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.

The Dutch, German, and Danish Wadden Sea is one of the largest tidal flats in the world which has been left in a nearly natural state. Several studies generated the topography of this valuable area, but presented only single maps, which covered partial regions of the Wadden Sea. The objectives of this paper are 1) to use the waterline method to analyze SAR images in order to generate annual topographic maps of the northern parts of the German Wadden Sea for the years 1996-1999 and 2006-2009 and 2) to quantitatively estimate and monitor the development of the tidal flats. The waterlines are detected by a wavelet-based edge detection algorithm, geocoded in Gauss-Krueger coordinates, assigned with water level information, and interpolated into topographic maps. Our results show the development of the tidal flats over the 8 years and illustrate three of the most changing regions: Tertiussand, Gelbsand, and Medemsand. Their area, shape, and sediment volume have changed significantly due to the tide, wave, wind, and river discharge influences. The waterline method is an efficient and economical way to monitor large tidal flat areas, which allows an estimation of long-term morphological development. One potential application is to assimilate the results into morphodynamic models in order to give finer morphological predictions.

FY-3E WindRAD (Fengyun-3E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The diversity of the radar view geometry varies across the swaths, with maximum diversity in the sweet swaths and limited diversity in the outer and nadir swaths. When NOC backscatter calibration coefficients are computed as a function of incidence angle only (NOCint), a smooth correction is found. However, when relative antenna azimuth angle is included (NOCant), it appears that the corrections as a function of relative azimuth angle vary harmonically and substantially for a specific incidence angle. NOCant corrections yield a better fit of the measurements to the GMF (Geophysical Model Function). Hence, NOCant is applied for the analysis of wind retrieval from the Ku-band and C-band. An extra engineering correction of 0.15 dB and 0.20 dB is applied on Ku-band and C-band backscatter values, respectively, to reduce the wind speed bias without increasing the standard deviation. Overall, NOCant is the best option for both channels. In addition, the instrument backscatter data stability over time is good, and the retrieved winds can fulfill operational requirements.

Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT (China France Oceanography SATellite), WindRad (Chinese Wind Radar) on FY-3E, and SeaWinds on QuikSCAT. Besides the comparison of the so-called first rank solution retrieval skill of the input wind field, other figures of merit (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the wind vector cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir but also to investigate resolution and accuracy through incorporating and analyzing the spatial response functions of the simulated Level-1B data for each WVC.

The rotating fan‐beam architecture wind scatterometer Chinese‐French Oceanography Satellite (CSCAT) onboard the Chinese‐French Oceanography Satellite leads to varying geometries and varying wind retrieval performance across the swath. To assess this, the wind vector cells (WVCs) are classified into three groups: outer, sweet, and nadir. The sweet swath WVCs contain the most diverse geometries, which give the best wind retrieval performance, whereas on the opposite the outer swath WVCs have the least diverse geometries, which makes the wind retrieval and ambiguity removal more challenging. NWP Ocean Calibration (NOC) is used to calibrate the radar backscatter prior to the wind inversion. It computes the difference between measured backscatter and simulated backscatter from collocated NWP winds through the geophysical model function. The difference is applied as a correction. An improved NOC method, NOC as a function of incidence angle and azimuth angle (NOCant) is introduced, which takes the orientation of the antenna into account. This method is compared with the widely used NOC as a function of incidence angle only (NOCinc). It shows a better fit with the geophysical model function, except for the outer swath WVCs which have limited geometry diversity. It also corrects the asymmetric wind direction bias distribution across the swath and reduced the nadir swath relative wind direction bias which is caused by the poor azimuth diversity. In conclusion, the rotating fan‐beam architecture of CSCAT leads to unique and varying data characteristics across the swath. Overall, the performance of the proposed NOCant correction is better than NOCinc and improves the wind statistics.

This paper describes the adaptation of the Bayesian sea ice detection algorithm for the rotating fan-beam scatterometer CSCAT onboard the China–France Oceanography Satellite (CFOSAT). The algorithm was originally developed and applied for fixed fan-beam and rotating pencil-beam scatterometers. It is based on the probability of the wind and ice backscatter distances from the measurements to their corresponding geophysical model functions (GMFs). The new rotating Ku-band fan-beam design introduces very diverse geometry distributions across the swath, which leads to three main adaptations of the algorithm: (1) a new probability distribution function fit for the backscatter distances over open sea; (2) a linear ice GMF as a function of incidence angle; (3) the separation of outer swath wind vector cells ((WVCs) number 1, 2, 41, 42) from the other WVCs to form two sets of probability distribution function fits for these two WVC groups. The results are validated against sea ice extents from the active microwave ASCAT and the passive microwave SSMI. The validation shows good agreement with both instruments, despite the discrepancies with SSMI during the melting season, and this discrepancy is caused by the lower sensitivity of the passive microwave to detect the ice at a low concentration with a mixed water/ice state, while the scatterometer is more tolerant regarding this situation. We observed that the sea-ice GMF regression between HH and VV sea-ice backscatter at low and high incidence angles decorrelates at around −12 dB (28) and −20 dB (50) and an experiment with truncated backscatter values at these incidence angles is executed, which significantly improves the year-long average sea ice extents. In conclusion, the adapted algorithm for CSCAT works effectively and yields consistent sea ice extents compared with active and passive microwave instruments. As such, it can, in principle, contribute to the long-term global scatterometer sea ice record, and as the algorithm was adapted for a rotating fan-beam scatterometer, it also can serve as a guideline for the recently launched, dual-frequency, rotating fan-beam scatterometer WindRAD.