Current climate changes, with weather-related natural disasters-such as floods, storms, cyclones, fires, and drought-have prompted scientific research to perform in-depth investigations of all components of the hydrological cycle and their subsequent interactions. Among parameters of the terrestrial water cycle, soil moisture content (SMC) emerges as a fundamental surface variable, controlling the water, energy, and carbon exchanges at the land-atmosphere interface. A precise evaluation of soil moisture on several spatial scales (from local to regional scales) can therefore be helpful in assessing the distribution of water between blue (surface water and ground water) and green (rainwater stored in the soil), the latter being essential for agricultural purposes (Liu et al. 2009; Liu and Yang 2010) or for identifying water wastes, due to human activities (Zang et al. 2012). The spatial distribution of soil moisture and its temporal evolution also play a significant role in the forecasting and management of flooding and landslides. However, a quantitative and accurate estimate of SMC on a global scale by using traditional techniques is intrinsically inadequate, since in situ measurement techniques are time consuming and the local SMC is highly variable in both space and time (Leese et al. 2001). Moreover, the use of hydrological models for estimating SMC and extending the forecast of moisture distribution over larger areas is not straightforward and depends heavily on the homogeneity of the selected areas and on the type and quality of information available (regarding their soil hydraulic characteristics and permeability, meteorological and climatological data, etc.).