Attention enhanced ResNet for ocean surface wind speed retrieval using CYGNSS observables

Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a pivotal technique for ocean surface wind speed retrieval; however, establishing robust multi-parameter retrieval models remains challenging due to the nonlinear relationships between GNSS-R observables and geophysical variables. An Attention-enhanced Residual Network (Att-ResNet) is proposed to address this challenge, leveraging Cyclone Global Navigation Satellite System (CYGNSS) bistatic radar data for wind speed estimation. The CYGNSS datasets were processed to extract multi-parameter observables, including Delay-Doppler Maps (DDMs), normalized bistatic radar cross-section (NBRCS), and incidence angle, which served as inputs for training wind speed retrieval models using diverse backbone architectures (e.g., ResNet and AlexNet). Ablation experiments employing the Att-ResNet framework were systematically conducted, with ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis 5) and CCMP (Cross-Calibrated Multi-Platform) wind products providing benchmark validation. Comparative analysis revealed that the Att-ResNet-retrieved wind speeds exhibited strong spatiotemporal consistency with ERA5 and CCMP data. Quantitative evaluations showed root mean square errors (RMSEs) of 1.379 m/s (ERA5) and 1.390 m/s (CCMP), with minimal biases (-0.069 m/s and -0.014 m/s, respectively) and unbiased RMSEs (ubRMSEs) of 1.377 m/s and 1.390 m/s. The study demonstrates that the Att-ResNet architecture, through its attention-driven feature selection and residual learning mechanisms, significantly enhances spaceborne GNSS-R wind retrieval accuracy. This artificial intelligence-driven framework establishes a new paradigm for high-resolution spatiotemporal ocean surface wind monitoring, demonstrating the transformative potential of deep learning in advancing GNSS-R applications.