## 1 Introduction  
Soil water content (SWC) in the upper soil layer remarkably affects runoff generation and infiltration processes, with obvious direct consequences on water fluxes through the soil-plant-atmosphere continuum [1]. Precise evaluations of this agro-ecological variable can contribute to better estimating the dynamics of these fluxes [2]. However, accurate estimation of SWC across spatial and temporal scales is still a challenging task mainly at high temporal (minutes to days) observation scales and high spatial resolution (\(-0.1\) m). Even though remote sensing (RS) does not provide direct measurements of surface SWC, models employing remotely-sensed thermal and visible/near-infrared images have been developed to this aim. Thermal inertia, in particular, has already been proven to be suitable to assess the actual distribution of SWC at spatial resolutions ranging from \(\sim\)1 m to \(\sim\)1 km by processing airborne or satellite images [3; 4]. Estimations of SWC are based on the existing relationship with soil thermal properties [3], which depends on soil bulk density, specific thermal capacity and thermal conductivity. Indeed, thermal inertia is equal to the square root of the product between volumetric heat capacity and thermal conductivity and it represents the admittance of the medium to change its temperature as a result of heat storage or release. The accuracies of these methods, based on high-resolution remote-sensing data (acquired from a proximity platform) are still unexplored. Many authors [5; 6] attempted to develop spatial information on thermal inertia distribution using the daily maximum and minimum radiative temperatures of the soil surface acquired by remote or proximity sensing. In these studies, different algorithms, usually referring to the radiative surface temperature, have been proposed to map the thermal inertia distribution [7]. Recently, other studies have shown the potential usefulness of the thermal inertia approach to estimate SWC by means of daytime and nighttime surface temperatures, as well as visible-near infrared (VIS-NIR) images [3; 8; 9]. Since the magnitude of the water thermal inertia is greater than that corresponding to the porous medium, the changes in the thermal inertia of soil can provide significant indications for its water content. In fact, SWC regulates the ability of soil to change its thermal status by accumulating or releasing heat within a defined time interval. Indeed, heat storage or release is forced by the energy balance at the soil surface. In particular, the amplitude of temperature variations is a direct consequence of the amount of energy entering the soil through the shortwave albedo. Coupling between the emerging surface thermal imaging techniques [3] and hydrological physically-based models [10] could provide accurate assessment for SWC dynamics at different temporal and spatial scales. More specifically, it could be valuable to couple remote sensing and hydrological physically-based (e.g., Hydrus 2D) models to exploit their benefits: the former capable of describing the spatial patterns of SWC at the acquisition time, the latter able to describe the SWC in time usually at some points of the domain. Hydroological physically-based models can be considered powerful and simple tools to predict SWC at various soil depths. During the last decades, a number of models have been developed and applied to simulate mass exchange processes in the soil-plant-atmosphere (SPA) continuum [11; 12; 13]. Several models have been proposed to simulate all the components of the water balance, including actual crop evapotranspiration [14], in particular under arid and semi-arid conditions [15; 16], deep percolation [17], and water and solute transport [18; 19]. The Hydrus numerical model [20], a physically-based code which solves the Richards equation for variably saturated water flow, allows simulating water and solute movement in one, two and three-dimensional variably saturated porous media.