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Technical Note
Peer-Review Record

Photometric Correction of Chang’E-1 Interference Imaging Spectrometer’s (IIM) Limited Observing Geometries Data with Hapke Model

Remote Sens. 2020, 12(22), 3676; https://doi.org/10.3390/rs12223676
by Xuesen Xu 1,2,3, Jianjun Liu 2,*, Dawei Liu 2, Bin Liu 2 and Rong Shu 1,3
Reviewer 1:
Reviewer 2: Anonymous
Remote Sens. 2020, 12(22), 3676; https://doi.org/10.3390/rs12223676
Submission received: 2 September 2020 / Revised: 4 November 2020 / Accepted: 4 November 2020 / Published: 10 November 2020
(This article belongs to the Special Issue Planetary 3D Mapping, Remote Sensing and Machine Learning)

Round 1

Reviewer 1 Report

Photometric Correction of Chang’E-1 Interference Imaging Spectrometer’s (IIM) Limited Observing Geometries data with Hapke model

 

Xu et al.

 

Review

 

The paper describes an approach to derive Hapke parameters for a photometric model for the correction of CE-1 IIM images, and demonstrates a significant improvement when applied to an image showing opposition surge. This is an essential task in the reduction of the instrument data.

 

There are a few minor points listed below where the clarity of the work could be improved. With these addressed, I would recommend the paper for publication.

 

Greg Michael

 

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Title/Ln15 Why is “Interference Imaging Spectrometer” abbreviated as ‘IIM’? Please clarify in text.

 

Ln16 I’m not familiar with the term ‘new highland’ – please clarify your distinction between ‘new’ and ‘old’ highland here. (and for Fig. 1)

 

Ln118-120. A few sentences describing the albedo filtering procedure would be helpful. Fig. 2a looks like two peaks, but realistically we could expect there to be more than two values at the surface: how would this affect your result?

 

Fig. 3 is very difficult to examine because of the double colour scheme: I would suggest you replace the region outlines with white/grey/black for this case.

 

Fig 4. Similar problem here: I can’t, for example, identify any yellow ’70-130’ region distinct from the yellow boundaries. I suggest using a colour scale for the area and black/grey/white for the boundaries. Also mention for which value you have derived the fluctuation in the caption (with units for the legend).

 

Fig. 8. The improvement within the red box is impressive. I suggest to give the actual i,e,g values for the two observations in the caption. It is notable that while 2576 is significantly improved, some detail has been lost in 2885. This is possibly a matter of the stretch you have chosen to show, but I would suggest to optimise the stretch for both if you can.

 

Ln238. “Hapke model can effectively” ->  “Hapke model can more effectively”

 

Ln260 “McEwen”

Author Response

Dear Michael:

Thank you so much for reviewing my manuscript and I have revised as your advices. Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

Authors study a very specific problem, and use a known model for corrections. The following two items (either or both) may improve (1) Comparison with other models (2) Modify the Hapke model for this specific application to improve corrections

Author Response

Thank you very much for reviewing my manuscript, I have revised and do some explaination in a word file. Plesase see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

If authors can share the SW used (MATLAB codes or Python codes), the impact of the paper may also bigger. It is a suggestion. GitHub of similar sites can be used to share scientific research SW.

 

Authors may also share original data (if appropriate) for future research and benchmarking purposes.

 

Author Response

I am happy to share my code and sample data with anyone who are interested in this work.
I have uploaded a Zip file with IDL code in this work to the academic Editor and I will upload them to the Github soon.
The sample data was too big to upload in the attachfile. Anyone can  download the IIM data at the website  http://moon.bao.ac.cn/.

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