Quantifying uncertainty in InSAR-derived displacement measurements for bridge monitoring

Bridge monitoring is essential for ensuring their long-term safety, reducing disruptions, and supporting economic and social activities. Traditional monitoring techniques rely on periodic inspections and contact-based sensing systems, which, while effective, have limitations due especially to high installation and maintenance costs. Satellite-based Synthetic Aperture Radar Interferometry (InSAR) offers a complementary, non-intrusive solution for Structural Health Monitoring, providing displacement time histories with millimetric precision through velocity maps. However, InSAR data from different orbits are typically acquired at different times and with non-homogeneous spatial distributions of measuring points. Therefore, when combining InSAR measurements from ascending and descending satellite orbits, spatial and temporal resampling processes are necessary, which introduce uncertainty in the reconstructed structural displacements. This paper applies a recently developed method to quantify the uncertainty introduced by resampling processes, focusing on grid spatial subsampling and linear temporal interpolation. Using displacement data from the European Ground Motion Service (EGMS), we investigate the behavior of the Schottwien Viaduct, a major prestressed concrete bridge in Austria. By employing error propagation theory, we assess how InSAR data processing impacts the precision of InSAR-derived displacement estimates. The analysis provides insights into uncertainty quantification, which informs the selection of the most suitable EGMS dataset and guides the optimization of grid configurations for structural displacement reconstruction. The findings contribute to improving the reliability of satellite-based SHM and highlight key considerations for integrating InSAR data into large-scale infrastructure monitoring.