# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
## 1 Introduction  
Landslides are one of the most widespread geohazards in the world, producing a significant social and economic impact. During the period between 1998 and 2017, more than 4.8 million people were affected by landslide incidents in the world, and of these 18,414 were fatalities [1]. In Europe, about 312 fatalities were caused by landslides between 1998 and 2009 [2] and the direct costs of these landslides were estimated to reach \(\ell 48\) billion. Asturias (NW Spain) represents a landslide-prone area in Southwest Europe. In this region, approximately 62 slope instabilities are recorded per year [3, 4], with an annual direct cost of 66 million euros [5]. According to [6, 7], rainfall and heavy rains are the most important physical processes for triggering landslides in Asturias, followed by human activities and other factors. A geomorphological approach is a well-established method in Earth Sciences for examining surface processes and landscape evolution. Geomorphology, particularly geomorphological mapping, is widely used in various applied fields such as natural hazard assessment, environmental planning, and civil engineering, among others [8, 9, 10]. Traditionally, assessing slope movement relied on geomorphological knowledge, utilizing evidence observed through field campaigns and aerial photography [6, 11]. Establishingthe features, size, and evidence of movement is crucial for ensuring accurate geomorphological mapping of mass movements at a local scale. Subsequently, instruments like inclinometers, GPS, and piezometers enabled precise and quantitative measurements, allowing continuous monitoring to determine slope stability [12]. Today, remote techniques and geophysical methods offer high-resolution surface and deep characterization of mass movements, marking significant progress in ground movement studies [13; 14; 15]. However, validating the results of these remote techniques is crucial, as they may sometimes exaggerate or underestimate slope displacement due to various factors like reflector deficits or atmospheric conditions [14]. Instrumental techniques serve as a reliable means to validate remote data, but their applicability may be limited by factors like accessibility or cost. In such cases, traditional geomorphological techniques, relying on fieldwork or photointerpretation and not requiring sophisticated equipment, become the only available means to validate remote data. On the other hand, analyzing weather and climate data to study landslide activity and evolution has proven to be a reliable and effective method [16; 17]. The climate approach encompasses simple rainfall-landslide relationships, multi-factor empirical assessment methods, distributed physically based models, and real-time warning systems [18]. During the last two decades, instrumental techniques based on Synthetic Aperture Radar (SAR) have been successfully used for detecting, modeling, and monitoring landslides [19; 20; 21; 22]. These techniques calculate the ground surface displacement that occurs between two different radar images, collected by sensors aboard a satellite [23]. A progressive comparison of the images during a certain period (usually years) allows a multi-temporal analysis of the displacement, from which time series of surface displacement and annual mean velocities can be obtained. Moreover, these advanced interferometric techniques allow the monitoring of large areas of land (about 10,000 km\({}^{2}\)) with millimeter accuracy [24; 25; 26; 27]. Particularly, the SAR data provided by Sentinel-1A/B satellite constellations have shown a high applicability in the detection and monitoring of landslides due to: (1) high spatial resolution (14 \(\times\) 4 m) and wavelength (5.5 cm); (2) temporal resolution (12 and 6 days); (3) free access to SAR data and current development of tools such as the Geohazard Exploitation Platform (GEP) [28; 29; 30] and the European Ground Motion Service (EGMS) [31; 32; 33].