This study exploits an earlier-developed analytical noise autocovariance model to evaluate two complementary post-processing strategies for noise reduction in SAR altimetry. Firstly, we develop an Optimal Filtering (OF) scheme to produce improved 20 Hz products from data at higher posting rates—and underline that an arithmetic mean is not suited for this purpose, because it leads to spurious along-track correlations. Secondly, we tailor the existing High-Frequency Adjustment (HFA) to SAR altimetry, which can readily be applied to data at a 20 Hz posting rate. In contrast to earlier works, we derive the HFA slope directly from the noise model.Both post-processing strategies are applied to ten days of unfocused SAR (UF-SAR) data from Sentinel-3 and Sentinel-6 processed at a 140 Hz posting rate. The precision gains are quantified by comparison of the 20-Hz noise levels of Sea Level Anomaly (SLA) and Significant Wave Height (SWH) estimates. The application of the HFA yields average noise reductions of 7–9% for both missions, while the OF achieves 14–17% for Sentinel-3 and 9–11% for Sentinel-6. While the OF achieves to reduce the noise floor over the whole spectral range, the HFA can only reduce the noise beyond a chosen cut-off frequency. We find that the achievable precision gains from increased posting rates are much lower than reported in earlier studies, because they applied the unsuited arithmetic mean for the compression to 20 Hz.All the improvements obtained on the data are well predicted by the noise model, so we suggest the latter as a helpful tool for future mission design and algorithm evolution. Some noteworthy conclusions from the model are (i) that optimal filtering of data at 80 Hz and 140 Hz posting rates can yield similar 20-Hz precision gains and (ii) the current UF-SAR 20-Hz noise levels in Sentinel-3 and Sentinel-6 data are far from the theoretically possible performance. Particularly in high sea states SWH > 7 m, LR-RMC processing in combination with Weighted Least Squares fitting of the waveforms can reduce the absolute 20-Hz noise levels of both SSH and SWH by more than 50% with respect to UF-SAR for both Sentinel-3 and Sentinel-6. Our results confirm that a significant fraction of the 20-Hz noise level in UFSAR is instead small scale signal variability, as it is not explained by speckle.

This study presents the first azimuth cutoff analysis in Synthetic Aperture Radar (SAR) altimetry, aiming to assess its applicability in characterizing sea-state dynamics. In SAR imaging, the azimuth cutoff serves as a proxy for the shortest waves, in terms of wavelength, that can be detected by the satellite under certain wind and wave conditions. The magnitude of this parameter is closely related to the wave orbital velocity variance, a key parameter for characterizing wind-wave systems. We exploit wave modulations exhibited in the tail of fully-focused SAR waveforms and extract the azimuth cutoff from the radar signal through the analysis of its along-track autocorrelation function. We showcase the capability of Sentinel-6A in deriving these two parameters based on analyses in the spatial and wavenumber domains, accompanied by a discussion of the limitations. We use Level-1A high-resolution Sentinel-6A data from one repeat cycle (10 days) globally to verify our findings against wave modeled data. In the spatial domain analysis, the estimation of azimuth cutoff involves fitting a Gaussian function to the along-track autocorrelation function. Results reveal pronounced dependencies on wind speed and significant wave height, factors primarily determining the magnitude of the velocity variance. In extreme sea states, the parameters are underestimated by the altimeter, while in relatively calm sea states and in the presence of swells, a substantial overestimation trend is observed. We introduce an alternative approach to extract the azimuth cutoff by identifying the fall-off wavenumber in the wavenumber domain. Results indicate effective mitigation of swell-induced errors, with some additional sensitivity to extreme sea states compared to the spatial domain approach.

In this study, the full-focusing (FF) algorithm is reviewed with the objective of optimizing it for processing data from different types of surfaces probed in altimetry. In particular, this work aims to provide a set of optimal FF processing parameters for the Sentinel-6 Michael Freilich (S6-MF) mission. The S6-MF satellite carries an advanced radar altimeter offering a wide range of potential FF-based applications which are just beginning to be explored and require prior optimization of this processing. In S6-MF, the Synthetic Aperture Radar (SAR) altimeter acquisitions are known to be aliased in the along-track direction. Depending on the target, aliasing can be tolerated or may be a severe impairment to provide the level of performance expected from FF processing. Another key aspect to consider in this optimization study is the unprecedented resolution of the FF processing, which results in a higher posting rate than the standard SAR processing. This work investigates the relationship between posting rate and noise levels and provides recommendations for optimal algorithm configurations in various scenarios, including transponder, open ocean, and specular targets like sea-ice and inland water scenes. The Omega–Kappa (WK) algorithm, which has demonstrated superior CPU efficiency compared to the back-projection (BP) algorithm, is considered for this study. But, unlike BP, it operates in the Doppler frequency domain, necessitating further precise spectral and time domain settings. Based on the results of this work, real case studies using S6-MF acquisitions are presented. We first compare S6-MF FF radargrams with Sentinel-1 (S1) images to showcase the potential of optimally configured FF processing. For highly specular surfaces such as sea-ice, distinct techniques are employed for lead signature identification. S1 relies on image-based lineic reconstruction, while S6-MF utilizes phase coherency of focalized pulses for lead detection. The study also delves into two-dimensional wave spectra derived from the amplitude modulation of image/radargrams, with a focus on a coastal example. This case is especially intriguing, as it vividly illustrates different sea states characterized by varying spectral peak positions over time.