Revealing long-term deformation time series of radar scatterers using multi-sensor SAR data

Synergizing SAR multi-sensors facilitates long-term deformation time series monitoring of radar scatterers on the Earth surface. Due to the disparity in e.g. radar wavelength, incidence angle, orbital direction, and polarization, however, there is no straightforward way to concatenate such time series from different SAR sensors. This study as an extension of [1] proposes the use of tie-point pairs, i.e. scatterers that are most likely reflected from a common ground target, aiming at integrating multi-sensor SAR data to monitor surface deformation without the loss of spatial resolution. Tie-point pairs are identified using geolocation uncertainty of radar scatterers. A probabilistic temporal model of tie-point pairs' time series is developed to link deformation time series from different sensors. We tested the proposed approaches in Groningen, The Netherlands, using 82 Radarsat-2 (C-band, July 2009 - June 2015) and 13 ALOS-2 (L-band, September 2014 - May 2020). Finally we identified 3315 tie-points with three different intersection types and determined their best temporal models. For those points, the maximum vertical subsidence velocity is up to 10 mm yr-1 between 2009 and 2020.