Improving the accuracy of satellite-based GNSS-R sea surface height inversion based on a novel fused self-attention mechanism and multimodal deep learning model for residual networks

Sea surface height (SSH) is an important parameter in oceanographic studies and is crucial for understanding oceanic and atmospheric processes. Traditional physical models simply compress two-dimensional delay-Doppler map (DDM) data into a single scalar message, resulting in the loss of critical information and the need for error modeling for correction. First, utilizing the reflected signals from the BeiDou Navigation Satellite System (BDS) provided by the FY-3E GNSS Radio Occultation Sounder-II (GNOS-II), a novel multimodal deep learning model integrating self-attention mechanisms and residual networks—termed Vision Transformer Residual Network Multimodal Deep Learning (ViTResNetMDL)—is proposed to retrieve sea surface height. ViTResNetMDL captures the global spatial features in the effective scattering area DDM through the self-attention mechanism in the Transformer module, while extracting the local detail features of the original DDM using the residual structure and combining with the auxiliary parameters for the multimodal data fusion, and finally inverts the SSH through four fully connected layers. Second, to validate the proposed improvements of the model, the inversion results are corrected by cumulative distribution function (CDF) matching, and the DTU18 Global Sea Surface Height Validation Model data, which has been corrected by the DTU23 Global Ocean Tide Model, is used as a reference to conduct extensive tests, and the results show that the SSH inversion results of the ViTResNetMDL model have a root mean square error (RMSE) of 0.74 m, a mean absolute error (MAE) of 0.48 m, and a coefficient of determination (R2) of 1.00. Third, compared with the inversion results based on the derivative polar tracking method, the RMSE, MAE, and R2 of the ViTResNetMDL model inversion results are improved by 80.9%, 80.1%, and 16.3%, respectively. The ViTResNetMDL model will provide a new theoretical and methodological reference for the means of inversion for GNSS-R altimetry.