Next Article in Journal
Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional Neural Network) and by Post-classification Comparison (PCC)
Next Article in Special Issue
Improving Ship Detection in Clutter-Edge and Multi-Target Scenarios for High-Frequency Radar
Previous Article in Journal
Lithospheric Equilibrium, Environmental Changes, and Potential Induced-Earthquake Risk around the Newly Impounded Baihetan Reservoir, China
Previous Article in Special Issue
HF Radars for Wave Energy Resource Assessment Offshore NW Spain
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 13
Issue 19
10.3390/rs13193896
Review Report
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Article
Peer-Review Record

Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements

Remote Sens. 2021, 13(19), 3896; https://doi.org/10.3390/rs13193896
by Max C. Bradbury * and Daniel C. Conley
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Remote Sens. 2021, 13(19), 3896; https://doi.org/10.3390/rs13193896
Submission received: 18 August 2021 / Revised: 17 September 2021 / Accepted: 22 September 2021 / Published: 29 September 2021
(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

Round 1

Reviewer 1 Report

This work provides a detailed presentation of high‐frequency radar processing scheme for the retrieval of surface currents using artificial neural networks. I think this is an interesting paper worth publishing with some modifications. Generally，it is well written, but please find below some remarks and suggestions which I think should be taken into account before the paper is suitable for publication.

（1） The introduction should be rewrite for logical formation, especially for the end of introduction.

（2）The references are relatively few in the last three years.

（3）Line 113, what is the mean of WECs, please give the full name in the paper.

Line 317, “this result in…”, what does “this” mean here?

（4）Conclusion should be revised to concentrate on the mainly contribute on this topic.

Author Response

The attached file represents our response to both reviewers.

Author Response File: Author Response.docx

Reviewer 2 Report

I have read carefully the paper and overall I believe it is an very interesting work, well written, worth publishing also with useful application on renewable energy issues. The paper focuses on the use of Artificial Neural Networks to predict current velocities, using data from high‐frequency radar. The results of the authors are highly correlated with measured values. In my view the manuscript is well written and organized, the results are supported by the data, the illustrations are well presented.

My main comments are the following:

  • In my view, the abstract should note how this method would contribute to renewable energy issues.
  • The introduction needs to address the aims of the paper.
  • The methods section is rather long, I would propose to organize into sub-chapters

Overall, I my suggestion is that the paper should be accepted with minor revisions

You may find in attachment the annotated pdf with some minor corrections for the authors.

Comments for author File: Comments.pdf

Author Response

Our response to both reviewers is uploaded here.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The manuscript can be published in this version.

Back to TopTop