## 1 Introduction  
In the last two decades, many studies have been carried out worldwide aiming to identify and to map land cover variations or post-event damaged areas through change detection approaches, using remotely sensed data acquired by different sensors and platforms (e.g., Satellite, UAV) applied to floods [1], volcanoes [2], earthquakes [3; 4; 5; 6; 7], wildfires [9], urban development [10; 11] and landslides [12]. The first step in any emergency response is to assess the extent and the impact of the damage caused by the disaster. First responders need to recognize and to collect useful information to mount their rescue operation effectively and quickly. There is indeed a strong link between timely rescue operations and the percentage of survived victims from natural disasters. Therefore, it is extremely important to ensure effective deployment of rescue teams as soon as possible by means of the optimization of resources, accurate information on how to access and to settle in the affected areas, and the definition of operational priorities. To further optimize the activity on the field, the potential of SAR satellite analysis was approached, thinking backwards about the seismic event of 30 October in Norcia (occurred at 06:40, UTC), where a damage map derived by CNVVF field surveys and satellite products for comparative analysis were available. Among the different kinds of Earth Observation systems, satellite SAR data are considered one of the most powerful systems for natural hazard detection and assessment due to their advantage of being independent from atmospheric and sunlight conditions and the capability of satellites to regularly acquire data over the same area [13]. Today, several satellite SAR missions are available, characterized by different technical features in terms of wavelengths, and temporal and geometric resolutions [14]. COSMO-SkyMed (Italian Space Agency) is one of the most powerful datasets available thanks to its 5-satellite constellation equipped with X-band SAR sensors [15] that can provide images with geometric resolutions up to 0.35 m (Spotlight mode), with a potential sampling frequency of a few hours. On the other hand, SAR missions such as C-band Sentinel 1 (made up of two satellites since 2015) are collecting images worldwide with a 6-day sampling frequency and a 5 \(\times\) 20-m geometric resolution. In the last few years, a growing number of missions that are expected to provide high-resolution (1 m or less) images with high sampling frequencies (days/hours) are under development or in planning (i.e., [16; 17]) and, in the light of this scenario, it is important to understand the performance of satellite SAR images characterized by different geometric resolutions to better support emergency phases after natural disasters. With this aim, we focused on the 2016 central Italy seismic sequence that caused severe damage in several municipalities in the region [18; 19]. This sequence of moderate to large earthquakes produced three main shocks: 24 August 2016 (Mw 6.0), 26 October 2016 (Mw 5.9), and 30 October 2016 (Mw 6.5). The epicenter of the latter was located close to the small towns of Norcia and Castelluccio. In these two towns that were severely affected by the 30 October 2016 earthquake, we had the opportunity to exploit the availability of three prevent and post-event datasets of SAR images with different geometric features: Sentinel-1 Interferometric Wide Swath (20 m \(\times\) 5 m meter nominal geometric resolution); COSMO-SkyMed StripMap (background mission in Italy with 3-m nominal geometric resolution), and COSMO-SkyMed Spotlight (1-m nominal geometric resolution) acquisition modes. Spotlight is not planned to be the background acquisition mode of COSMO-SkyMed but it is, at present, among the radar satellite missions one of the best images resolutions, and in this area we have the very rare opportunity to have pre-event acquisitions and to test the performance of such a radar image, especially considering the future satellite mission planned.