## 1 Introduction  
Landslide processes cause substantial direct and indirect economic damages and are the cause of significant number of fatalities. [PERSON] [1] concluded that, between 2004 and 2010, a total of 32,322 lives were lost all over the world due to non-seismic-triggered landslide events, relating to an annual death toll of more than 4600. The global annual landslide-related economic damage effects are in the order of billions of US$; e.g., for China only, [PERSON] [2] estimated an equivalent of approximately US$ 1.65 billion and 700-900 fatalities each year. Consequently, efforts for landslide forecasting and monitoring have to be increased to be able anticipate potentially threatening events. A wide range of landslide monitoring methods is available to engineering geologists and landslide practitioners. Traditionally, landslide monitoring focused on field-based measurements of ground displacements, e.g., using inclinometers, extensometers, and triggering factors such as groundwater tables and pore-water pressures, e.g., utilizing rain gauges, piezometers, and tensiometers [3; 4; 5; 6]. Today, ground-based methods such as synthetic aperture radar (GB-SAR), Light Detection and Ranging (LiDAR) and Automated Total Station (ATS) are commonly used to examine landslide morphologies and movement rates [7; 8; 9; 10; 11]. Moreover, Unmanned Aerial Vehicles (UAVs) have gained popularity amongst landslide researchers, combined with photogrammetric applications [12; 13; 14], whilst Global Position System (GPS) surveying, even if intrinsically a point-wise technique requiring a careful selection of the points to be monitored, represents a benchmark technique in slope monitoring [15; 16; 17]. This also holds true considering the potential of the endless upcoming of low-cost GNSS receivers, which will probably make GNSS monitoring even more popular in the next future, due to the high-quality performances of new devices [18; 19; 20; 21]. In recent years, several techniques have increased the potential of satellite data for the mapping and monitoring of landslides [22; 23; 24]. The launches of new generations of optical (e.g., Geoeye-1, WorldView-2, Pleiades 1A and 1B, SPOT5, 6 and 7, Formosat-2 and Kompsat-2, Sentinel-2) and radar (e.g., TerraSAR-X, COSMO-SkyMed, Sentinel-1) satellites with short repeat-pass cycles and high spatial resolutions resulted in better capabilities to acquire data over wide areas shortly after major landslide triggering events [25; 26; 27; 28] and to monitor them at regular intervals [29; 30]. In particular, radar interferometry is today frequently applied to landslide detection and monitoring of ground-deformations and landslide processes [31; 32; 33; 34; 35; 36; 37; 38]. Multi-temporal Interferometry (MTI) techniques use large stacks composed of many Synthetic Aperture Radar (SAR) images (i.e., >20) acquired with the same geometry and exploit the redundant information of phase difference, thus removing much inherent noise (in particular reducing the atmospheric contribution called Atmospheric Phase Screen (APS)) for a better accuracy of the phase difference due to the ground displacement [39]. MTI techniques can be grouped in two main classes: Persistent Scatterers (PS) [39] or Small BAseline Subsets (SBAS) [40] to deliver displacements along the Line Of Sight (LOS) direction with accuracy in the order of a few millimeters per year [41]. Whilst building or infrastructure is taken as PS in urban areas [42], artificial Corner Reflectors (CRs) were installed to investigate areas characterized by low interferometric coherence or when a point wise analysis is foreseen [43; 44; 45; 46; 47; 48; 49]. [PERSON] et al. [50] demonstrated the potential of X-band SAR data using TerraSAR-X for landslide mapping and monitoring but a few natural PS were found in mountainous areas. [PERSON] et al. [51] used the same band, combining PS-MTI and SBAS techniques for mapping slow movements in the Alps comparing these results to Real Time Kinematic GPS surveys. [PERSON] et al.