The time series analysis using Interferometric Synthetic Aperture Radar (InSAR) has demonstrated its capability in monitoring temporal subsidence changes. The most common methods for interferometric monitoring of land surface changes are the SBAS and PSI methods. Recently, it has been stated that multi-looking pixels and using short-term interferograms in the SBAS algorithm introduce unwanted phases in displacement measurements. The main objective of this study is to investigate different interferometric networks in the SBAS algorithm to detect and mitigate the interfering phase. Time series analyses were performed using 62 Sentinel-1 images from 2020 to 2021. To examine this effect, a reference displacement map without bias was required. For this purpose, two displacement maps were generated using two SBAS algorithms (full connectivity) and PSI. The obtained map using the PSI algorithm, considering its nature derived from multi-looking pixels in the SBAS algorithm, is bias-free. After comparing the two generated maps, a correlation value of 96.3% indicated that the fully connected network in the SBAS algorithm is also bias-free and was considered as a reference in this research. To investigate the impact of this value on displacement estimation, the number of connections between images and the length of interferograms used in different interferometric networks were examined. The results showed that by increasing the connections from 1 to 8 between images and the length of interferograms in the interferometric network, the correlation value between the generated displacement maps increased from 66.4% to 81.3%, and the phase bias decreased. Additionally, considering the different phase bias values in various land covers, three land covers, including urban, agricultural, and bare soil, were investigated in this study. The difference between the phase bias values in the connections from 1 to 8 between images in the agricultural, urban, and soil areas revealed values of 1.228, -0.25, and -0.672, respectively. The highest difference was related to the agricultural area, and the lowest value was related to the urban area. Ultimately, the phase bias has the greatest impact on the agricultural cover and the least on the urban cover.
Type of Study: Research |
Subject: Photo&RS Received: 2023/07/19
References
1. Gabriel, Andrew K., Richard M. Goldstein, and Howard A. Zebker. "Mapping small elevation changes over large areas: Differential radar interferometry." Journal of Geophysical Research: Solid Earth 94.B7 (1989): 9183-9191. [DOI:10.1029/JB094iB07p09183]
2. Wright, T. J., Parsons, B. E. and Lu, Z., 2004, Toward mapping surface deformation in three dimensions using InSAR, Geophys. Res. Lett., 31,L01607, doi:10.1029/2003GL018827. [DOI:10.1029/2003GL018827]
3. Devanthéry, N., Crosetto, M., Monserrat, O., Cuevas-González, M., & Crippa, B. (2018). Deformation monitoring using Sentinel-1 SAR data. Paper presented at the Multidisciplinary Digital Publishing Institute Proceedings [DOI:10.3390/ecrs-2-05157]
4. Terzaghi, K. (1925). Principles of soil mechanics, IV-Settlement and consolidation of clay. Engineering News-Record, 95(3), 874-878.
5. Zisk, S. H. "A new, earth-based radar technique for the measurement of lunar topography." The moon 4.3-4 (1972): 296-306. [DOI:10.1007/BF00561997]
6. Bamler, R., Principles of synthetic aperture radar. Surveys in Geophysics, 2000. 21(2-3): p. 147-157. [DOI:10.1023/A:1006790026612]
7. Ferretti, A., C. Prati, and F.L. Rocca. Permanent scatterers in SAR interferometry. in Remote Sensing. 1999. International Society for Optics and Photonics.
8. Massonnet, D. and K.L. Feigl, Radar interferometry and its application to changes in the Earth's surface. REVIEWS OF GEOPHYSICS-RICHMOND VIRGINIA THEN WASHINGTON-, 1998. 36: p. 441-500. [DOI:10.1029/97RG03139]
9. Ferretti, A., C. Prati, and F.L. Rocca. Permanent scatterers in SAR interferometry. in Remote Sensing. 1999. International Society for Optics and Photonics.
10. Tapete, D., Fanti, R., Cecchi, R., Petrangeli, P., & Casagli, N. (2012). Satellite radar interferometry for monitoring and early-stage warning of structural instability in archaeological sites. Journal of Geophysics and Engineering, 9(4), S10-S25. [DOI:10.1088/1742-2132/9/4/S10]
11. Berardino, P., et al., A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. Geoscience and Remote Sensing, IEEE Transactions on, 2002. 40(11): p. 2375-2383. [DOI:10.1109/TGRS.2002.803792]
12. Dehghani, M., et al., InSAR monitoring of progressive land subsidence in Neyshabour, northeast Iran. Geophysical Journal International, 2009. 178(1): p. 47-56. [DOI:10.1111/j.1365-246X.2009.04135.x]
13. Cigna, F., Osmanoğlu, B., Cabral-Cano, E., Dixon, T. H., Ávila-Olivera, J. A., Garduño-Monroy, V. H., & Wdowinski, S. (2012). Monitoring land subsidence and its induced geological hazard with Synthetic Aperture Radar Interferometry: A case study in Morelia, Mexico. Remote Sensing of Environment, 117, 146-161. [DOI:10.1016/j.rse.2011.09.005]
14. M. Voege, R. Frauenfelder, and Y. Larsen, "Displacement monitoring at Svartevatn dam with interferometric SAR," in 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012, pp. 3895-3898. [DOI:10.1109/IGARSS.2012.6350561]
15. pp. 3895-3898. [4] D. Perissin, C. Prati, F. Rocca, and T. Wang, "PSInSAR analysis over the Three Gorges Dam and urban areas in China," in 2009 Joint Urban Remote Sensing Event, 2009, pp. 1-5.
16. Karimzadeh, S., Characterization of land subsidence in Tabriz basin (NW Iran) using InSAR and watershed analyses. Acta Geodaetica et Geophysica, 2015: p. 1-15. [DOI:10.1007/s40328-015-0118-4]
17. Pepe, Antonio. "Multi-temporal small baseline interferometric SAR algorithms: Error budget and theoretical performance." Remote Sensing 13.4 (2021): 557. [DOI:10.3390/rs13040557]
18. Morishita, Yu, et al. "LiCSBAS: An open-source InSAR time series analysis package integrated with the LiCSAR automated Sentinel-1 InSAR processor." Remote Sensing 12.3 (2020): 424. [DOI:10.3390/rs12030424]
19. Singhroy, V. "SAR integrated techniques for geohazard assessment." Advances in Space Research 15.11 (1995): 67-78. [DOI:10.1016/0273-1177(95)00076-Q]
20. Ansari, Homa, Francesco De Zan, and Alessandro Parizzi. "Study of systematic bias in measuring surface deformation with SAR interferometry." IEEE Transactions on Geoscience and Remote Sensing 59.2 (2020): 1285-1301. [DOI:10.1109/TGRS.2020.3003421]
21. Maghsoudi, Yasser, et al. "Characterizing and correcting phase biases in short-term, multilocked interferograms." Remote Sensing of Environment 275 (2022): 113022. [DOI:10.1016/j.rse.2022.113022]
22. Falabella, F., & Pepe, A. (2022). On the Phase Nonclosure of Multilook SAR Interferogram Triplets. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17. [DOI:10.1109/TGRS.2022.3216083]
23. Yujine Zheng, Heresh Fattahi, ET 1L 2019 On Closure Phase and Systematic Bias in Multi-looked SAR Interferometry: IEEE TRANSACTION OF GEOSCIENCE AND REMOTE SENSING . DOI 10.1109/TGRS.2022.3167648, IEEE [DOI:10.1109/TGRS.2022.3167648]
24. Gurrola, E. M., Rosen, P. A., & Sacco, G. F. (2015). ISCE updates: Improved InSAR processing and new tools for science analysis. In IGARSS 2015-2015 IEEE International Geoscience and Remote Sensing Symposium (pp. 3156-3159). IEEE. [DOI:10.1109/IGARSS.2015.7326544]
25. Yunjun, Z., Fattahi, H., & Amelung, F. (2019). Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences, 133, 104331. [DOI:10.1016/j.cageo.2019.104331]
26. Jolivet, R., R. Grandin, C. Lasserre, M. P. Doin, and G. Peltzer (2011), Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data, Geophysical Research Letters, 38(17), L17311, doi:10.1029/2011GL048757. [DOI:10.1029/2011GL048757]
27. Fattahi, H., and F. Amelung (2013), DEM Error Correction in InSAR Time Series, Geoscience and Remote Sensing, IEEE Transactions on, 51(7), 4249-4259, doi:10.1109/TGRS.2012.2227761. [DOI:10.1109/TGRS.2012.2227761]
28. Yun, S.-H., Fielding, E.J., Agram, P., Hensley, S., Yun, H., Simons, M., Webb, F., Rosen, P.A., 2020. MintPy: A Python Package for Interferometric Synthetic Aperture Radar Time Series Analysis. Journal of Open-Source Software 5, 2283. [DOI:10.21105/joss.02283]
29. Sandwell, D., Mellors, R., Tong, X., Xu, X., Wei, M., & Wessel, P. http://topex. ucsd. edu/gmtsar/tar/GMTSAR_2ND_TEX. pdf.
30. Hooper, A., Bekaert, D., Hussain, E., & Spaans, K. (2018). StaMPS/MTI manual: Version 4.1 b. School of Earth and Environment, University of Leeds. Retrieved October, 15, 2019.
Karami E, Azadnezhad S, Maghsoudi Mehrani Y. Investigation of phase bias in the interferometric network in the SBAS algorithm and its impact on different coverages.. JGST 2024; 13 (4) : 2 URL: http://jgst.issgeac.ir/article-1-1153-en.html
