Improving Differential Interferometry Synthetic Aperture Radar Phase Unwrapping Accuracy with Global Navigation Satellite System Monitoring Data
Round 1
Reviewer 1 Report
Traditional Differential Interferometry Synthetic Aperture Radar monitoring methods and time-series InSAR techniques developed from DInSAR technology face limitations due to inconsistencies in mine monitoring. The reliability of the monitoring data is poor when the consistency is low. To solve these problems, this paper focused on research based on the MRF random field theory and GNSS 447 monitoring data-assisted InSAR phase unwrapping. But there are still some problems in this manuscript.
1. The readability of Figure 1a is poor, it is recommended to revise the content and legends in Figure 1.
2. The distribution map of monitoring points should be presented intuitively in Figure 2. It is recommended to add the compass and scale contents.
3. It is recommended that more information on the application of machine learning and simulated annealing algorithm in related fields should be added in the Introduction section.
Zhang W, Li H, Li Y, et al. Application of deep learning algorithms in geotechnical engineering: a short critical review[J]. Artificial Intelligence Review, 2021: 1-41.
KK Phoon & W Zhang (2022): Future of machine learning in geotechnics, Georisk, DOI: 10.1080/17499518.2022.2087884
4. The abscissa, ordinate, and legend in Figure 7 are all in Chinese, and similar issues still exist in Figure 1. It is recommended that the authors carefully check and modify them.
5. There are significant problems with the writing style of the Reference in this manuscript. It is recommended that the authors revise them according to the standard format of the Journal.
6. The writing format of Eq. (19) in Line 307 should be revised.
7. This manuscript introduces the principles of many methods, but the introduction of each method is simple. It is recommended to introduce the main methods used in this paper to ensure logical smoothness.
The english is good.
Author Response
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Author Response.pdf
Reviewer 2 Report
1. The authors' choice of data (ALOS2) has the benefits of high resolution, extensive coverage, and a quick revisit cycle. The model employed here is a step towards monitoring surface deformation and geological risks outside of extensive surface deformation investigations, particularly in coal mining regions where constant monitoring of subsidence is a major issue. The main accomplishment of this study is the model's ability to extract deformation over areas of relatively very low coherence (with relatively low error, in cm).
2. The authors selected data in accordance with the area's known mining period (December 2017 to August 2019). Although the deformation above the working face was obvious, the pattern of the deformation could not be recovered using conventional techniques because of the low coherence.
3. Authors have amply illustrated the method's application in the mining region, where it can be used to analyze significant deformation features.
4. The unwrapping technique above the working surface has issues with poor coherence and missing data in the LOS direction. For sites with poor unwrapping accuracy or when unwrapping is not possible, the proposed methodology performs well.
5. The findings of fitting the GNSS LOS-direction deformation to the estimated unwrapped deformation were close. Using the threshold method and a coherence range of 0.20–1.00 over a one-year period, the research area was recalculated. When comparing the calculated unwrapped deformation with the GNSS LOS direction deformation fitting data, the median error was found to be 30.3 mm.
6. However, in the conclusion, the limitations of the method of need to be mentioned clearly. The conclusion highlights that the method was able to retrieve the deformation over areas with very low coherence (as the two images were a year apart). Thus, the estimated error (absolute and relative) may also be mentioned in the conclusions.
7. A 30m SRTM DEM was used in the investigation. The usage of reasonably high-resolution (5 or 10m) public domain DEMs and the related modeling deformation errors may be discussed by the authors.
Author Response
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Author Response File: Author Response.pdf
Reviewer 3 Report
1. The title can be further modified.
2. The author's affiliation is missing the school information.
3. In the abstract, the phrase "a relative error of 2.5% compared to GNSS fitting results" mentions a 2.5% error without specific reference in the article. Could you please provide the source of this information?
4. In the last paragraph of the first section of the article, "The use of GNSS monitoring results to assist the InSAR phase deconvolution data processing can effectively enhance the deconvolution effect."
(1) The use of -> The method of
(2) "enhance the deconvolution effect" can be changed to "resolve the decorrelation issue."
5. Reference [19] has an incorrect citation order.
6. In section 3.1 of the article, the geographic coordinates for the mine range from 106° 49′ 51″ to 106° 53′ 05″ E, and from 37° 34′ 41″ to 37° 41′ 50″ N. The administrative division falls under Yanchi County, Wuzhong City.
(1) Change the beginning "This section" to "The study area, Jinfeng Coal Mine."
(2) Change "coordinates" to "coordinate"; "for the mine are" to "of the mine is."
(3) The sentence "the administrative division is under the jurisdiction of Yanchi County, Wuzhong City" is redundant with previous information.
7. In section 3.1 of the article, separate the figure caption from the figure title in Figure 1.
8. In section 4.3 of the article, consider removing the sentence "The published SRTM 30 m and 90 m DEMs."
9. In section 6.1 of the article, below Equation (24), there is inconsistency in the font usage.
10. Abstract: It is not a good way to separate the abstract into three parts. One single paragraph is ok.
Author Response
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Author Response File: Author Response.pdf
Reviewer 4 Report
The authors investigated the MRF random field theory and GNSS monitoring data-assisted InSAR phase unwrapping. Algorithm reliability was confirmed by combining simulated phases with digital elevation model (DEM) data for deconvolution calculations, showing good agreement with real phase-value results (medium error: 4.8E-04). Applied to ALOS-2 data in the Jinfeng mining area, the algorithm transformed interferometric phase into deformation, obtaining simulated deformation by fitting GNSS monitoring data.
This manuscript is useful to improving DInSAR phase unwrapping accuracy.
1 Does the formula in the article come from other literature? If so, please provide references.
2 Text of some Figures are in Chinese. E.g., Figure 1a, Figure 7.
3 In Figure 8, c and d are almost same.
4 There are fewer data points this time, so it is recommended to increase the data to further verify the correctness of the model and algorithm.
5 The name of the points in Figure 2 is not clear.
Can be improved.
Author Response
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Author Response File: Author Response.pdf
