Fast soil moisture content (SMC) mapping is necessary to support water resource management and to understand crops' growth, quality and yield. Thereby, Earth Observation (EO) plays a key role due to its ability of almost real-time monitoring of large areas in a low cost. This study aims to explore the possibility of taking advantage of free-available Sentinel-1 (S1) and Sentinel-2 (S2) EO data for the simultaneous prediction of SMC with cycle-consistent adversarial network (cycleGAN) for time-series gap filing. The proposed methodology, first, learns latent low-dimensional representation of the satellite images, then learns a simple machine learning model on top of these representations. Specifically, we presented an efficient framework for extracting latent features from S1 and S2 imagery.