The PSI technique represents an advanced class of the differential interferometric SAR (DInSAR) techniques, which makes use of multiple SAR images acquired over the same site and appropriate tools to separate the deformation signal of interest from other components of the PSI observations, such as the residual topographic error component, the atmospheric component and the phase noise. For a comprehensive review of PSI, see Crosetto et al. (2016).

The PSI techniques have experienced a major development in the last decades, which has been mainly related to C-band data from ERS-1/2, Envisat and Radarsat sensors. The advent, in 2007, of very high resolution X-band data (TerraSAR-X and CosmoSkyMed) enabled a major step forward for the PSI techniques. A new significant improvement comes from the availability of C-band data from the sensors on board the Sentinel-1A and 1B satellites. The main PSI activities of our group include:

1) The in-house development of PSI software tools. We have developed a number of tools to perform different types of DInSAR and PSI analyses, e.g. see Biescas et al (2007). An overview of such tools is provided in Crosetto et al. (2011). An in depth description of one of the PSI approaches is described in Devanthéry et al. (2014).

2) Experience in processing interferometric SAR data from multiple SAR sensors, including C-band (ERS, Envisat, Sentinel-1), X-band (TerraSAR-X and CosmoSkyMed) and L-band (ALOS).

3) Development of advanced PSI applications. Examples include:

  • the geohazards regional monitoring and forecasting based on Sentinel-1 images of the ECHO projects Safety ( and UGeohaz (web under construction).
  • other applications include: bridge monitoring (Huang et al., 2017), active deformation areas (Barra et al., 2017), glaciar monitoring (Dematteis et al., 2017), etc.
  • the development of active corner reflectors for landslide monitoring, which is part of the H2020 GIMS project.


Deformation of a reclaimed area, estimated using TarraSAR-X imagery.


Deformation map of Catalonia (Spain), estimated using Sentinel-1 imagery.


, L. Solari, M. Béjar-Pizarro, , S. Bianchini, G. Herrera, , R. Sarro, A. G.E., R. Maria Mateos, S. Ligüerzana, C. López, S. Moretti, A Methodology to Detect and Update Active Deformation Areas Based on Sentinel-1 SAR Images , Remote Sensing, Vol. 9, No. 10, September 2017.

E. Biescas, M. Crosetto, M. Agudo, O. Monserrat, B. Crippa. Two radar interferometric approaches to monitor slow and fast land deformation. Journal of Surveying Engineering, 133(2), 66-71. 2007.

M. Crosetto, O. Monserrat, M. Cuevas, B. Crippa. Spaceborne Differential SAR Interferometry: Data Analysis Tools for Deformation Measurement, Remote Sensing, 3, 305-318, doi:10.3390/rs3020305. 2011.

, B. Crippa, Persistent Scatterer Interferometry: A review , ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 115, pp. 78-89, May 2016.

N. Dematteis, , D. Giordan, F. Zucca, P. Allasia, Monitoring Alpine glacier surface deformations with GB-SAR , Remote Sensing Letters, Vol. 8, No. 10, June 2017.

, B. Crippa, An Approach to Persistent Scatterer Interferometry , Remote Sensing, Vol. 6, No. 7, pp. 6662-6679, July 2014.

Q. Huang, , B. Crippa, Displacement monitoring and modelling of a high-speed railway bridge using C-band Sentinel-1 data , ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 128, pp. 204-211, 2017.