## 1 Introduction  
Coastal areas are subject to several natural and human-induced changes. The increasing apprehension worldwide revolves around the anticipated alterations and potential threats to individuals, ecosystems, and the natural environment [1]. Understanding the dynamic nature of coastal areas and predicting future behaviors is crucial for informed planning decisions. Quantifying physical changes along shorelines and their response to human actions plays a pivotal role in this endeavor. Often, the rates of physical change are expressed in various units [2], with the common measure being meters per year. While shoreline is loosely defined as the meeting point of water and land surfaces, its dynamic nature and dependence on temporal and spatial scales necessitate the use of different shoreline indicators. These indicators can be observed with diverse techniques and technologies [3, 4]. Over the past 38 years, remote sensing and satellite technology have been used to monitor coastal areas, offering an abundant resource for obtaining historical shoreline datasets spanning various spatial scales. These datasets can range from small-scale localized studies to groundbreaking global applications, exemplified by the endeavors of [5; 6]. In this domain, two types of satellite imagery play a crucial role: multi-spectral images (optical) and synthetic aperture radar (SAR) images. The former, often referred to as a \"passive sensor\", captures data within the optical bandwidth. When a scene is illuminated by sunlight or another light source, different materials absorb and reflect light in distinct ways, creating specific spectral signatures. These unique patterns of reflected light at various wavelengths can be combined to generate indicators capable of discerning the boundary between land and sea [7; 8; 9; 10]. Nonetheless, optical sensors come with several limitations. The precision and frequency of coastal change measurements are compromised when cloud cover or other atmospheric conditions obstruct the area of interest. Countries like the United Kingdom, Ireland, or northern Spain (Cantabrian Sea) experience persistent cloud cover throughout much of the year. Consequently, despite numerous optical satellite acquisition operations, only a few provide valuable data for establishing reliable shorelines. As opposed to optical sensors, SAR is an \"active\" microwave imaging system, belonging to a wider family of imaging radars, generically named side-looking airborne radars. It is mounted on a platform moving at a constant velocity along a trajectory (azimuth direction) [11]. During its movement, the sensor transmits a sequence of closely spaced electromagnetic waves at regular intervals towards the Earth. These high-power pulses traveling at the speed of light along the line of sight radar-target (slant range) interact with the Earth's surface, and in most cases, their energy is partially re-irradiated back (backscattered) to the receiving antenna a short time later at a slightly different location, as the satellite travels along its orbit. During the step of recording the signal backscattered from the surface, both its amplitude and phase are considered, and they depend on the physical (i.e., geometry, roughness) and electrical properties (i.e., permittivity) of the imaged scene [12] but also on radar system parameters, such as electromagnetic wave polarization (horizontal or vertical) of transmitting and receiving antennas or transmitted power. A complex focusing technique applied to all the recorded signals generates a 2D reflectivity map that can be used to discriminate among targets depending on the strength of the backscattered signal [13]. Although many image processing techniques have been developed for shoreline extraction using SAR imagery, it is not entirely understood to what extent the signal returned from the sea surface differs from that received from the adjacent land surface. This fundamental disparity stands as one of the most critical factors affecting the detection of the land-sea boundary.