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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.

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