Disentangling Currents and Waves

Abstract

Surface velocities measured by Synthetic Aperture Radar (SAR) contain contributions from both mean surface motion—referred to as total surface current (TSC)—and the often more prominent sea-state-induced wave motions. Most modern SAR systems cannot distinguish between these two phenomena, which has stifled TSC retrieval from SAR data for decades. We propose a new framework to separate TSC and wave-motion components by leveraging polarization diversity, exploiting the tendency of each phenomenon to imprint distinct signatures on orthogonal polarizations. Building on a foundational signal model, we derive four source-separation algorithms. To address the model’s theoretical limitations, we introduce empirical extensions via symbolic regression, guided by varying levels of theoretical insight. The developed algorithms are evaluated using simulated C-band SAR data, and benchmarked against a reference geophysical model function (GMF) implementation. Our methods demonstrate comparable overall performance, with errors on the order of O(0.1ms⁻¹), and notably outperform the GMF in resolving kilometer-scale spatial features—a domain where traditional GMFs generally struggle. Preliminary results obtained on TanDEM-X observations confirm the generalizability of our approach. These findings highlight the potential of future SAR missions with polarimetric capabilities, such as Harmony, to achieve high-resolution separation of surface-motion sources using polarization diversity.