Modeling the SAR altimetry noise

Abstract

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.