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Article
Peer-Review Record

Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer

Remote Sens. 2022, 14(24), 6249; https://doi.org/10.3390/rs14246249
by Thomas Johnson 1,*, Michel Tsamados 1, Jan-Peter Muller 2 and Julienne Stroeve 1
Reviewer 1:
Reviewer 2: Anonymous
Reviewer 3:
Remote Sens. 2022, 14(24), 6249; https://doi.org/10.3390/rs14246249
Submission received: 9 August 2022 / Revised: 15 November 2022 / Accepted: 16 November 2022 / Published: 9 December 2022
(This article belongs to the Special Issue Remote Sensing of Changing Arctic Sea Ice)

Round 1

Reviewer 1 Report

The manuscript presented is well written and well structured. Only minor revisions have to be done in order to improve the high quality of the work before going ahead in the pubblication phase.

Here it is some suggestions to improve the quality of the paper.

1 - I suggest you to add some parts in the introduction section talking about also the application on lakes. Please consider to include these works in your introduction section.

- https://doi.org/10.3390/cli9030047

- https://doi.org/10.3390/rs11232780

- https://doi.org/10.1109/36.563282 

- https://doi.org/10.1109/TGRS.2015.2429917 

2 - I suggest you to entitle the first section Introduction (at this time is absent) and consider to introduce a short section describing the study area

3 - In Methods please provide better indication about which software you adopted to perform the entire workflow data processing 

4 - In each map caption section please provide the reference system or projection, the datum, the nominal and represenation scale and the title. i suppose you performed it on R Studio please add this crucial information considering that Remote Sensing deal with applied geomatics.

 

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper examined a multi-angle camera approach to measure sea surface roughness, which is challenging and interesting work and can be considered potential publication after some modifications for regression modeling.

Figure 6. The color scheme can be changed. For example, zero can be colored in white, positive values can be colored in red, and negative values can be colored in blue. In the current figure, positive and negative values are difficult to distinguish.

Line 240. RFR (random forest regression) and GBR (gradient boosting regression) are also widely used methods for non-linear regression. You can compare the results from the two or three methods.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Dear authors,

Very interesting manuscript. You will find my comments in the attached file.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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