Two empirical algorithms are developed for wave mode images measured from the synthetic aperture radar aboard Sentinel-1 A. The first method, called CWAVE_S1A, is an extension of previous efforts developed for ERS2 and the second method, called Fnn, uses the azimuth cutoff amongst other parameters to estimate significant wave heights and average wave periods without using a modulation transfer function. Neural networks are trained using co-located data generated from WAVEWATCH III and independently verified with data from altimeters and in-situ buoys. We use neural networks to relate the nonlinear relationships between the input SAR image parameters and output geophysical wave parameters. CWAVE_S1A performs well and has reduced precision compared to Fnn with Hs root mean square errors within 0.5 and 0.6 m respectively. The developed neural networks extend the SAR's ability to retrieve useful wave information under a large range of environmental conditions including extra-tropical and tropical cyclones.