Volume 42 Issue 6
Jun.  2017
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YANG Mengshi, LIAO Mingsheng, SHI Xuguo, ZHANG Lu. Land Subsidence Monitoring by Joint Estimation of Multi-platform Time Series InSAR Observations[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6): 797-802. DOI: 10.13203/j.whugis20140924
Citation: YANG Mengshi, LIAO Mingsheng, SHI Xuguo, ZHANG Lu. Land Subsidence Monitoring by Joint Estimation of Multi-platform Time Series InSAR Observations[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6): 797-802. DOI: 10.13203/j.whugis20140924
shu

Land Subsidence Monitoring by Joint Estimation of Multi-platform Time Series InSAR Observations

  • 1.

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • 2.

    Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China

Funds: 

The National Natural Science Foundation of China 61331016

the Key Projects of Natural Science Foundation of Hubei Province 2014CFA047

the National Program on Key Basic Research Project of China 2013CB733205

More Information
  • Author Bio:

    YANG Mengshi, PhD, specializes in SAR data processing and analysis. E-mail:yangms@whu.edu.cn

  • Received Date: July 05, 2015
  • Published Date: June 04, 2017
    Graphical Abstract
    • Abstract
  • A method for estimating the land subsidence velocity field was proposed by combining m ulti-platform InSARdata set. The precise estimation of subsidence could be achieved by combing redun-dant observations.However, the key problems to be solved in integration of different SAR datasetsincluded different imaging geo metries and inconsistent spatial/tem poral resolutions.We discussed allthese problems in detail and given one solution. Our method then was applied to detect unbiased long-term velocity field in Shanghai, China with 18 Terra SAR-X、16 ENVISAT ASARand 20 ALOS PAL-SAR datasets. Firstly, large-scale velocity maps from three SAR data stacks were extracted whichshow similar deformation patterns and different nu merical ranges. Then, weighted least squares ad-justment was used to derive unbiased velocity field. The experimental results were validated with lev-eling data. The experiment results show that combing multi-platform InSA R data achieved precise es-timation of deformation without any prioriinformation.
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    • References (10)
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