Global navigation satellite system-reflectometry (GNSS-R), as an emerging observation method, has recently been applied to the retrieval of significant wave height (SWH). Existing studies typically use extracting features from delay Doppler maps (DDMs) to construct empirical geophysical model functions (GMFs) for SWH retrieval. However, using multiple variable parameters as model inputs poses significant challenges. Therefore, this article proposes a deep learning network model (named GloWH-Net) that integrates spaceborne GNSS-R data and multivariate parameters to invert global sea surface SWH. To verify the performance of the proposed model, ERA5, Wavewatch Ⅲ (WW3), and AVISO SWH data were used as reference data for extensive testing to evaluate the SWH retrieval performance of the GloWH-Net model and previous models (i.e. empirical and machine learning models). The results showed that when ERA5, WW3, and AVISO SWH were used as reference data respectively, the root mean square error (RMSE) of the proposed GloWH-Net model for retrieving SWH were 0.330 m, 0.393 m, and 0.433 m, respectively, the correlation coefficients (CC) were 0.91, 0.89, and 0.84, respectively. Compared with the empirical combination model based on the minimum variance estimator (MVE), the RMSE of SWH retrieval is reduced by 53.45%, 48.06%, and 40.63%, respectively; Compared to bagging tree (BT) machine learning model, the RMSE of SWH retrieval decreased by 21.92%, 18.72%, and 4.47%, respectively. This indicates that the deep learning method proposed in this article has significant advantages in retrieving global sea surface SWH.
