LAUSR

Recent advancements in spaceborne global navigation satellite system (GNSS) missions have produced extensive global datasets, providing a robust basis for deep-learning (DL)-based significant wave height (SWH) retrieval. While existing DL models predominantly use cyclone GNSS (CYGNSS) data with four-channel information, they often adopt single-channel inputs or simple channel concatenation without leveraging the benefits of cross-channel information interaction during training. To address this limitation, a novel spatial–channel attention-based network, namely, SCAWaveNet, is proposed for SWH retrieval. Specifically, features from each channel of the delay-Doppler maps (DDMs) are modeled as independent attention heads, enabling the fusion of spatial and channelwise information. For auxiliary parameters (APs), a lightweight attention mechanism is designed to assign weights along the spatial and channel dimensions. The final feature integrates both spatial and channel-level characteristics. Model performance is evaluated using four-channel CYGNSS data. Quantitative and qualitative experiments were conducted on CYGNSS-ERA5 test set, SCAWaveNet achieves an average RMSE of 0.438 m. Compared with state-of-the-art models, SCAWaveNet reduces RMSE by at least 3.52%. Furthermore, evaluations on WaveWatch III (WW3), Jason-3, and National Data Buoy Center (NDBC) buoy data, as well as in wind speed (WS), rainstorm, typhoon, and noisy scenarios, further confirm the superiority of SCAWaveNet. The code is available at https://github.com/Clifx9908/SCAWaveNet