Surface winds (10 m equivalent neutral wind velocity) from scatterometer missions since 1992 to present require homogenization to meet the requirements for oceanic and atmospheric climate data records. Sources of differences between winds retrieved from different scatterometer measurements mainly arise from calibration/validation procedures used for each scatterometer and differences in measurement physics. In this study, we focus on the calibration/validation component of the European Remote Sensing Satellite (ERS)‐1 and ERS‐2 wind speed biases. ERS‐1 and ERS‐2 data, named as WNF products, are from the Institut Français de Recherche pour l'Exploitation de la MER (IFREMER). In addition to WNF data, the newly calibrated ERS‐2 products provided by the European Space Agency (ESA), indicated as ASPS2.0 products, are also used. Our approach utilizes collocated satellite‐buoy data. Expected values of the normalized radar cross section (NRCS) are calculated from buoy winds for each antenna beam using the Cmod5.n geophysical model function. The comparisons between expected and measured NRCS examine differences along with variables such as backscatter coefficient and incidence angle ranges. The difference between the expected and measured NRCS is then used to set up empirical models aiming at the correction for biases in ERS‐1 and ERS‐2 WNF NRCS calibrations. Finally, ERS‐1 and ERS‐2 wind retrievals are reprocessed using the corrected NRCS and Cmod5.n. These earlier corrected ERS‐1/2 winds are analysed along with later scatterometer data (QuikSCAT and ASCAT‐A) for their deviations from in situ buoy winds during 1992–2011 period. The scatterometer data homogeneity is also investigated at global scales based on the use of collocated scatterometer retrievals and atmospheric re‐analyses winds derived from ERA Interim and CFSR models.

One of the challenges in ocean surface current retrieval from synthetic aperture radar (SAR) data is the estimation and removal of the wave-induced Doppler centroid (DC). This article demonstrates empirically the relationship between the dc derived from spaceborne X-band InSAR data and the ocean surface wind and waves. In this study, we analyzed over 300 TanDEM-X image pairs. It is found that the general characteristics of the estimated dc follow the theoretically expected variation with incidence angle, wind speed, and wind direction. An empirical geophysical model function (GMF) is fit to the estimated dc and compared to existing models and previous experiments. Our GMF is in good agreement (within 0.2 m/s) with other models and data sets. It is found that the wind-induced Doppler velocity contributes to the total Doppler velocity with about 15% of the radial wind speed. This is much larger than the sum of the contributions from the Bragg waves (~0.2 m/s) and the wind-induced drift current (~3% of wind speed). This indicates a significant (dominant) contribution of the long wind waves to the SAR dc. Moreover, analysis of dual-polarized data shows that the backscatter polarization ratio ( $PR=\sigma ^{0}_{VV}/\sigma ^{0}_{HH}$ ) and the dc polarization difference ( $PD={|dc_{VV}|-|dc_{HH}|}$ ) are systematically larger than 1 and smaller than 0 Hz, respectively, and both increase in magnitude with incidence angle. The estimated PR and PD are compared to other theoretical and empirical models. The Bragg scattering theory-based (pure Bragg and composite surface) models overestimate both PR and PD, suggesting that other scattering mechanisms, e.g., wave breaking, are involved. In general, it is found that empirical models are more consistent with both backscatter and Doppler data than theory-based models. This motivates a further improvement of SAR dc GMFs.

This article presents the first direct comparison of the sea surface radial velocity (RVL) derived from the two satellite SAR systems Sentinel-1 and TanDEM-X, operating at different frequencies and imaging modes. The RVL is derived from the Doppler centroid (Dc) provided in the Sentinel-1 OCN product and from the along-track interferometric phase of the TanDEM-X. The comparison is carried out using unique opportunistic acquisitions, collocated in space and time, over three different sites located in the Iceland Sea, the Pentland Firth, and the Kattegat Sea. First, it is observed that the RVL derived from both satellites is biased, thus calibration is applied using the land as a reference. The comparison shows that the correlation and the mean bias between the two datasets depend on the differences in acquisition time, incidence angle, and azimuth angle, and on wind and surface velocities. It is found that, given a time difference of $\lesssim$ 20 min, the spatial correlation coefficient is relatively high (between 0.70 and 0.93), which indicates that the two SAR systems observe similar sea surface current fields. The spatial correlation degrades primarily due to increasing time difference and decreasing velocity magnitudes. It is also found that the mean RVL bias increases primarily with the radial wind speed, which suggests that the bias is mainly due to the wave-induced Doppler shift. This article shows that under certain conditions, i.e., similar acquisition geometry and short time delay, a good agreement between the two independently derived RVL is achieved, both in the spatial variation and absolute mean value. This encourages a synergistic use of the sea surface velocity estimated from different C- and X-band SAR systems.

In this paper, the capability of Sentinel-1 data to map ocean surface currents in the Skagerrak Sea, with a focus on the Norwegian Coastal Current (NCC), is investigated. Post-processing methods for removing artefacts in the data and improving of the geophysical interpretation are suggested. Scalloping is one major artefact, which significantly degrades the quality of the velocity maps. Two methods, in spatial and spectral domain, for correcting this effect are proposed. It is also found that the radial velocity provided in the Sentinel-1 ocean data is biased, hence land is used as a reference to correct for the absolute and inter-beam bias. Finally, the retrieved (corrected) velocity is compared to a regional ocean circulation model (ROMS). It is shown that there is a good agreement between the ocean model and the retrieved velocity with values of ≈ 0.8 m/s in the core of the NCC.

It has been demonstrated that Along Track Interferometric (ATI) SAR is a useful tool to retrieve ocean surface currents. The ATI SAR provides an interferometric phase (hereafter called phase) which is directly related to the Line-Of-Sight (LOS) component of the surface velocity. The accuracy of ocean currents retrieval is highly dependent on the phase processing. For instance, a properly processed phase must equal zero over static targets. The measured TanDEM-X phase rarely (if ever) satisfies this condition which indicates a phase offset independent of the surface properties. The offset can be either due to a phase synchronization issue or to using inaccurate orbital and attitude information in the processing. The objective of phase calibration is the estimation and the removal of the offset and possible trends from the measured phase. In this paper, the topographic phase is simulated using a Digital Elevation Model (DEM) and baseline information. The calibration is carried out by estimating the average of the phase over land, after topography correction, and subtracting the estimated value from the measured phase. Finally, the residual phase trend is removed using a second order polynomial fitting.