Sea surface winds and waves are very important phenomena that exist in the air-sea boundary layer. With the advent of climate change, cascade effects are bringing more attention to these phenomenon as warmer sea surface temperatures bring about stronger winds, thereby altering global wave conditions. Synthetic aperture radar (SAR) is a powerful sensor for high-resolution sea surface wind and wave observations, and has accumulated data in large quantities. Furthermore, deep learning methods have been increasingly utilized in geoscience, especially the inversion of ocean information from SAR imagery. Here we propose a method to invert various parameters of ocean surface winds and waves using Sentinel-1 SAR data. To ensure this method is more robust and scalable, we augmented the input data with dual-polarized SAR imagery, incident angle, and a more constrained homogeneity test. This method adopts a deeper structure in order to retrieve more wind and wave parameters, and the use of residual networks can accelerate training convergence and improve regression accuracy. Using 1600 training samples filtered by a novel homogeneity test and with significant wave heights between 0-10 m, results from error parameters including the root mean square error (RMSE), scatter index (SI), and correlation coefficient (COR) show great performance of this proposed method. The RMSE is 0.45 m, 0.76 s and 1.90 m/s for the significant wave height, mean wave period and wind speed, respectively. Furthermore, the temporal variation and spatial distribution of the estimates are also consistent with China-France Oceanography Satellite (CFOSAT) observations, buoys measurements, WaveWatch3 regional model data, and ERA5 reanalysis data.