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Summary of Proposal LAN1624

TitleLand Subsidence Monitoring of Yangtze River Delta Urban Agglomerations with Time Series SAR Interferometry Using TerraSAR-X data
Investigator XIAO, Ruya - Hohai University, School of Earth Science and Engineering
Team Member
Dr. Ferreira, Vagner - School of Earth Science and Engineering, Department of Geodesy and Survey Engineering, Hohai Unversity
Prof. He, Xiufeng - Institute of Satellite Navigation and Spatial Information System, Department of Geodesy and Survey Engineering, Hohai Unversity
Dr. He, Min - Institute of Satellite Navigation and Spatial Information System, Department of Geodesy and Survey Engineering, Hohai Unversity
Mr. Akorful, Samuel - Institute of Satellite Navigation and Spatial Information System, Department of Geodesy and Survey Engineering, Hohai Unversity
Summary

Algorithms for time series analysis of SAR data have been developed to better address the major limitations of conventional InSAR since the late 1990s. The time series algorithms fall into two broad categories: persistent scatterer(PS) InSAR and the small baseline approach.

PS algorithms operate on a time series of interferograms all formed with respect to a single “master” SAR image. There are essentially two approaches for determining the level of decorrelation noise for each of the candidate pixels. The first [Ferretti] relies on modelling the deformation in time and the phase is unwrapped during the selection process, by fitting a temporal model of evolution to the double difference phase. The second approach [Hooper] for estimating decorrelation noise relies on the spatial correlation of most of the phase terms and a phase unwrapping algorithm is applied to the selected pixels without assuming a particular model for the temporal evolution. In both Ferretti method and Hooper method, deformation phase is separated from atmospheric phase and noise, by filtering in time and space based on the assumption that deformation is correlated in time; atmosphere is correlated in space but not in time, and noise is uncorrelated in space and time.

By forming interferograms only between images separated by a short time interval and with a small difference in look and squint angle, decorrelation is minimised, and for some resolution elements can be small enough that the underlying signal is still detectable. Pixels for which the filtered phase decorrelates little over short time intervals are the targets of small baseline methods. In many small baseline algorithms, the interferograms are then multilooked to further decrease decorrelation noise and later full resolution operation were also developed. Results show the small baseline method can achieve accuracies similar to those of the PS technique, on the order of ~1 mm/year.

In our project, we will be focused on using Time Series InSAR techniques with TerraSAR data for ground deformation monitoring in Yangtze River Delta, thus analyzing the ground instability and monitoring the activity of groundwater mining. The research will help to gain more insight into the deformation and its evolution in this region and to improve the overall accuracy and reliability of the InSAR technology.

The support mainly comes from the National Natural Science Foundation of China(Grant No.41274017/D0401), Fundamental Research Funds for the Central Universities, Ministry of Education, China (Grant No. 2010B14714) and Post-Graduate Student Research and Innovation Program, Department of Education of Jiangsu Province(Grant No.CXZZ11_0451).

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