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The Emilia-Romagna region (Italy) is characterized by widespread landslide activity, representing a major challenge for land-use planning and risk mitigation. Recent extreme rainfall events have further highlighted the regional susceptibility to slope instabilities (Berti et al., 2024), emphasizing the need for systematic tools to characterize landslide activity at regional scale. In this context, comprehensive monitoring approaches are required to support the Hydrogeological Asset Plan (PAI), particularly under evolving climate conditions.The University of Bologna is actively involved in the observation and analysis of several landslide sites, in collaboration with the Civil Protection Agency, the Regional Geological Service, and the Po River Basin Authority. With over 80,000 mapped landslides across the region, it is impractical to monitor all of them using traditional ground-based geodetic methods. Therefore, satellite-based Differential Interferometric Synthetic Aperture Radar (DInSAR) data is proposed as a key resource, offering broad spatial coverage and the capability to detect millimetric ground displacements over time.However, several challenges must be addressed, particularly in rural and mountainous environments affected by complex types of mass movements such as earth flows, earth slides, debris flows, rock slide and rock fall. These phenomena can destroy or displace radar scatterers, reducing the quality and density of DInSAR measurements.The purpose of this work is to evaluate the feasibility of using satellite interferometry for landslide monitoring in the Emilia-Romagna region and to identify which landslide types are most suitable for DInSAR analysis by combining radar data with the regional landslide inventory prior to the 2023 flood events. Furthermore, the assessment of landslide activity derived from the analyses represents a key outcome of the project and provides valuable support to the PAI.The SAR acquisitions are from the Sentinel 1 mission, covering the period from 2018 to 2022, and processed using Small Baseline Subset (SBAS) (Berardino et al., 2003) algorithm to derive deformation time series and compute the velocity maps. Geospatial analisys was carried out taking into account the spatial distribution and the density of radar scatterers through a clustering process based on the DBSCAN algorithm (Ester et al., 1996).Preliminary results indicate that less than 20% of the landslides can be monitored; however, this fraction still corresponds to several thousand landslides. Satellite interferometry therefore represents a valuable tool to be used complementarily with other satellite-based, airborne, and ground-based instrumentation.The satellite data were processed using the Earth Console service by Progressive Systems, supported by ESA NoR sponsorship. ReferencesBerardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2003). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on geoscience and remote sensing, 40(11), 2375-2383.Berti, M., Pizziolo, M., Scaroni, M., Generali, M., Critelli, V., Mulas, M., ... & Corsini, A. (2024). RER2023: the landslide inventory dataset of the May 2023 Emilia-Romagna event. Earth System Science Data Discussions, 2024, 1-24.Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34, pp. 226-231).