Deep Learning Merge of 2-D Wave Spectra From Real and Synthetic Aperture Radars

Currently, global directional wave spectra are routinely providing by synthetic aperture radar (SAR) on Sentinel-1 in Wave Mode and real aperture radar, i.e., SWIM onboard CFOSAT. However, SAR spectra suffer from azimuth cut-off effects, while SWIM spectra exhibit parasitic peaks and 180° ambiguity. Leveraging the complementary of these two satellite radars for directional spectra merging remains an open scientific challenge. In this letter, we propose a deeply-learned based 2D wave spectrum fusion model aiming to integrate CFOSAT SWIM and Sentinel-1A wave mode Level-2 products. After spatiotemporal collocating, and rejecting suspicious spectra, two-year period (June 2022 to June 2024) of 2D wave spectra triplets (S-1, SWIM, and ERA5) are used for the fusion model training. The deep learning merged spectra show good consistency with ERA5 benchmark regarding 2D spectra, the derived omni - directional energy distribution and the integrated wave parameters. Comparative analysis with ERA5 reanalysis demonstrates that our fused directional spectrum could effectively compensate for high-frequency information loss and spectral distortion in SAR products, meanwhile suppress SWIM's parasitic peaks and resolve directional ambiguity. Qualitatively, compared to ERA5, the Brüning’s correlation coefficient and square error of our fusion results reaches 0.95 and 0.17, significantly outperforming official SAR (0.79 and 0.58) and SWIM (0.83 and 0.52) products. The fusion mitigates SAR’s high-frequency losses and SWIM’s artifacts, enhancing ocean wave spectrum accuracy for improved geophysical applications.