A novel significant wave height estimation method combining synthetic aperture radar observations with ERA5 reanalysis data

Due to the scarcity of high-quality training data and the limitations of traditional feature extraction methods, significant challenges exist in applying machine learning approaches to marine significant wave height (SWH) inversion using synthetic aperture radar (SAR). To this end, this paper proposes a novel method to construct a training dataset using Sentinel-1 SAR images over the Atlantic region in 2024, together with ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). A total of 12,853 paired datasets of Sentinel-1 and ERA5 were generated through spatiotemporal matching. These data were separated into training, validation, and testing datasets to develop a deep learning model named WaveFusionNet, incorporating residual structure and attention mechanism. The results show that the model inversion accuracy in the testing dataset reaches root-mean-square error (RMSE) 0.61 m, standard deviation (STD) 0.59 m, scatter index (SI) 0.28. In addition, validation at five independent buoy locations confirms high agreement between model predictions and buoy measurements, with a 0.54 m RMSE and a 0.51 m STD. It proves the feasibility of constructing the training dataset from ERA5 data and provides a new idea for the inversion of SAR ocean parameters.