Regular and efficient monitoring of coastal changes is necessary to inform coastal management decisions. In this regard, recent developments based on Google Earth Engine (Gorelick et al., 2017) enable the automatic detection of coastal changes using satellite data (Turner et al., 2021) However, such tools are usually calibrated with limited information and, consequently, they may lead to significant errors when applied to beaches with different conditions than those considered in the calibration data sets. In this work, we evaluate the capability to monitor changes in coastal morphology at various temporal and spatial scales using satellite-derived data obtained from an automatic processing method that includes site-specific information. These method was developed within the framework of the Coastal Erosion from Space Project (https://coastalerosion.argans.co.uk/) that brought together experts from several countries with different backgrounds, such as Earth Observation, Geology, Engineering and Management, to develop satellite products to monitor coastal change based on end-user requirements.

The high frequency acquisition and broad area covered by new satellite missions (e.g. Sentinel-1 and Sentinel-2) provide high amount of information that allows for the monitorization of coastal processes in several time and spatial scales. In the past few years, the potential of using satellite-derived shorelines obtained from optical sensors to detect coastal changes has been deeply explored (Vos et al., 2023). A collaborative work between SAR experts and end- users was developed with the aim of enhancing the understanding of SAR-SL from Sentinel-1 and to assess their potential application to detect coastal change. The results from such a collaboration are presented here through three pilot case studies with different environmental characteristics (e.g. different backshore type and met- ocean conditions). All three sites are located in tidal beaches of the European Atlantic coast, where the probability of cloud cover is high. This allowed to assess the skill of SAR-SL in different environments and in sites that represent a challenge for shoreline detection from optical satellite sensors.