ZHAO Feng, ZHANG Leixin, WANG Teng, WANG Yunjia, YAN Shiyong, FAN Hongdong. Polarimetric Persistent Scatterer Interferometry for Urban Ground Deformation Monitoring with Sentinel-1 Dual Polarimetric Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1507-1514. DOI: 10.13203/j.whugis20210496
Citation: ZHAO Feng, ZHANG Leixin, WANG Teng, WANG Yunjia, YAN Shiyong, FAN Hongdong. Polarimetric Persistent Scatterer Interferometry for Urban Ground Deformation Monitoring with Sentinel-1 Dual Polarimetric Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1507-1514. DOI: 10.13203/j.whugis20210496

Polarimetric Persistent Scatterer Interferometry for Urban Ground Deformation Monitoring with Sentinel-1 Dual Polarimetric Data

Funds: 

The National Natural Science Foundation of China 42004011

the Fundamental Research Funds for the Central Universities of China University of Mining and Technology 2020QN27

More Information
  • Author Bio:

    ZHAO Feng, PhD, specializes in InSAR theory and applications. E-mail: feng.zhao@cumt.edu.cn

  • Corresponding author:

    WANG Yunjia, PhD, professor. E-mail: wyj4139@cumt.edu.cn

  • Received Date: April 11, 2022
  • Available Online: September 19, 2022
  • Published Date: September 04, 2022
  •   Objectives  Thanks to the data open access policy of European Space Agency, it has become easy to acquire long time series dual polarimetric Sentinel-1 data over the most of the world's big cities. The use of dual polarimetric Sentinel-1 data is expected to produce better urban ground deformation monitoring results than single polarimetric data, however, there are few studies about this topic. To this end, based on the long time series single and dual polarimetric Sentinel-1 data, this study employs persistent scatterer interferometry (PSI) and polarimetric persistent scatterer interferometry (PolPSI) to monitor the recent ground deformation of the Mexico City in United Mexican States and Beijing City in China, respectively.
      Methods  For the interferograms' polarimetric optimization of PolPSI technique, the exhaustive search polarimetric optimization (ESPO) meth‍od has been used, and the dispersion of amplitude (DA) is taken as the interferometric phase quality criteria. After the polarimetric optimization, for the PolPSI technique, the optimized interferograms are then employed for ground deformation monitoring through PSI processing over the two study areas. The StaMPS algorithm has been employed for the PSI processing for both single and dual polarimetric Sentinel‍-‍1 data sets.
      Results  The results show that, after the polarimetric optimization with dual polarimetric Sentinel-1 data, the phase quality of the interferograms is improved and the proportion of high quality pix‍els is significantly increased. There is a significant increase in high quality pixel after using dual polarimetric data with respect to that of single polarimetric data for ground deformation monitoring. Specifically, the qualified pixels' density has been increased by 88% and 50% for Mexico City and Beijing City, respectively. Moreover, due to the higher pixel density, the obtained ground deformation monitoring results of some sub‍areas are more reliable by using dual polarimetric data.
      Conclusion  This study demonstrates that higher density and better reliability of the qualified pixels can be obtained by using Sentinel-1 dual polarimetric data for urban ground deformation monitoring.
  • [1]
    Cigna F, Tapete D. Present-Day Land Subsidence Rates, Surface Faulting Hazard and Risk in Mexico City with 2014-2020 Sentinel-1 IW InSAR[J]. Remote Sensing of Environment, 2021, 253: 112161 doi: 10.1016/j.rse.2020.112161
    [2]
    王茹, 杨天亮, 杨梦诗, 等. PS-InSAR技术对上海高架路的沉降监测与归因分析[J]. 武汉大学学报·信息科学版, 2018, 43(12): 2050-2057 doi: 10.13203/j.whugis20180150

    Wang Ru, Yang Tianliang, Yang Mengshi, et al. Attribution Analysis on Deformation Feature of the Shanghai Elevated Highway by Persistent Scatterer SAR Interferometry[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2050-2057 doi: 10.13203/j.whugis20180150
    [3]
    师芸, 李伟轩, 唐亚明, 等. 时序InSAR技术在地球环境监测及其资源管理中的应用: 以交城-清徐地区为例[J]. 武汉大学学报·信息科学版, 2019, 44(11): 1613-1621 doi: 10.13203/j.whugis20190068

    Shi Yun, Li Weixuan, Tang Yaming, et al. Time Series InSAR Measurement for Earth Environmental Monitoring and Resource Management: A Case Study of Jiaocheng-Qingxu Area[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1613-1621 doi: 10.13203/j.whugis20190068
    [4]
    Ferretti A, Prati C, Rocca F L. Permanent Scatter‍ers in SAR Interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1): 8-20 doi: 10.1109/36.898661
    [5]
    Berardino P, Fornaro G, Lanari R, et al. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2375-23835 doi: 10.1109/TGRS.2002.803792
    [6]
    Hooper A, Zebker H, Segall P, et al. A New Meth‍od for Measuring Deformation on Volcanoes and Other Natural Terrains Using InSAR Persistent Scatterers[J]. Geophysical Research Letters, 2004, 31(23): https://doi.org/10.1029/2004GL021737
    [7]
    张双成, 司锦钊, 徐永福, 等. 时序InSAR用于安康膨胀土机场稳定性监测[J]. 武汉大学学报·信息科学版, 2021, 46(10): 1519-1528 doi: 10.13203/j.whugis20210223

    Zhang Shuangcheng, Si Jinzhao, Xu Yongfu, et al. Time-Series InSAR for Stability Monitoring of Ank‍ang Airport with Expansive Soil[J]. Geomatics and Information Science of Wuhan University, 2021, 46(10): 1519-1528 doi: 10.13203/j.whugis20210223
    [8]
    Casu F, Manzo M, Lanari R. A Quantitative Assessment of the SBAS Algorithm Performance for Surface Deformation Retrieval from DInSAR Data[J]. Remote Sensing of Environment, 2006, 102(3/4): 195-210
    [9]
    Ferretti A, Fumagalli A, Novali F, et al. A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3460-3470 doi: 10.1109/TGRS.2011.2124465
    [10]
    Lee J S, Pottier E. Polarimetric Radar Imaging: From Basics to Applications[M]. Boca Raton, Flor‍ida, USA: CRC Press, 2009
    [11]
    Pipia L, Fabregas X, Aguasca A, et al. Polarimetric Differential SAR Interferometry: First Results with Ground-Based Measurements[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(1): 167-171 doi: 10.1109/LGRS.2008.2009007
    [12]
    Navarro-Sanchez V D, Lopez-Sanchez J M, Ferro-Famil L. Polarimetric Approaches for Persistent Scatterers Interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(3): 1667‍-1676 doi: 10.1109/TGRS.2013.2253111
    [13]
    Iglesias R, Monells D, Fabregas X, et al. Phase Quality Optimization in Polarimetric Differential SAR Interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2875-2888 doi: 10.1109/TGRS.2013.2267095
    [14]
    Roghayeh S, Hossein N, Mahdi M. Persistent Scatterer Analysis Using Dual-Polarization Sentinel-1 Data: Contribution from VH Channel[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(9): 3105-3112 doi: 10.1109/JSTARS.2018.2848111
    [15]
    Zhao F, Mallorqui J J. A Temporal Phase Coher‍ence Estimation Algorithm and Its Application on DInSAR Pixel Selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8350-8361 doi: 10.1109/TGRS.2019.2920536
    [16]
    Mestre-Quereda A, Lopez-Sanchez J M, Ballester-Berman J D, et al. Evaluation of the Multilook Size in Polarimetric Optimization of Differential SAR Interferograms[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(9): 1407-1411 doi: 10.1109/LGRS.2018.2839179
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