<p>Soil water content (SWC) is an essential climatic variable that plays a leading role in different soil-related applications in the ecosystem. Especially in agriculture, SWC controls the plants' growth and soil productivity, so its accurate estimation could improve the crops’ yields and provide necessary information to optimize water consumption in precision farming.</p> <p>Optical sensors cannot accurately measure SWC in agricultural parcels, because agricultural fields are predominantly vegetated and covered with plants with high biomass. A Ground Penetrating Radar (GPR) sensor that can penetrate the ground and record pertinent information on soil roughness and its dielectric constant could be a solution for SWC monitoring in agricultural fields.</p> <p>In this study, we investigate the potential of a GPR sensor mounted on a drone for monitoring SWC on a profile scale from the surface to a specified depth. The results demonstrated that both the GPR reflection and derivative variables associated with the amplitude could be utilized as informative input variables in an Artificial Neural Network model for the estimation of the SWC.</p>