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.

Coasts are continually changing and remote sensing from satellite has the potential to both map and monitor coastal change at multiple scales. This study aims to assess the application of shorelines extracted from Multi-Spectral Imagery (MSI) and Synthetic Aperture Radar (SAR) from publicly available satellite imagery to map and capture sub-annual to inter-annual shoreline variability. This is assessed at three macro-tidal study sites along the coastline of England, United Kingdom (UK): estuarine, soft cliff environment, and gravel pocket-beach. We have assessed the accuracy of MSI-derived lines against ground truth datum tideline data and found that the satellite derived lines have the tendency to be lower (seaward) on the Digital Elevation Model than the datum-tideline. We have also compared the metric of change derived from SAR lines differentiating between ascending and descending orbits. The spatial and temporal characteristics extracted from SAR lines via Principal Component Analysis suggested that beach rotation is captured within the SAR dataset for descending orbits but not for the ascending ones in our study area. The present study contributes to our understanding of a poorly known aspect of using coastlines derived from publicly available MSI and SAR satellite missions. It outlines a quantitative approach to assess their mapping accuracy with a new non-foreshore method. This allows the assessment of variability on the metrics of change using the Open Digital Shoreline Analysis System (ODSAS) method and to extract complex spatial and temporal information using Principal Component Analysis (PCA) that is transferable to coastline evolution assessments worldwide.

Remote sensing, and particularly Sinthetic Aperture Radar (SAR) is a potential tool to monitor the presence of plastic on the ocean surface, and AI algorithms can learn to find patterns in the satellite signals that lead to authomatic detection of plastic patches. The main challenge to train AI algorithms for this purpose is the lack of insitu ground truthdata that allow to reliably label the images. This paper describes a first pilot of binary pixel classification (plastic vs. non-plastic) in SAR imagery using Random Forest, GaussianNaïve Bayes and Support Vector Machines classifiers. It also describes the strategy used to generate ground truth data using Sentinel-1 and Sentinel-2 in the Balearic Islands.

This paper proposes an unsupervised clustering algorithm for multispectral images, which automatically determines the number of statistically distinct clusters in the image. It uses the multivariate student-t distribution as a more flexible underlying statistical model, with the Gaussian as only a special case. The algorithm shows better data modeling flexibility than the Gaussian case. Excellent and reproducible clustering results are observed for both simulated data and real data from Worldview-2 multispectral sensor.

A new methodology, based on the International Organization for Standardization (ISO) Guide to the expression of Uncertainty in Measurement (GUM), for the analysis of uncertainties in Synthetic Aperture Radar (SAR) remote sensing data is presented. The principal idea is to assess the sources of uncertainty, developing computational approaches to propagate uncertainties through the whole SAR signal processing chain. The final goal is to construct uncertainties budgets to quantify the total uncertainty of SAR products, permitting their `trace-ability' to international reference standards.