Land Subsidence Monitoring Method in Regions of Variable Radar Reflection Characteristics by Integrating PS-InSAR and SBAS-InSAR Techniques

Round 1

Reviewer 1 Report

The authors seems to have addressed the comments form the previous round of reviews and thus this revised manuscript may be accepted for publication without further changes.

Author Response

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Reviewer 2 Report

The need to combine PS-InSAR with SB-InSAR data is nicely justified but I am not convinced whether this would be truly novel.  The authors should discuss more broadly previous attempts to combine those two datasets in the past. Besides, many figures' quality is insufficient for publication (missing scale bars and north arrows on the maps, sometimes even coordinates; the fonts are too small). Pour English makes it hard to evaluate some aspects of the paper. See some examples in the attached pdf. 

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Round 2

Reviewer 2 Report

The authors have done a great job to improve the manuscript. I only have one moderate and two minor comments remaining (see the attached pdf)

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This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Land subsidence monitoring method in regions of variable radar reflection characteristics by integrating PS-InSAR and SBAS-InSAR techniques

 

This study proposes a strategy for combining PS-InSAR and SBAS-InSAR mean LOS velocity results, in order to get the advantages of both methods, accuracy for PS, spatial coverage for SBAS. They illustrate their approach by processing Sentinel-1 data on the area of Suzhou city which suffers from subsidence due to excessive water withdrawal.

First, the main issue of this paper should be set more clearly. The main objective is to propose a way to combine PS and SBAS results. The method that is proposed is not specific to any type of ground deformation. Hence, the subsidence phenomenon which is put forward (for example in the title) is simply what happens on the studied area, but has nothing to do with the fusion method that is described.

Regarding the PS-SBAS integration approach, I have not been convinced at all. If I understood correctly, this method keeps PS measurements only in dense urbanized areas (based on PS “coherence” higher than some threshold, and a clustering analysis) and uses SBAS measurements elsewhere. This is reasonable even though very crude. Throughout the manuscript it’s unclear whether the authors consider isolated PS as reliable or not. I think most of them are reliable. So, their approach tend to remove reliable PS measurements away from PS clusters, at the benefit of the SBAS results that are likely much less precise. A really efficient way of combining PS and SBAS results should not do that. The PS selection is based on the amplitude dispersion index which is not necessarily related to the phase stability. Moreover, nothing is said about the uncertainties associated with the mean LOS velocity estimates. Fig. 8d is interesting since it points out discordance between PS and SBAS mean LOS velocity, but no argument is put forward in order to explain this difference, and to decide whether PS is better than SBAS or the reverse. Implicitly, authors decide that SBAS is better since PS are kept only in densely urbanized areas, but I am not sure they are right. This approach is applied to the Suzhou subsidence case, but no analysis is provided that explain the importance of their combination in the final interpretation. Finally, this approach should be presented within a detailed state-of-the-art in PS-SBAS combination, which is totally absent.

If authors believe in the relevance of their combination, then I suggest their rewrite their manuscript following my previous comments. Otherwise, I suggest they focus on the analysis of the subsidence over the study area, and use their combination strategy if ever it helps.

I list hereafter my comments.

I hope it helps.

 

Abstract:

Acronyms (PSC, SDFP, PS-InSAR, SBAS-InSAR, DBSCAN) are used but not defined.

SDFP also cover urbanized areas.

Why would this “data fusion” approach be limited to subsidence phenomena?

The originality and relevance of the proposed combination must appear in the abstract

1. Introduction:

Most bibliographic references must be preceded with e.g. since they are not exhaustive.

The manuscript focuses immediately on subsidence while this is of very secondary interest relatively to the main issue of the paper.

What is expected here is the expression of the main issue (PS-SBAS combination) within a brief, but complete state-of-the-art. Instead of this, one can only find a vague description of PS and SBAS techniques.

2. Methodology:

I suggest removing all the useless information on PS and SBAS processing, just to focus on what is important for the rest of the study: (1) how the confidence in PS and SBAS mean velocities is precisely evaluated, and (2) the strategy of combination with its justification.

The clustering method is described before the reader discovers what the real objective of its application is.

I don’t think equations from 6 to 16 are all necessary.

3. SAR datasets of the study area:

Are they in ascending or descending orbits (or both)?

4. Comparison of single interferometry:

Does the ADI value reflect the reliability of the final mean LOS velocity? I think it does only partly.

The uncertainty on mean LOS velocities should be displayed.

How is the null-velocity reference set?

Isn’t there any external measurement (GNSS…) to compare InSAR results with?

What is the multilook factor that has been used for the SBAS processing?

Did the authors expect PS in water areas?

Subplot (d) in Fig. 8: Why is there such a difference? Is there some unwrapping artefact in one of the 2 techniques? Which one is correct? If the SBAS solution is correct, then it pleads for the strict separation proposed by the authors, otherwise it proves that PS measurements must be kept with high priority even when spatially isolated.

5. Data fusion:

The method sounds correct for determining reliable PS cluster in dense urbanized areas.

Eps and MinPts should have been defined much before.

Fig. 13: What does ‘density’ in Fig.13 means? What is the unit of distance? If it is km, why the peak is said to be found at 4286 m (P15 L332)?

What happens in case of multi-cluster zone?

What is the sensitivity of the final clustering analysis relatively to these parameters?

Chapter 5.3 would be easier to read if the objective of the authors is better expressed before.

P16 L 360: Are the authors really sure that PS in suburban areas have low quality and must be removed? I do not share this opinion. But, maybe I am wrong….

Considering the analysis of subsidence (since this is the issue on this area), what improvement does this PS-SBAS combination bring?

6. Discussion:

This is not a discussion.

7. Conclusion:

The reader should find here the originality and the main relevance of the data fusion that is proposed in the paper.

 

 

Reviewer 2 Report

The authors propose a ground subsidence monitoring method by PS and SBAS -InSAR and show using experiments the benefits of their proposed solution. While the manuscript write-up in terms of the overall flow and presentation style is able to convey a general idea of the inner workings of their proposed solution, there are certain details that need more attention before this manuscript may be considered ready for publication:

1. It appears there exists some published research [1] in established journals that deal with similar types of methods as the authors ( combining PS-InSAR and SBAS-InSAR for ground subsidence monitoring ). Thus, it is essential that the authors revisit their literature survey and make it more thorough and carefully look at even related methods that may be not be identical to theirs, but have very similar objectives. In short, the literature review needs to be more comprehensive than it currently is.
2. In continuation to the above point, it may be noted that the authors do not show any comparison of their results with those of any other similar methods. Does this imply that there are no other methods to compare with ? It seems this goes back to the first point about the comprehensiveness of literature survey.
3. The authors mention the issues of noise / coherence in several places in the manuscript. However, it some (all ?) of those places, they do not cite the filtering method(s) used. Few examples of this are lines 222-223 and 230-232.
4. In continuation to the above point, it would increase the impact of the manuscript if the authors add a discussion on the potential of using machine / deep learning based methods like [2] that have been shown to be able to effectively produce filtered InSAR phase and coherence in the wrapped InSAR signal domain ( without unwrapping the signal ).

References

[1] Armaş, I., Mendes, D., Popa, RG. et al. Long-term ground deformation patterns of Bucharest using multi-temporal InSAR and multivariate dynamic analyses: a possible transpressional system?. Sci Rep 7, 43762 (2017). https://doi.org/10.1038/srep43762

[2] S. Mukherjee, A. Zimmer, N. K. Kottayil, X. Sun, P. Ghuman and I. Cheng, "CNN-Based InSAR Denoising and Coherence Metric," 2018 IEEE SENSORS, 2018, pp. 1-4, doi: 10.1109/ICSENS.2018.8589920