Annual and monthly average temperatures are analyzed with statistical methods to characterize the temperature regime in Italy. Data from 738 weather stations, with homogeneous spatial cover throughout Italy, are used to estimate the annual and monthly air temperature normals. Geographic and morphologic parameters, computed around the points of measure, are considered as explicative variables within a georegression model. Morphometric variables are defined using the USGS GTOPO30 digital elevation model, which has a resolution of 1 km. On the basis of a stepwise regression analysis, the significant variables are found to be elevation, latitude, distance from the sea, and a measure of terrain concavity. The relationship between the average annual air temperature and the mentioned variables explains 92% of the variance and produces a standard error of 0.89°C. The temperature regime ͑normalized mean temperature for each of the 12 months͒ is reproduced with a two-harmonic Fourier series, with parameters estimated using stepwise regression. Analyses of the reconstruction errors demonstrate that the results are quite satisfactory for many technical purposes, particularly for large-scale climatic characterization.
AVHRR (Advanced Very High Resolution Radiometer on board NOAA satellites) data are considered here to evaluate the possibility of using the surface temperature as an indicator of the soil/canopy water content at the short timescale. This is obtained by means of an indirect approach based on a simplified soil-atmosphere energy balance. The techniques provide sufficiently detailed coverage of the processes in terms of the time and spatial scale with respect to hydrological applications. Two different approaches have been tried: the first based on thermal inertia measurements (Xue & Cracknell, 1995) 1 through ATI (Apparent Thermal Inertia), the second based on surface temperature (LST) and vegetation indices (NDVI), with the TVDI (Temperature Vegetation Dryness Index) suggested by Sandholt et al. (2002) 2. Both techniques were used in detecting moist areas in a single image (or day/night images), and in multitemporal multitemporal applications. In particular, a new cloud detection algorithm, based on the bimodal frequency distribution of the infrared brightness temperatures (Ch 5 AVHRR) when clouds affect the image, has been proposed for ATI. As regards TVDI, a modified technique has been proposed for fixing the warm edge of the triangle based on the detection of the extreme dryness conditions on a monthly basis. The modified TVDI has been tested in comparison with an antecedent precipitation index (API) for moisture detection in single images. The substitution of day/night land surface temperature differences instead of noon temperatures in the "triangle method" has been also tested with good results in the multitemporal approach. Application of the proposed techniques can allow one to track the evolution of soil moisture in space and time and to improve the knowledge on the relationship between vegetation (NDVI) and soil moisture dynamics.
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