Uncertainty propagation in satellite InSAR data analysis for structural health monitoring

Structural Health Monitoring (SHM) supports the management of the integrity and functionality of critical infrastructure like bridges. Interferometric Synthetic Aperture Radar (InSAR) offers a scalable, non-intrusive alternative to conventional methods, ideal for large-scale and automated monitoring. However, spatial and temporal resampling, commonly used to reconstruct structural displacements from InSAR data, introduces uncertainty into final estimates. This paper presents a framework to quantify and reduce this uncertainty. A case study of a bridge in Rome, Italy, demonstrates that uncertainty metrics can effectively guide improvements in Persistent Scatterer (PS) clustering and grid configuration. Specifically, removing off-bridge PSs from a single grid cell reduced displacement uncertainty by 45 %, while replacing a fixed grid with a tailored layout for the entire bridge resulted in a 12 % average reduction in uncertainty. These findings provide valuable guidance for optimising InSAR data processing in SHM and underscore the critical role of PS clustering in improving measurement precision.