General considerations for a Sentinel-1 constellation InSAR time series processing chain for ground deformation measurements

Wednesday, 17 December 2014
Pablo J Gonzalez, Tim J Wright, Andrew J Hooper and Richard J Walters, University of Leeds, COMET, School of Earth and Environment, Leeds, United Kingdom
Sentinel-1A was launched on April 3rd, 2014. It is the first satellite of a European Space Agency (ESA) constellation that promises to revolutionize measurement of deformation of the Earth’s surface. The constellation is designed to acquire data globally as frequently as every 6 days on the same orbital pass, and every 3 days in alternating ascending and descending orbits over the same regions. This data acquisition plan is possible due to a much larger swath coverage than previous SAR (Synthetic Aperture Radar) sensors. In addition, all observations from Copernicus, the European Commission Earth Observation program, have a liberal data policy, which will enable full exploitation of the archived Sentinel-1 big data, both for scientific and commercial use.

Sentinel-1, and similar future constellations, shape a new landscape in the way that InSAR data have traditionally been processed. We have started to develop a completely new re-engineered and adapted InSAR time series processing approach, which efficiently processes the data from this new type of SAR constellation, with the goal to deliver ground deformation products with the highest possible precision. In summary, the proposed system approach will require the development of an automatic, almost unsupervised, system that integrates methods to obtain time-dependent surface deformation estimates and correction products for atmospheric noise and refined orbits. The ground velocity maps will ideally meet the desired precision of 1 mm/yr / 100 km to measure strain-rates (10 nanostrain/yr) at a comparable level of precision to current existing sparse regional GPS measurement networks.

In this communication, we describe the different steps we have adopted to partially solve: 1) coregistration of TOPS (Terrain Observation with Progressive Scans) SAR images to enable interferometry, 2) how to manage the ambiguity between ground motion in azimuth and in line-of-sight for TOPS InSAR, 3) how to process efficiently newly acquired data, 4) adaptive filtering of interferograms, 5) how to select reliable temporally stable pixels for the phase unwrapping.