## 1 Introduction
Most shoreline detection techniques rely on this difference in the returned signal from the land and the sea. The contrast between the land and the sea is significantly influenced by many factors, including the polarization method used for transmitting and receiving the electromagnetic waves, the land morphology (presence/absence of cliffs or dune systems), and the sea surface conditions. The polarization method used for transmitting and receiving electromagnetic waves determines the strength of the backscatter from the land or sea, which affects the contrast between the two. The land morphology can cause shadows or reflections, which further affect contrast. The sea surface conditions, such as waves and foam, also have a significant impact on contrast. Also, SAR images are affected by speckle, a multiplicative noise that gives the images a grainy appearance and makes interpretation more challenging than with optical images. Furthermore, most of the above-mentioned techniques have been employed using a restricted number of SAR scenes. To the best of the authors' knowledge, no comprehensive utilization of the abundant public satellite imagery has been implemented to investigate and assess shoreline evolution over time through SAR as it has been performed with optical imagery [14]. In previous works [15; 16], an initial effort was made to interpret SAR shorelines (hereinafter SLs) by comparing them with SLs extracted from optical satellite data. The studies utilized publicly available satellite imagery to explore how effectively these two technologies can map and capture SL variations on a sub-annual to inter-annual basis. Although the studies yielded interesting results, the authors acknowledged the potential for misleading comparisons due to the complexity of SAR images. Consequently, they emphasized the need for further analysis to gain a comprehensive understanding of what SAR observes. Motivated by the unresolved questions surrounding SAR, this paper seeks to enhance our comprehension of SAR capabilities by thoroughly investigating the ground-based features detected in SLs in thousands of images collected between 2015 and 2021. To achieve this goal, a novel approach is introduced, involving both qualitative and quantitative analyses of SAR-derived SLs. This approach includes an improved processing chain for SL extraction and a series of validation methods to ensure the accuracy and reliability of the results. The manuscript offers a comprehensive overview of the SAR product utilized in this analysis and presents a detailed explanation of the steps involved in extracting shorelines from SAR data. The various filters applied to the SAR product, as well as the \"qualitative\" outcomes that can be achieved with this technology, are discussed. Subsequently, the study introduces three distinct sites with different environments, namely, a flat beach (Bull Island); high dunes of sand (Salinas beach); and a steep, rocky cliff (Start Bay). Each site undergoes a validation process tailored to its unique characteristics, enabling a \"quantification\" of what SAR detects over time. The manuscript concludes with a discussion of the findings, providing valuable insights into the lesser-understood aspects of utilizing SL data derived from publicly available SAR satellite missions.