Abstract
Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is a widely used technique for local ground deformation estimation due to its millimetric accuracy and full-resolution results. However, with the enhancement of data acquisition capability of the SAR sensors, larger coverage and higher temporal-spatial resolution of SAR images can cause explosive increase and uneven distribution of PS points. PS network constructed between PSs with different qualities may cause spatial-error propagation. Large PS points list with large amount of data also have high requirements on computing performance and storage space. Those problems can bring big challenge for PS-InSAR technique in processing capacity, deformation monitoring accuracy and algorithm efficiency. In order to effectively overcome those limitations, this paper proposed a novel block PS-InSAR method that uses the approach of partitioning a study area into regular blocks with overlapping regions, selecting a high-quality reference point for each block and eliminating the discontinuity of the parameter calculating results in adjacent blocks by the weighted least square method. We used this approach to analyze ground subsidence in over 13,000 km2 of southern California, and it performed successfully when 69 Sentinel-1 images were used and 21,029,968 PSs were selected. Only approximately 10 h was needed for this experiment. Comparing the deformation rate and time series of the InSAR results with 29 GPS observations, the mean and standard deviation of difference of deformation rate were − 0.63 mm/yr and 1.53 mm/yr, respectively, and the average root mean squared error of deformation time series was 3.7 mm. The experiment demonstrated that this method could achieve high accuracy results without spatial discontinuity and increase the computational efficiency significantly.
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Data availability
The Sentinel-1 datasets analyzed during the current study are available from https://search.asf.alaska.edu/, and the GNSS datasets are available from Southern California Integrated GPS Network (SCIGN, http://www.scign.org).
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Acknowledgements
This work was partly supported by the National Key R&D Program of China, Grant Number 2018YFC1503603; the National Science Fund for Distinguished Young Scholars, Grant Number 41925016; the National Natural Science Foundation of China, Grant Number 41804008; and the Fundamental Research Founds for the Central Universities of Central South University, Grant Number 2019zzts296. We would like to thank ESA (European Space Agency) for supplying the Sentinel-1 datasets. The GPS data used were from the SCIGN website. We thank the anonymous reviewers for their valuable comments, which have helped to improve the quality of this paper.
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B.X. and Z.L. proposed the conception; J.H., B.X. and Z.L. conceived the framework of this research; J.H. and B.X. designed and performed the experiments, analyzed the data in the paper and wrote the paper; Z.L., Y.Z. helped to analyze the data and write the paper; G.F. helped perform the experiments and edit the paper.
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Hou, J., Xu, B., Li, Z. et al. Block PS-InSAR ground deformation estimation for large-scale areas based on network adjustment. J Geod 95, 111 (2021). https://doi.org/10.1007/s00190-021-01561-1
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DOI: https://doi.org/10.1007/s00190-021-01561-1