Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps

Round 1

Reviewer 1 Report

I think the authors carried out a significant amount of work for mapping retrogressive thaw slumps using the DL application. I recommend the manuscript for publication after the following minor changes:

• The introduction starts are very clear and lead correctly to a certain problem statement. A recent study highlighted the importance of the band combinations in the use of multispectral datasets on DL model prediction accuracy for remote sensing applications (Park et al.2020; Bhuiyan, et al.2020;  Abdalla, Alwaseela, et al.2019). The authors should explain this aspect in the introduction section.

Bhuiyan et al. 2020 “Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery. J. Imaging 2020, 6, 97.”

Abdalla, Alwaseela, et al. "Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm." Remote Sensing 11.24 (2019): 3001.

Park, Ji Hyun, et al. "RGB Image Prioritization Using Convolutional Neural Network on a Microprocessor for Nanosatellites." Remote Sensing 12.23 (2020): 3941.

• You can add a paragraph explaining high-resolution remote sensing applications?
• Can you explain the high-resolution features and high-level semantic information with examples in the methodology section?

• Data fusion, the process of combining multispectral (MS) and high-resolution panchromatic (PAN) images with complementary characteristics often serve as an integral component of remote sensing mapping workflows. You should explain these issues? (See: Witharana et al. 2020)You will get some insight and you can discuss it)?

Witharana, Chandi, et al. "Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high-resolution commercial satellite imagery for automated ice-wedge polygon detection." ISPRS Journal of Photogrammetry and Remote Sensing 170 (2020): 174-191.

Author Response

Dear reviewer, thank you for taking your time to carefully review our manuscript. Please find our detailed comments in the attached pdf file.

Author Response File: Author Response.pdf

Reviewer 2 Report

Attached.

Comments for author File: Comments.pdf

Author Response

Dear reviewer, thank you for taking your time to carefully review our manuscript. Please find our detailed comments in the attached pdf file.

Author Response File: Author Response.pdf