provide a comprehensive understanding and serve as a reference for future research.
Response: Thank you for your useful suggestion. We have added a new section 5.1 (L283-L286) in discussion section and discussed the limitations of the methodology to generate the DEM time series. These additions aim to serve as a reference for future research.
‘Since the DEM time series were obtained by interpolation, the accuracy of the DEM time series is highly dependent on the number and quantity of DEMs input during the stacking procedure. In addition, radar based TanDEM-X can penetrate snow, leading to an elevation bias on the snow-covered terrain in co-registration.’
Q4: While the data adequately supports the conclusions, presenting them in a more engaging and appealing way could better convey their significance and impact.
Response: Thank you for your useful suggestion. We have deleted unnecessary content from the conclusion section and given the authors' thoughts on future research as L378-L382 and L385-L387. We are sure these revisions can enhance the readability of the manuscript.
‘Since the DEM time series were obtained by interpolation, the accuracy of the DEM time series is highly dependent on the number and quantity of DEMs input during the stacking procedure. In addition, radar based TanDEM-X can penetrate snow, leading to an elevation bias on the snow-covered terrain in co-registration.’
‘By refining these methodologies and extending the temporal and spatial scope of the data, future research can provide even deeper insights into the complex interplay between climatic factors and glacier behavior. ’
Q5: The sections including data introduction, methods are not detailed enough.
Response: Thank you for your useful suggestion. We have added more describe on the significance of ASTER and ICESat-2 datasets as L110-L112 and L130-L133. Additionally, we have added the necessary formulas to describe gaussian process regression (GPR) to enhance the readability of the manuscript as L153-159.
‘ASTER remote sensing data covers wide areas with a revisit period of approximately 16 days, facilitating multiple captures of the same area to monitor land surface changes. Moreover, it is freely available.’
‘Studies have shown that the accuracy of ATL08 terrain height data can reach 0.70 m. Compared to ATL06, ATL08 product offers a broader coverage with nearly consistent accuracy in non-vegetated areas across both summer and winter.’
‘GPR takes a kernel σh(x, y, Δt)2 as input:
σh(x, y, Δt)2 = PL(x, y, Δt)+ESS(Φper, σ2per, Δt) + RBT(Δtloc, σ2loc, Δt) + RQ(Δtnl, σnl, Δt) * PL(x, y, Δt) + σh(t, x, y)2
Where PL is a pairwise linear kernel, representing the long-term elevation trend of the pixel, ESS a periodic exponential sine-squared kernel, representing the seasonality of the elevation changes, RBF a local radial basis function kernel, showing the elevation closeness with varying time differences, RQ a rational quadratic kernel multiplied by a linear kernel, to capture the long-time non-linear trends and the white noise representing the average measurement errors at time .’
Q6: About the validation dataset, it will be better to explain the reason why choosing L3A Land and Vegetation Height V008 (ATL08) product, why not choosing Land Ice Along-Track Height Product (ATL06) product?
Response: Thank you for your useful suggestion. We have add the reason in section 3.1.4 (L130-L133). ATL08 product is widely used for the accuracy verification of digital elevation model (DEM) due to its wider coverage. Using the same reference dataset facilitates the comparison of our results with other DEM datasets more favorably.
‘Studies have shown that the accuracy of ATL08 terrain height data can reach 0.70 m. Compared to ATL06, ATL08 product offers a broader coverage with nearly consistent accuracy in non-vegetated areas across both summer and winter.’
Q7: Please provide some more detailed information about ASTER DEMs, such as the numbers of DEMs from ASTER images.
Response: Thank you for your careful review. We have added the number of DEMs from ASTER images in section 3.2.1 (L165-L166). We hope this revision can improve the readability of the manuscript.
‘To eliminate the data gap in the results, 53 DEMs with RMSE less than 10 m in co-registration were stacked,’
Q8: About the data preprocessing (L208-L210), I think it should be in the methodology section.
Response: Thank you for your careful review. We have adjusted the data preprocessing to section 3.2.2 (L172-L174) to enhance the readability of the manuscript.
‘As the resolution of ICESat-2 and ASTER DEM is different, the ASTER DEM grid data were interpolated according to the time epoch of the ICESat-2. Then, the elevation differences in stable glacier-free regions between ASTER DEM and ICESat-2 were calculated.’
Q9: The DEM verification should be carried out over glacier-free areas. It should be explained in the manuscript.
Response: Thank you for your useful suggestion. We have re-validated the DEM accuracy in glacier-free areas and described in section 3.2.2 (L173-L174) of the text to clarify the above concerns.
‘As the resolution of ICESat-2 and ASTER DEM is different, the ASTER DEM grid data were interpolated according to the time epoch of the ICESat-2. Then, the elevation differences in stable glacier-free regions between ASTER DEM and ICESat-2 were calculated.’
We hope that the revised manuscript is accepted for publication in the Remote Sensing.
Best regards.
Mr. Huai Lin
School of Geodesy and Geomatics, Wuhan University
Author Response File: Author Response.pdf
