## 3 Materials and Methods  
### SAR Data and Multi-Temporal InSAR Processing  
A long stack of 99 SAR scenes acquired by the Copernicus Sentinel-1A and Sentinel-1B satellites in ascending mode (relative orbit 102) was used in this study. SAR images are remote sensing satellite data increasingly used for observing, mapping and monitoring Earth's surface processes [34] and are characterized by cloud-penetrating, day and night operational capabilities. Among the Sentinel-1 SAR constellation imaging modes, we used the Interferometric Wide (IW) swath, which is based on the novel Terrain Observation with Progressive Scans (TOPS) strategy [35]. IW images are composed of three sub-swaths (i.e., IW1, IW2 and IW3), each consisting of a series of bursts, and provide a 250-km large coverage, with pixel resolutions of 5 m and 20 m (single look) in range and azimuth, respectively [36]. The Sentinel-1 data used in this study were collected from the Sentinel Hub platform and cover the time period from March 2015 to August 2019. The ascending mode acquisition geometry was selected to undertake the analysis in order to optimize visibility of the east-facing slopes, where the town of Methana (and thus the major urban settlement of the peninsula) is located (see Figure 1). The stack of 99 Sentinel-1 SAR scenes was processed using an advanced MT-InSAR approach, an extension of the basic technique of Differential InSAR (DInSAR; e.g., [37; 38]), that is based on the phase comparison of multiple SAR images gathered at different times over the study region and allows for measurements of land deformation along the line-of-sight (LOS) direction of the SAR sensor with up to millimetre precision (e.g., [39]). Advanced DInSAR methods are increasingly being used in various applications in the field of volcanology, seismology, crustal dynamics, landslides, land subsidence and geothermal energy exploitation (e.g., [40; 41; 42; 43; 44; 45]). MT-InSAR methods exploit either Persistent (PS) or Distributed (DS) Scatterers or coherent targets. Typically, PS are artificial objects that reflect the radar signal well, such as metal structures, buildings and rock outcrops, and are used in Persistent Scatterer Interferometry (PSI), e.g., the Permanent Scatterers (PS-InSAR) technique [46; 47]. In urban areas, there is a prevalence of PS, and PSI methods allow for analysis of even individual structures on the ground. Methods exploiting coherent targets include algorithms such as the Small BAseline Subset (SBAS) method [48]. DS reflects lower radar energy compared to PS targets and usually spans several pixels in high-resolution SAR images [49], which exhibit similar scattering properties and can be used together for deformation estimation. The identification and monitoring of such targets is helpful especially in suburban and more rural areas, where the density of PS can be low. In such circumstances, the combination of PS and DS is important for effective displacement monitoring using SAR Single Look Complex (SLC) data. What is certain is that the processing aiming to detect both PS and DS requires large computing resource and processing time [50]. Concerning the precision of MT-InSAR measurements, error bars are extremely complex to estimate theoretically, since they depend on several factors, including the number of SAR SLC images used, the spatial density and quality (signal-to-noise ratio levels) of the measurement targets, their distance from the reference PS, the climatic conditions at the time of SAR acquisition and the satellite repeat cycle. Recent studies provide an empirical relationship between the number of SAR interferograms used and the resulting precision of the ground deformation estimates (e.g., by using 50 ERS-1/2 interferograms, a precision of 0.15 cm/year can be achieved with the SBAS method [51]). In this study, the GAMMA SAR and Interferometry software (GAMMA Remote Sensing AG, Gumligen, Switzerland) and the Interferometric Point Target Analysis (IPTA) method [52] were used for the interferometric processing.