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This study represents the first attempt to map the sediment thickness spatial distribution along the Andalusian coastal zone by integrating various publicly available datasets. While prior studies have presented bedform- and sediment-type syntheses, none have attempted to quantify sediment thickness at the scale and resolution performed in this study. The study area has been divided into 18 physiographic zones, and we have used BGS Groundhog Desktop v2.6 software for 3D modeling and sediment thickness model calculations. We present here the modeling workflow, model results, and the challenges that we have encountered, including discrepancies in geological maps, difficulty managing data input for grain size/consolidation, and the need for additional geological information. We have compared the modeled sediment fractions of the unconsolidated material with 4194 seabed samples distributed along the study area and found that the differences between the modeled versus the sampled emphasized the importance of incorporating river contributions, particularly from the Guadalquivir River, into the model for more accurate results. The model intermediate and final outputs and the software routines used to query the sediment thickness model are provided as publicly accessible datasets and tools. The modeled sediment thickness could contribute to making quantitative predictions of morphological change at a scale that is relevant to longer-term strategic coastal management in Andalusia. The methodology and tools used for this study are transferable to any study area.

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.