With high anthropogenic pressure and the effects of climate change (e.g., sea level rise) on coastal regions, there is a greater need for accurate and up-to-date information about the topography of these systems. Reliable topography and bathymetry information are fundamental parameters for modelling the morpho-hydrodynamics of coastal areas, for flood forecasting, and for coastal management. Traditional methods such as ground, ship-borne, and airborne surveys suffer from limited spatial coverage and temporal sampling due to logistical constraints and high costs which limit their ability to provide the needed information. The recent advancements of spaceborne remote sensing techniques, along with their ability to acquire data over large spatial areas and to provide high frequency temporal monitoring, has made them very attractive for topography and bathymetry mapping. In this review, we present an overview of the current state of spaceborne-based remote sensing techniques used to estimate the topography and bathymetry of beaches, intertidal, and nearshore areas. We also provide some insights about the potential of these techniques when using data provided by new and future satellite missions.

This article proposes a new methodological approach to measure and map the size of coarse clasts on a land surface from photographs. This method is based on the use of the Mask Regional Convolutional Neural Network (R-CNN) deep learning algorithm, which allows the instance segmentation of objects after an initial training on manually labeled data. The algorithm is capable of identifying and classifying objects present in an image at the pixel scale, without human intervention, in a matter of seconds. This work demonstrates that it is possible to train the model to detect non-overlapping coarse sediments on scaled images, in order to extract their individual size and morphological characteristics with high efficiency (R² = 0.98; Root Mean Square Error (RMSE) = 3.9 mm). It is then possible to measure element size profiles over a sedimentary body, as it was done on the pebble beach of Etretat (Normandy, France) in order to monitor the granulometric spatial variability before and after a storm. Applied at a larger scale using Unmanned Aerial Vehicle (UAV) derived ortho-images, the method allows the accurate characterization and high-resolution mapping of the surface coarse sediment size, as it was performed on the two pebble beaches of Etretat (D50 = 5.99 cm) and Hautot-sur-Mer (D50 = 7.44 cm) (Normandy, France). Validation results show a very satisfying overall representativity (R² = 0.45 and 0.75; RMSE = 6.8 mm and 9.3 mm at Etretat and Hautot-sur-Mer, respectively), while the method remains fast, easy to apply and low-cost, although the method remains limited by the image resolution (objects need to be longer than 4 cm), and could still be improved in several ways, for instance by adding more manually labeled data to the training dataset, and by considering more accurate methods than the ellipse fitting for measuring the particle sizes.

Des changements climatiques importants affectent une grande partie de notre planète. Les différents modèles prévoient ainsi une hausse probable des précipitations et des températures dans plusieurs régions du monde. Le présent travail analyse l'évolution des températures et des pluies enregistrées sur le littoral algérien pendant plus de quarante années. Les évolutions de ces deux paramètres témoignent des bouleversements climatiques subis ces dernières années par l'Afrique du Nord et le Grand Maghreb.

Radar altimetry was initially designed to measure the marine geoid. Thanks to the improvement in the orbit determination from the meter to the centimeter level, this technique has been providing accurate measurements of the sea surface topography over the open ocean since the launch of Topex/Poseidon in 1992. In spite of a decrease in the performance over land and coastal areas, it is now commonly used over these surfaces. This study presents a semi-automatic method that allows us to discriminate between acquisitions performed at high tides and low tides. The performances of four radar altimetry missions (ERS-2, ENVISAT, SARAL, and CryoSat-2) were analyzed for the retrieval of sea surface height and, for the very first time, of the intertidal zone topography in a coastal lagoon. The study area is the Arcachon Bay located in the Bay of Biscay. The sea level variability of the Arcachon Bay is characterized by a standard deviation of 1.05 m for the records used in this study (2001–2017). Sea surface heights are very well retrieved for SARAL (R~0.99 and RMSE < 0.23 m) and CryoSat-2 (R > 0.93 and RMSE < 0.42 m) missions but also for ENVISAT (R > 0.82 but with a higher RMSE >0.92 m). For the topography of the intertidal zone, very good estimates were also obtained using SARAL (R~0.71) and CryoSat-2 (R~0.79) with RMSE lower than 0.44 m for both missions.

The aim of this study is to understand the inter-annual hydrological variability (precipitation and streamflow) in the basins of the High Atlas in Morocco and to determine how climate fluctuations (represented by the North Atlantic Oscillation (NAO) climate index) are expressed in the hydrological system. To reach this objective, time series of precipitation and streamflow are processed as standardized anomalies and studied by continuous wavelet analysis and wavelet coherence analysis, which are particularly suitable for the study of unsteady processes. Wet and dry periods vary from one basin to another between three and five years. The wavelet analysis shows the existence of many bands of energy in most of the sub-basins, from annual to inter-annual scales regarding the precipitation and streamflow time series. These bands correspond to intervals of one year, 2–4 years, 4–8 years and 8–12 years. The wavelet coherence analysis shows a strong coherence between NAO/streamflow and precipitation/NAO identified at the inter-annual scale. Non-stationarity can be observed in the late 1980s, 1990s and 2000s. The contribution of the NAO is different from one basin to another ranging between 67% and 77%.

The future mission of surface water and ocean topography (SWOT), launched in 2020 over a period of 3–5 years, will be designated to address the issue of combining surface water hydrology with physical oceanography aiming to present new perspectives of applications for coastal areas. The extent to which the synthetic SWOT measurements can reproduce the temporal variability of the sea level was investigated. The eastern English Channel (NW France) was considered as a case of application. The hourly sea-level records were filtered from the aliased harmonic tides by classic harmonic analyses to obtain the nontidal residual. This residual was used to simulate synthetically the satellite samples based on the number of overpasses per repeat cycle at each geographical station. Both real and synthetic SWOT measurements were compared by the use of different approaches of inference statistics and wavelets. The statistical behavior, deduced from the functions of probability density (pdf) and cumulative distribution (cdf), shows correlations between 65% and 75% for hourly measurements, which increase to 85% for monthly average ones. The frequency of positive and negative extreme values is under-estimated with an order less than 25%. The potential use of SWOT depends on the number of measurements and the sampling interval between SWOT overpasses per repeat orbit. In the time–frequency domain, the wavelet multiresolution analysis of the nontidal sea level displays four components: 1) 1 year; 2) $\sim$ 4–7 month; 3) $\sim$ 2–3 month; and 4) $<2$ month bands. Such modes seem to be well manifested by SWOT samples with a mean explained variance more than 75%. The aliasing frequency of the altimeter generates a dispersion and an overexpression of the energy spectrum, which increases with the number of overpasses per repeat orbit and the high frequency ( $\sim $ 2–3 and $<2$ month bands). The reconstructed wavelet components evidence the capacity of SWOT to estimate the annual and the seasonal variability of the nontidal sea level. In particular, SWOT is able to reproduce the most of extreme storm surges in the English Channel. The main finding of this research clearly shows the utility of SWOT satellite altimetry in observing and understanding the sea-level variability and storm surges, complementing tide-gauge observations for the validation and improvement of coastal models.