Year: 2025
Type: article
Abstract: GNSS Reflectometry (GNSS-R) technology utilizes existing navigation satellite signals as opportunistic microwave illumination sources, eliminating the need for dedicated transmitters and thereby significantly reducing hardware costs. Benefiting from the global coverage of navigation constellations, GNSS-R enables worldwide sea surface wind monitoring with superior spatiotemporal resolution. The acquired Delay-Doppler Maps (DDMs) encode sea surface roughness characteristics near the specular reflection point, which are intrinsically modulated by wind-driven capillary waves. We proposed CyGNSSMsa employs CyGNSSnet’s dual-branch framework for preliminary feature extraction from DDM and auxiliary parameters, followed by multihead self-attention (MSA) mechanisms to decipher long-range dependencies within fused feature representations. Multi-scale feature fusion is achieved through residual connections across hierarchical network layers, culminating in precise wind speed regression via multilayer perceptrons. Experimental validation demonstrates CyGNSSMsa’s performance with an RMSE of 1.345 m/s, achieving simultaneous improvements in both accuracy and systematic bias mitigation.
Authors: Weimin Chen, Bin Wang, Dongmei Song
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4411778602