Spatial distribution and structure of remotely sensed surface water content estimated by a thermal inertia approach.

A major problem recurring in soil and hydrological sciences is the representation of flow and transport processes in the presence of large spatial and temporal variability of soil hydraulic properties. Their measurement is normally time consuming and expensive and it is usually considered impractical to perform sufficiently dense in situ measurements. Measurement techniques primarily designed for field, plot or local scale monitoring, are often impractical for larger scales such as watersheds. However, remote sensing from air- or space-borne platforms offers the possibility to address this problem by providing large spatial coverage and temporal continuity. A crucial variable that can actually be monitored by remote sensors is the water content in a thin soil layer, usually up to a depth of 5 cm below the surface. However, difficulties arise in the estimation of the vertical and horizontal distribution of the water content within the soil profile, which are closely connected to soil hydraulic properties and their spatial distribution. A promising approach for estimating soil water content profiles is the integration of remote sensing of surface water content and hydrological modeling. A major goal of the scientific group is to develop a practical and robust procedure for estimating water contents throughout the soil profile from surface water contents to be deduced from thermal inertia data, which in turn have to be estimated by multi-spectral remote sensing data. The procedure is largely based on the integration of the remote sensing information into a hydrological model to be used in a stochastic simulation framework, in the perspective of predicting the crucial vadose zone processes at large scale. As a first step, in this work we will show some preliminary results from aircraft image analysis and their validation by field campaign data. The data extracted from the airborne sensors provided the opportunity to retrieve land surface temperatures with a very high spatial resolution. The distribution of surface moisture, as deduced by the thermal inertia estimates, was compared to the surface water content maps measured in situ by TDR-based probes.