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Article
Peer-Review Record

High Wind Speed Inversion Model of CYGNSS Sea Surface Data Based on Machine Learning

Remote Sens. 2021, 13(16), 3324; https://doi.org/10.3390/rs13163324
by Yun Zhang 1, Jiwei Yin 1, Shuhu Yang 1,*, Wanting Meng 2, Yanling Han 1 and Ziyu Yan 1
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Remote Sens. 2021, 13(16), 3324; https://doi.org/10.3390/rs13163324
Submission received: 29 June 2021 / Revised: 13 August 2021 / Accepted: 19 August 2021 / Published: 23 August 2021

Round 1

Reviewer 1 Report

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Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

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Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

see attached.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

Introduction.
You list many of works here and this is useful. But I would maybe first add paragraph giving the general idea of the different approaches that exists. You do so in line 39, but this is only true for some of the references.

Lines 39-94. I believe the paragraph is too long. I would split it into one paragraph for each reference or method (for example: [4] [5] and [7]; [11]; [12] [13] and [14]).
Line 42. Clarizia
Line 43. Delay-Doppler Map
Lines 95-150. I believe the paragraph is too long. . I would split it into one paragraph for each reference or method 
Line 319. "distance"
Line 322. Quality Flag (QC), Signal-to-Noise Ratio (SNR), GNSS-R satellite position in ECEF
Line 324 BRCS's DDM
Line 325. Doppler


 

Author Response

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Author Response File: Author Response.docx

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