A Geometry-Compensated Sensitivity Study of Polarimetric Bistatic Scattering for Rough Surface Observation

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The ability to obtain information about soil moisture content using SAR has great scientific and practical significance. To do this, it is necessary to be able to estimate with high accuracy the polarimetric parameters of the received echo, which can be achieved using polarimetric SAR (PolSAR), which has many combinations of linear polarization channels available. If you use the bistatic PolSAR system, you can obtain additional information about the radar scattering of the surface due to the spatial separation of the transmitting and receiving parts. The use of bistatic PolSAR has attracted growing interest in recent years, so the topic of this work is certainly relevant today.
In the paper, the authors explore the fundamental question of how different polarimetric variables respond to soil moisture and surface roughness with the goal of finding the optimal non-specular bistatic PolSAR geometry for obtaining information about soil moisture content. In particular, a possible choice of a set of polarimetric variables that works well in bistatic geometries away from the specular direction was explored.
To solve the problem, the authors use a two-scale rough surface scattering model (TSM), where the model assumes that the surface roughness has two different variation regimes, and the increase in surface roughness occurs due to the random orientation of the surface facets. By rotating the polarization orientation, it was possible to reduce the difference caused by geometry and obtain polarization channels with geometric compensation, and the evaluation of polarimetric variables was carried out independently of the polarization bases. The simulation was carried out for a horizontal rough surface under a beam with a fixed incidence angle at various receiving directions. As a result of the simulation, an estimate of the sensitivity of the polarimetric variables to changes in soil moisture was obtained, which, however, showed that the forward geometry near the specular direction is still more reliable.
Although the obtained result is in agreement with the results of previous studies, it did not show the advantage of using bistatic polarimetric SAR in determining soil moisture, I believe that the research performed can be useful from the point of view of the presented modeling methodology, as well as for planning bistatic SAR measurements for the purpose of collecting polarimetric data. The research performed is important, but requires additional modeling, for example with a variation of beam incidence angle.
The authors describe in great detail and clearly the methods used, as well as the results obtained during data processing. The figures and tables presented in the article are qualitative and fully reflect the results obtained. I believe that the article can be published in its present form, having eliminated only the typo on lines 27-35.

Author Response

Thank you for reviewing and commenting on our paper. We are especially grateful to you for bearing with our horrible mistake of accidentally leaving a paragraph of the Remote Sensing template in place. The corresponding author slipped in the formatting and failed to spot this embarrassing error - for this, we would like to take this opportunity to extend our sincere apologies. 

We fully agree with your summary and your suggestion on what needs to be done for the next step in the future work. 

Reviewer 2 Report

Comments and Suggestions for Authors

The paper addresses an important and timely issue in remote sensing, specifically the sensitivity of polarimetric variables to soil moisture and surface roughness in bistatic radar scattering configurations. This is a valuable contribution given the increasing interest in using bistatic polarimetric SAR for environmental and agricultural applications. A major revision is suggested

 

I have some comments:

1.        The clarity of the pictures throughout this article is seriously lacking and must be improved.

2.        While the article provides a detailed introduction to the Two-Scale Rough Surface Scattering Model (TSM) and its sensitivity assessment to soil moisture and surface roughness, it lacks a sufficient description of the model's applicability, potential limitations, and its accuracy under different environmental conditions. Further discussion on these aspects would enhance the reader's understanding of the model's practical value and potential issues that may arise in its application.

3.        While the paper is strong in theoretical and simulation aspects, it lacks experimental validation of the proposed model with real-world data. Including such validation could significantly strengthen the paper's conclusions and its applicability to practical remote sensing operations.

4.        Although the paper mentions the use of simulated data for sensitivity analysis, there is relatively little detail about the choices of these datasets, parameter settings, and how they represent real-world scenarios. Providing more background information, such as why specific parameter values were chosen and how these values correspond to real-world observations, would help enhance the transparency and credibility of the research.

5.        The article provides a relatively brief comparison between the TSM model and other models, such as the AIEM. A more in-depth analysis of the strengths and weaknesses of these models in handling specific types of surfaces (such as different levels of roughness, different types of soil, etc.) and their performance in practical applications (such as accuracy, computational efficiency, etc.) would help to highlight the unique value and potential applications of the TSM model.

6.        Every model and simulation study is subject to various factors that may introduce uncertainties and errors. The article lacks a thorough discussion of these potential sources of uncertainty and error, including the validity of model assumptions, the impact of parameter variability on the results, and more. A more comprehensive discussion of these factors and their potential impact on the study's conclusions would help assess the reliability and generalizability of the research findings.

7.        Ensure consistent use of terms throughout the paper. For example, "soil moisture content (SMC)" is a term that should be consistently used after its first mention to avoid confusion.

8.        Some sentences could be revised for grammatical correctness and clarity. For example, "In the simulation, we assume an incidence angle of 30°, which corresponds to a typical illumination geometry from a space borne SAR, and we assume the scattered return from a horizontal rough surface is received at various beam directions of 𝜃 and 𝜙." could be rephrased for clarity and conciseness.

9.     Given the complexity and challenges inherent in polarimetric bistatic SAR observations, especially in relation to surface roughness and moisture content analysis, integrating advanced signal processing techniques could offer substantial benefits. Specifically, the application of blind source separation technology has shown promising results in addressing similar challenges within the realm of spaceborne SAR. Articles that can be referenced are as follows:

Ø  Chang, S., Deng, Y., Zhang, Y., Zhao, Q., Wang, R., & Zhang, K. (2022). An advanced scheme for range ambiguity suppression of spaceborne SAR based on blind source separation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-12.

Ø  Zhang, W., Tait, A., Huang, C., Ferreira de Lima, T., Bilodeau, S., Blow, E. C., ... & Prucnal, P. (2023). Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation. Nature Communications, 14(1), 1107.

Comments on the Quality of English Language

It is recommended that the author check the English usage throughout the article

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

I found that the Introduction of the article is written using AI. As such, the article cannot be trusted, in my opinion. Unfortunately, I have to reject this article, which tries to cover a very interesting and important subject.

 

Author Response

We sincerely apologize for accidentally leaving a paragraph of the Remote Sensing template in place. We realized this horrible mistake only after we were perplexed to notice this AI-generation concern.

We reformatted our finalized manuscript into the Remote Sensing template as the last step. The corresponding author slipped in the formatting and failed to spot this embarrassing error. Unfortunately, it is the leading paragraph and we can feel for the reviewers being equally bewildered as we do.

We still hope you would be able to read our revised paper. The formatting mistake does not reflect the quality of our work. We look forward to hearing your valuable opinion and comments on this subject.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for submitting your revised manuscript to our journal. I have reviewed the changes made in response to the earlier set of comments. Unfortunately, I must express my dissatisfaction with the revisions, as they appear insufficient to address the critical concerns raised during the initial review process. 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The manuscript requires thorough proofreading to correct grammatical errors and awkward phrasings that detract from the overall readability. Examples of such issues include improper verb tenses, misuse of scientific terminology, and convoluted sentence structures. Enhancing these aspects will not only improve readability but also ensure that the scientific content is communicated effectively.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

 

Thank you very much for addressing the issue detected earlier!

After reviewing the updated manuscript, I came up with the following comments:

1.      Please check equations 4 and 5. I see that in the equation 5 the expression under square root or vinculum in the last brackets of lower part of the equation is shorter than in in the corresponding part of the equation 4. Providing that everything else on the lower parts of equations belong to both polarizations are equal, this issue worth checking.

2.      In Section 4, all the subsections start with number 3. They should start from 4.

3.      How were all the results obtained? I understand that it was a some kind of simulation rather than a practical experiment with a real bistatic radar system. Please provide all the details regarding the simulations such as type of the software, applied parameters, assumptions, working wavelengths, etc. Adding of a good paragraph discussing the issue would be useful here.

4.      Please describe your soil moisture and roughness datasets as well as all other data that was used in the study.

5.      The paper does not discuss the relationships between roughness and wavelengths.  What is rough for X-band is not rough for L-band according to Rayleigh or Peake-Oliver surface roughness criterions. This is important for practical SAR and earth observation applications so is advisable to be discussed. The example is that a typical field after tillage is rough for X-band and most probably C-band but not for L or P.

 

I believe that article is potentially has a great interest for the readers and advances knowledge in radar theory. The article, I believe, might be published after all the comments are addressed.

Good luck!

 

Kind Regards,

 

Your Reviewer

Author Response

Please see the attachment.

Author Response File: Author Response.pdf