Significant Wave Height (SWH) is a crucial parameter in oceanographic research, essential for understanding various marine and atmospheric processes.Traditional methods for obtaining SWH, such as ship-based and buoy measurements, face limitations like limited spatial Rotating Cover coverage and high operational costs.With the advancement of Global Navigation Satellite Systems reflectometry (GNSS-R) technology, a new method for retrieving SWH has emerged, demonstrating promising results.This study utilizes Radio occultation sounder (GNOS) data from the FY-3E satellite and incorporates the latest Vision Transformer (ViT) technology to investigate GNSS-R-based SWH retrieval.We designed and evaluated various deep learning models, including ANN-Wave, CNN-Wave, Hybrid-Wave, Trans-Wave, and ViT-Wave.
Through comparative training using ERA5 data, the ViT-Wave model was identified as the optimal retrieval model.The ViT-Wave model achieved a Root Mean Square Error (RMSE) accuracy of 0.4052 m and Mean Absolute Error (MAE) accuracy of 0.2700 m, significantly outperforming both traditional methods and Course a pied - Homme - Vetements - Chandail - Long sleeves brosse newer deep learning approaches utilizing Cyclone Global Navigation Satellite Systems (CYGNSS) data.These results underscore the potential of integrating GNSS-R technology with advanced deep-learning models to enhance SWH retrieval accuracy and reliability in oceanographic research.