## 1 Introduction  
### Background  
Satellite based A-DInSAR (Advanced-Differential Interferometry Synthetic Aperture Radar) techniques have been used since the end of the last century as powerful tools to detect, monitor, and analyze natural hazards. These methods combine the information obtained from a set of satellite SAR interferograms to retrieve deformation time-series, ground displacement velocities, topographic information, atmospheric artifacts, and orbital errors [1]. Among them, Persistent Scatter Interferometry (PSI) [2; 3] and Small Baseline Subsets (SBAS) [4] are the most representative ones. The authors of [5] provide a comprehensive review of such methods. The range of applications is wide, including seismic and volcanic risk [6; 7; 8], subsidence phenomena [9; 10; 11; 12], landslide activity [13; 14; 15; 16], overexploitation of aquifers [17; 18; 19], and civil infrastructure monitoring [20; 21; 22]. One of the key factors of these techniques is the great satellite data availability acquired by different satellite constellations providing different performances: (1) C-Band Satellites (ERS-1/2, Envisat ASAR, Radarsat, and Sentinel-1) with a wavelength around 5.5 cm, (2) L-Band (ALOS PALSAR) with a wavelength around 23 cm, and (3) X-Band (TerraSAR-X, COSMO-SkyMed and PAZ), with a wavelength around 3.1 cm. These constellations will be complemented in the near future with the new generation of high-resolution satellites such as Capella Space (REFE) or Ice Eye (REFE). They will be able to provide 30-40 cm resolution images every few hours. In this paper, we have used images acquired by the C-Band ESA constellation Sentinel-1, which provides a unique performance acquired over the whole Earth every 6 days. This represents a significant improvement with respect to previous C-Band satellites, considerably increasing the monitoring potential of geohazards over large areas [23]. Moreover, the medium size wavelength (5.6 cm) and the relatively high resolution (\(\approx\)4 \(\times\) 14 m) provide a good trade-off to map ground displacement over the bare surface and sparse vegetation [24], which are the most common types of surface in our area of interest. Rocky coasts represent about 75-80% of coasts worldwide [25]. In recent decades, several authors [26; 27; 28; 29] have addressed their studies of these coasts from a geomorphological and evolutionary point of view. Some of them [30; 31; 32; 33] have focused on the development of slope instabilities and the measurement of cliff retreat rates. Others [14; 34; 35; 36] have applied different A-DInSAR techniques to monitor terrain deformation by means of InSAR data. In coastal areas, the SAR dataset provided by Sentinel-1 A/B is used to define the coastline [37], to detect landslide activity [23; 38; 39], and to analyze ground displacements and subsidence phenomena [40; 41; 42]. The cliff coast of Asturias has already been studied by [43; 44; 45], focusing mainly on the development of slope instabilities that causes coastal retreat. These studies have included photointerpretation analysis, the use of unmanned airborne vehicles (UAV), and field work topographic campaigns since June 2018, continuing to date. The present work is based on the use of A-DInSAR techniques to detect and monitor coastal instabilities over a regional sector of the central Asturian coast (N Spain) (Figure 1), with the main goal of analyzing their relationship with coastal retreat. For this purpose, different local areas with known landslides and other ground movements have been chosen for study, considering previous data recorded in the BAPA (_Base de datos de Argayos del Principado de Asturias_-Principality of Asturias Landslide Database [46]. Considering that A-DInSAR techniques are only capable of detecting very slow movements (\(<\)1.6 m year\({}^{-1}\)) [47], the scope of this work aims to assess the potential and limitations presented by the A-DInSAR techniques for mapping and monitoring of ground movements in cliff coastal areas.