Work

Physics-Aware Hybrid CNN–Transformer Network for GNSS-R Sea Surface Wind Speed Estimation

Year: 2026

Type: article

Abstract: Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for global ocean wind monitoring with high temporal resolution. However, accurate wind speed retrieval remains challenging due to the complex scattering mechanisms and the nonlinear coupling between delay–Doppler maps (DDMs) and observation geometries. To address these limitations, a Physics-Aware Hybrid CNN–Transformer Network (PA-HCTN) is proposed accordingly. The model integrates a CNN for local DDM feature extraction, a Transformer encoder for global context modeling, and a cross-attention module to dynamically fuse auxiliary physical parameters. A geophysical model function (GMF)-constrained loss is incorporated to enhance physical consistency. Evaluated on CYGNSS and ERA5 data, the PA-HCTN achieves an RMSE of 1.35 m/s and an R2 of 0.75, outperforming existing benchmarks and significantly mitigating high-wind-speed underestimation. In addition, through independent validation using NDBC buoy data from four sites, the results demonstrate the effectiveness of the hybrid architecture and physics-aware design for GNSS-R wind retrieval.

Source: Remote Sensing

DOI: 10.3390/rs18071053

Authors: Baiwei An, Weiwei Qin, Weijie Kang, Li Zhang, Hao Chi

Institutions: PLA Rocket Force University of Engineering, Baoji University of Arts and Sciences

Language: English

Topic: Ocean Waves and Remote Sensing

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,258