The incidence angle dependence of synthetic aperture radar (SAR) backscatter from sea ice has been observed to differ between ice types. This is indicative of its complex dielectric and backscattering properties and cannot be analytically determined or easily modelled. Without assumptions this dependence can only be measured from SAR through multiple observations of the same ice. Due to the sea ice drift, doing so at high resolution is time inefficient and to some extent only feasible for land fast ice. Thus, the incidence angle dependence can currently not be easily related to ground measurements or be fully exploited for classification tasks. By reformulating the problem of incidence angle dependence to a domain transfer task and making use of known physical relations, we can train a Generative Adversarial Network (GAN) to predict the incidence angle dependence from a single Sentinel-1 satellite scene patch-wise using no only local data. The network’s predictions are validated for multiyear and first-year ice backscatter using coincident ICESat-2 altimeter measurements. The network suggests a diverse incidence angle dependence in the HV channel, that so far have been observed only scarsely. The results of this study are a novelty in sea ice remote sensing insofar that artificial intelligence manages to solve a task that would be nearly impossible for a human observer.