A refined parallel stacking InSAR workflow for large-scale deformation fast extraction—a case study of Tibet

Abstract

The Tibet Autonomous Region has a complex topography, and there are many difficulties in extracting its large-scale surface deformation. Traditional time-series Synthetic Aperture Radar Interferometry (InSAR) approaches are time-consuming and difficult to handle the massive growth of data in a timely manner. In this paper, a refined parallel stacking InSAR workflow for large-scale deformation fast extraction is proposed. The burst images of Sentinel-1 data were first extracted, and a series of procedures were introduced to improve accuracy, including using a short spatial baseline to mitigate topographic error, using a polynomial to fit and remove the small-scale atmospheric error, and using the joint polarization stacking method to reduce random noise. The workflow was deployed to a supercomputing system for parallel processing to improve efficiency. Using 1638 Sentinel-1 acquisitions, the surface deformation rate of the entire Tibet Autonomous Region was obtained and in good agreement with the Global Positioning System (GPS) data, with a Root Mean Squared Error (RMSE) of 3.27 mm/year, indicating high accuracy.

Keywords:

• InSAR
• large-scale
• refined parallel stacking
• Tibet
• supercomputing

Data availability statement

The Sentinel-1 SAR data used in the study are available through https://scihub.copernicus.eu/; the SRTM digital elevation model is available through https://srtm.csi.cgiar.org/; and the GPS data are available through https://data.tpdc.ac.cn/zh-hans/.

Acknowledgement

The authors would like to thank the European Space Agency for the Sentinel-1 data, the National Tibetan Plateau Data Center for the GPS data, NASA for SRTM DEM data, and the SCWU for computational resource support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by National Natural Science Foundation of China projects (NSFC) [grant number 41801397] and National Key Research and Development Program of China [grant number 2018YFC0825803].