## 5 Conclusions  
In this paper, the performance of the thermal inertia and Hydrus models to estimate the SWC of the upper soil layer were assessed under both laboratory and field conditions. The TLSH and ThetaProbe indoor measures allowed the calibration of a relationship between soil thermal inertia and its water content. In the laboratory, the soil water content (SWC\({}_{\text{Kp}}\)) was computed according to [PERSON] et al.'s 2009 [8] model in which the fitting parameter, obtained by fixing the soil porosity \(e\) = 0.5 m\({}^{3}\) m\({}^{-3}\), was equal to \(\varepsilon\) = 1.78 and \(\mu\) = 0.47.  
A quantitative analysis of the examined models showed good performance for the Hydrus-2D model if compared to the measured soil water content, while the thermal inertia model simulated SWC values lower than those measured. However, the Nash-Sutcliffe efficiency coefficient was appreciable for both the Hydrus-2D (\(E\) = 0.98) and the thermal inertia model based on TLHS (\(E\) = 0.82), showing that both the methodologies can be considered suitable to predict the temporal dynamics of _SWC_ in the surface soil layer.  
A remote-sensing-based thermal inertia method was then applied in the open field under natural conditions, allowing monitoring of the soil thermal properties of two extreme soil water statuses (well-watered and air-dry). Continuous sensor-based measurements of the soil water content allowed the evaluation, qualitatively and quantitatively, of the performance of thermal inertia and the Hydrus-1D models.  
In the field trial, the very high spatial resolution (\(\approx\)0.03 m) of the proximity sensing images allowed the emphasis of the main operational limits due to tree shadows and soil vegetation cover.  
On the other hand, simulations with the Hydrus-1D model allowed enhancing SWC simulated by the remote-sensing-based thermal inertia model (SWC\({}_{\text{RS}}\)). The Hydrus-1D model showed good performance if compared to the soil water content measured by means of the TDR probe. Based on the Nash-Sutcliffe efficiency coefficient, however, the confirmation of the good performance can be ascertained only in the wet conditions (\(E\) = 0.98), being \(E\) < 0 under the dry conditions.  
With reference to the RS-based model, even if a limited number of remote observations were available, it was possible to quantify good predictability with the Hydrus 1D model with average _RMSEs_ equal to 0.03 m\({}^{3}\) m\({}^{-3}\) and 0.02 m\({}^{3}\) m\({}^{-3}\), respectively, for the wet and dry conditions.  
Further investigations will be carried out to assimilate the remote sensed acquisitions in numerical models, as well as to extend the proposed application to larger scales.  
This work has been partly funded by the Universita di Pisa (Fondi di Ateneo, 2016) and by Universita degli Studi di Palermo (Progetto MITO: Informazioni Multimediali per Oggetti Territorial PAC01_00119, CUP B72F1300030001. Linea intervento \"Ricerca e Innovazione\"--Azione \"Potenziamento Strutturale\"). All authors set up the research, analyzed the results and contributed to writing the paper.  
Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.