## 4 Discussion  
The results presented here highlight the potential of applying SAR images for analyzing changes in coastal morphology and raise questions that will benefit from further research. SAR-SL time series (e.g., from S1) enable between 10 and 25 times more observations for a given time period when compared with those derived from optical sensors (e.g., S2; see Table 1). This advantage arises from SAR's independence from cloud cover and day light [47], making SAR data powerful information to detect short-term changes, such as those that occur during severe storms. Such high-frequency shoreline time series would also allow for defining other coastline indicators. For example, some authors highlighted the advantage of using probabilistic shorelines instead of deterministic ones when assessing coastal dynamics [47; 48]. This kind of SL indicators allow for dealing with the uncertainty in shoreline detection by estimating a probabilistic SL position based on several measurements.  
Through the validation analysis carried out in the three tidal beaches studied here, we explored the ability of SAR to detect coastal changes in sites that represent a challenge for typical satellite monitoring techniques, and the results demonstrated a coherence between SAR-SLs and the ground-truth data. We have shown how the rates of change obtained from SAR-SLs agree with the change rates derived from the vegetation lines in Bull Island; the SAR-SLs in Salinas seem to be mapping changes in the upper shoreface and are able to capture the beach rotation observed in Start Bay. However, although SAR-SL time series were correlated to the changes observed in the ground (CRs, extreme TWL, and beach rotation), our findings also highlighted that SL detection using this technology is a complex and multifaceted task. This complexity arises from the dependence of SAR-SL detection on a set of variables, including but not limited to the relationship between the satellite orbit (ASC or DESC) and the geometry of the observed coastal scene, the sea conditions at the moment of SAR detection, the dielectric constant properties of sand, and the fluctuating tidal level. Each of these factors has an impact on the SAR signal and makes SAR-SL characterization a challenge. For example, the complexity of the SAR signal makes it difficult to define what is being detected as shoreline at each site, as local environmental conditions may play a significant role in this task [47]. Local characteristics may also result in difference in shoreline detection from ascending and descending orbits, and it is crucial  
Figure 19: Annual CRs derived from ASC and DESC shorelines at Start Bay shown on top of the 2016-2018 digital elevation model of difference (DoD). Histograms show the annual rate value distribution in three categories (erosive in red, neutral in yellow, and accretive in blue). Neutral changes (yellow bins) are shown as transparent polygons in the maps for clarity. BGS ©UKRI.  
to identify which data should be used at each site to obtain reliable results regarding coastal changes. Based on the results we obtained from the three study sites, in the following, we discuss some of the site-specific aspects that may affect SAR-SL detection, highlighting the points that still need to be further explored to improve shoreline change analysis based on SAR techniques.