ICESat-2 Performance for Terrain and Canopy Height Retrieval in Complex Mountainous Environments
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe authors present a comprehensive evaluation of the performance of ICESat-2 ATL03 V6 data in retrieving terrain elevation and forest canopy height in a highly complex topographic and vegetation environment in Yunnan province.
The study is highly significant in scientific progress as it fills gaps in validation in Asian mountainous regions and helps promote forest carbon monitoring.
The article is relevant to the field of remote sensing and of interest to readers of the journal. 1.
Bibliographic coverage is good. Language and style are adequate.
From a scientific point of view, the study is solid and methodologically consistent with what is present in today's panorama of scientific research in the sector. Strengths include the transparent experimental design, the detailed analysis of environmental and observational variables as well as the use of statistical and machine learning techniques.
In addition, the adaptive implementation of BDT-ADBSCAN for photon noise reduction and the structured evaluation of beam intensity and acquisition time (day/night) are innovative and impressive.
Below are a few small touches that could help to further improve this interesting manuscript:
- Provide the complete parameter configuration or link to the code repository for the BDT-ADBSCAN algorithm
- Improve the quality of the figures: 4 - 5 - 8
- Standardise the references section to mdpi standards
- Sometimes RMSE/MAE values are hidden in a long narrative text: it is advisable to summarise them in a table for easy cross-referencing.
- Consider simplifying the title.
- Discuss practical implications and future missions.
Author Response
Dear Reviewer,
We would like to express our sincere gratitude for your thorough review and insightful comments on our manuscript, "ICESat-2 Performance for Terrain and Canopy Height Retrieval in Complex Mountainous Environments" (ID: remotesensing-3664957). Your feedback has been invaluable in helping us enhance the quality and clarity of our paper.
We have carefully considered each of your suggestions and have revised the manuscript accordingly. A detailed, point-by-point response letter addressing all comments has been prepared and submitted along with the revised manuscript via the official system.
We believe these changes have significantly improved our work and hope that they meet your expectations.
Thank you once again for your time, expertise, and constructive feedback.
Sincerely,
The Authors of Manuscript remotesensing-3664957
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsDear Authors
The article is well written. It needs minor modifactions as suggested in the pdf file.
Comments for author File: Comments.pdf
Author Response
Dear Reviewer,
We would like to express our sincere gratitude for your thorough review and insightful comments on our manuscript, "ICESat-2 Performance for Terrain and Canopy Height Retrieval in Complex Mountainous Environments" (ID: remotesensing-3664957). Your feedback has been invaluable in helping us enhance the quality and clarity of our paper.
We have carefully considered each of your suggestions and have revised the manuscript accordingly. A detailed, point-by-point response letter addressing all comments has been prepared and submitted along with the revised manuscript via the official system.
We believe these changes have significantly improved our work and hope that they meet your expectations.
Thank you once again for your time, expertise, and constructive feedback.
Sincerely,
The Authors of Manuscript remotesensing-3664957
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsDear Authors,
A good paper but a couple of minor points and one slightly larger.
minor point 1 - in table 1 you have the "instrument name" as "532 nm (green light)"
minor point 2 - figure 5 (where you state that the r2 is 1) isn't very helpful, what would be more interesting would be the average or median difference against elevation. Figure 4 clearly shows that differences of 10 meters or more are possible, these differences are not visible on figure 5
more major point - you supply the equations for r-squared and RMSE - which are things you can find in a school text book, but you give no clear indication of how your novel algorithm works ... there is an R package dbscan which implements the basic density based clustering, but if you use it you don't mention it. I need to know how your Bayesian "twist" improves the process. As you are claiming it is an improvement then you should present some results showing how it is better. At the moment I have no idea how you method works, I presume in some sense you are using it to delete outliers? but?
Author Response
Dear Reviewer,
We would like to express our sincere gratitude for your thorough review and insightful comments on our manuscript, "ICESat-2 Performance for Terrain and Canopy Height Retrieval in Complex Mountainous Environments" (ID: remotesensing-3664957). Your feedback has been invaluable in helping us enhance the quality and clarity of our paper.
We have carefully considered each of your suggestions and have revised the manuscript accordingly. A detailed, point-by-point response letter addressing all comments has been prepared and submitted along with the revised manuscript via the official system.
We believe these changes have significantly improved our work and hope that they meet your expectations.
Thank you once again for your time, expertise, and constructive feedback.
Sincerely,
The Authors of Manuscript remotesensing-3664957
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
Comments and Suggestions for AuthorsDear Authors,
I would still have liked to have seen something on how you use Bayes with the clustering routine, but, I am recommending this get published anyway.
