Coasts are continually changing and remote sensing from satellites has the potential to both map and monitor coastal change at multiple scales. Unlike optical technology, synthetic aperture radar (SAR) is uninfluenced by darkness, clouds, and rain, potentially offering a higher revision period to map shoreline position and change, but this can only be feasible if we have a better interpretation of what shorelines as extracted from SAR imagery represent on the ground. This study aims to assess the application of shorelines extracted from SAR from publicly available satellite imagery to map and capture intra-annual to inter-annual shoreline variability. This is assessed in three tidally controlled coastal study areas that represent sand and gravel beaches with different backshore environments: low-lying dunes and marsh; steep, rocky cliff; and urban environments. We have found that SAR shorelines consistently corresponded to positions above the high-water mark across all three sites. We further discuss the influence of the scene geometry, meteorological and oceanographic conditions, and backshore environment and provide a conceptual interpretation of SAR-derived shorelines. In a low-lying coastal setting, the annual change rate derived through SAR presents a high degree of alignment with the known reference values. The present study contributes to our understanding of the poorly known aspect of using shorelines derived from publicly available SAR satellite missions. It outlines a quantitative approach to automatically assess their quality with a new automatic detection method that is transferable to shoreline evolution assessments worldwide.

This study introduces a novel methodology for the automated co-registration and georeferencing of satellite imagery to enhance the accuracy of shoreline detection and coastal monitoring. The approach utilizes feature-based methods, cross-correlation, and RANSAC (RANdom SAmple Consensus) algorithms to accurately align images while avoiding outliers. By collectively analyzing the entire set of images and clustering them based on their pixel-pair connections, the method ensures robust transformations across the dataset. The methodology is applied to Sentinel-2 and Landsat images across four coastal sites (Duck, Narrabeen, Torrey Pines, and Truc Vert) from January 2020 to December 2023. The results show that the proposed approach effectively reduces the errors from ∼1 to at least $0.4$ px (although they are likely below $0.2$ px). This approach can enhance the precision of existing algorithms for coastal feature tracking, such as shoreline detection, and aids in differentiating georeferencing errors from the actual impacts of storms or beach nourishment activities. The tool can also handle complex cases of significant image rotation due to varied projections. The findings emphasize the importance of co-registration for reliable shoreline monitoring, with potential applications in coastal management and climate change impact studies.

Accurate and consistent mapping of the boundary between land and water (the ‘waterline’) is critical for tracking coastal change and coastal management. Earth Observation satellite remote sensing provides a unique cost-effective alternative to traditional methods. Waterlines from satellites are often derived by methods based on spectral indices that lead to the separation between land and water. The validation strategy for these products requires a complex approach from accuracy assessment (quantifying error) to verification of its suitability for monitoring applications. Traditionally the accuracy of EO products is reduced and simplified to the resolution of the sensor or satellite that collects the data. However, environmental variables (sea conditions, weather, vegetation, anthropic) that may have a direct effect on the sensor and on the coastline that we are trying to monitor are not taken into consideration. Segments of Sentinel-2-derived waterlines were selected in North Bull Island for further analysis in the creation of a new benchmark dataset for understanding the waterline models of eastern Ireland. In our novel approach, we propose that horizontal accuracy assessment is performed by using the mean absolute distance between the GNSS reference line and the Sentinel-2-derived waterline. The vertical accuracy assessment was then calculated by the difference between the attributed waterline height compared with the mean GNSS elevation at the intersection points. Results were then compared with Dublin Port tide gauge height record. The development of reference validation models can allow more efficient application of satellite data for monitoring, and understanding how environmental variables affect each case study.

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.

Coastal change assessments have significant socioeconomic, environmental, and infrastructure implications due to the extensive impacts of climate change, such as rising sea levels, the increasing frequency and intensity of storms, as well as the consequences of human intervention. Satellite products have been used to monitor the coast at relatively high resolution (30 m) since the 1970s through the Landsat program. However, the arrival of the EU Copernicus/Sentinels in 2014 introduced a marked increase in coastal applications thanks to the improved spatial and temporal resolutions. The research presented in this study explores the derivation of waterlines from Sentinel 2 imagery and the creation of a novel holistic approach to a validation framework. Specifically, this study aims to: a) explore the inherent waterline errors against reference datasets and begin to establish the overall uncertainty in deriving waterlines from optical satellite imagery; and b) assess the potential of these results and their suitability for coastal change applications. The results indicate an average positional error of approximately 4 meters for Sentinel images in coastal regions by evaluating the Sentinel-2 satellite images with distinct features visible in aerial orthophotography. Subsequently, the horizontal and vertical inaccuracies of the satellite-derived waterlines (SDWL) were further determined by using a GNSS line as a reference dataset. The horizontal assessment was conducted by calculating the average distance between the SDWLs and the GNSS reference lines across eighteen Sentinel-2 images corresponding to the years 2021, 2022, 2023 and 2024. These were analysed, showing a median displacement of 15 meters, and indicating an offshore trend for the satellite-derived waterlines. The vertical assessment, or height error, was computed by comparing the average height of SDWLs (as determined by the average tide gauge heights) with the reference dataset height (as measured by GNSS), resulting in a mean absolute error of 6 cm. The vertical results indicate that the SDWLs’ heights, as measured by the local tide gauges, align well with in situ local height measurements. The results of this study will aid in identifying temporal and spatial scales and resolutions at which Earth Observation products are suitable for coastal management. The initial stages of a validation framework are presented to assess the quality and applicability of satellite-derived waterlines for coastal change monitoring based on specific user requirements. Identifying the sources of error and improving uncertainty models for satellite-derived products enables better decision-making in coastal management. These analyses will demonstrate whether the outcomes remain consistent among satellite images or change according to local environmental conditions. Increasing end-user confidence in the rates of change obtained from available satellite products can provide crucial information in study areas, and at space-time resolutions previously unattainable.