Ghana's coastline has been facing erosion and sedimentation phenomena for several decades, resulting in a serious threat to life and property considering that major urban settlements are located on the coast. In this region, there has been a lack of emphasis on comprehensive, large-scale investigations into coastal changes: prior research has predominantly centered on site-specific assessments. These studies have revealed alarming erosion rates, with reports indicating that nearly ten meters are lost annually. The use of high-resolution remotely sensed data can be a consistent support in regions where physical or economic obstacles interfere with collecting in situ information. In particular, the use of continuous all-weather SAR data may facilitate the evaluation of erosion and sedimentation phenomena in coastal areas. In this paper, we apply SAR data over a time period between 2017 and 2021. Sentinel-1 data are pre-processed using the Google Earth Engine platform, and a dedicated algorithm is then applied to identify and quantify erosion and sedimentation processes. Optical images are used as a reference for detecting the location of two areas where consistent sedimentation and erosion phenomena occurred in the considered four years. The results demonstrate that SAR backscattering variations over time offer a reliable method for monitoring coastal changes. This approach enables the identification of the type of phenomena occurring - sedimentation or erosion -, and allows for the quantification of their intensity and dimensions over time. The method can be worldwide applied once the appropriate thresholds are evaluated and help in predictive studies and environmental planning.

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Despite in-situ observations of perennial firn aquifers (PFAs) at specific locations of the Antarctic ice sheet, a comprehensive continent-wide mapping of PFA distribution is currently lacking. We present an estimate of their distribution across Antarctica in the form of a probability assessment using a Monte Carlo technique. Our approach involves a novel methodology that combines observations from Sentinel-1 and Advanced SCATterometer (ASCAT) with output from a regional climate model. To evaluate our method, we conduct an extensive comparison with Operation Ice Bridge observations from the Greenland Ice Sheet. Application to Antarctica reveals high PFA probabilities in the Antarctic Peninsula (AP), particularly along its northern, northwestern, and western coastlines, as well as on the Wilkins, Müller, and George VI ice shelves. Outside the AP, PFA probability is low, except for some locations with marginally higher probabilities, such as on the Abbot, Totten, and Shackleton ice shelves.

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In recent years, our knowledge of coastal environments has been enriched by remotely sensed data. In this research, we co-analyse two sensor systems: Terrestrial Laser Scanning (TLS) and satellite-based Synthetic Aperture Radar (SAR). To successfully extract information from a combination of different sensors systems, it should be understood how these interact with the common environment. TLS provides high-spatiotemporal-resolution information, but it has high economic costs and limited field of view. SAR systems, despite their lower resolution, provide complete, repeated, and frequent coverage. Moreover, Sentinel-1 SAR images are freely available. In the present work, Permanent terrestrial Laser Scanning (PLS) data, collected in Noordwijk (The Netherlands), are compared with simultaneous Sentinel-1 SAR images to investigate their combined use on coastal environments: knowing the relationship between SAR and PLS data, the SAR dataset could be correlated to beach characteristics. Meteorological and surface roughness have also been taken into consideration in the evaluation of the correlation between PLS and SAR data. A generally positive linear correlation factor up to 0.5 exists between PLS and SAR data. This correlation occurs for low- or moderate-wind-speed conditions, whilst no particular correlation has been highlighted for high wind intensity. Furthermore, a dependence of the linear correlation on the wind direction has been detected. @en

Anthropogenic activities and climate change in coastal areas require continuous monitoring for a better understanding of environmental evolution and for the implementation of protection strate-gies. Surface moisture is one of the important drivers of coastal variability because it highly affects shoreward sand transport via aeolian processes. Several methods have been explored for measuring surface moisture at different spatiotemporal resolutions, and in recent years, light detection and ranging (LiDAR) technology has been investigated as a remote sensing tool for high-spatiotemporal-resolution moisture detection. The aim of the present study is the assessment of the performance of a permanent terrestrial laser scanner (TLS) with an original setting located on a high position and hourly scanning of a wide beach area stretching from a swash zone to the base of a dune in order to evaluate the soil moisture at a high spatiotemporal resolution. The reflectance of a Riegl-VZ2000 located in Noordwijk on the Dutch coast was used to assess a new calibration curve that allows the estimation of soil moisture. Three days of surveys were conducted to collect ground-truth soil moisture measurements with a time-domain reflectometry (TDR) sensor at 4 cm depth. Each in situ measurement was matched with the closest reflectance measurement provided by the TLS; the data were interpolated using a non-linear least squares method. A calibration curve that allowed the estimation of the soil moisture in the range of 0–30% was assessed; it presented a root-mean-square error (RMSE) of 4.3% and a coefficient of determination (R-square) of 0.86. As an innovative aspect, the calibration curve was tested under different circumstances, including weather conditions and tidal levels. Moreover, the TDR data collected during an independent survey were used to vali-date the assessed curve. The results show that the permanent TLS is a highly suitable technique for accurately evaluating the surface moisture variations on a wide sandy beach area with a high spatiotemporal resolution.

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Environmental effects and climate change are lately representing an increasing strain on coastal areas, whose topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. Continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, and tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relationship with precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content.@en