European Water 35: 45-56, 2011. © 2011 E.W. Publications Production of a 5-years long dataset of Soil Moisture Maps on Italian Territory with an Operational Algorithm L. Campo 1 , F. Castelli 1 , F. Caparrini 2 and D. Entekhabi 3 1 Department of Civil and Environmental Engineering, University of Firenze, Italia, email: lcampo1@dicea.unifi.it 2 Eumechanos, Firenze, Italia, email: f.caparrini@eumechanos.it 3 Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, e-mail: darae@mit.edu Abstract: The estimation of the soil moisture state is fundamental in water resources management, agricultural application and prediction of drought and flood events. The evaluation of such variable at watershed scale can be achieved by mean of remote sensing techniques. In this work maps of Land Surface Temperature retrieved by satellite imagery were used in a variational assimilation framework named ACHAB (Assimilation Code for Hydrologic and Atmospheric Budget) in order to provide optimal estimates of the land surface energy balance components and daily maps of soil moisture saturation index (SMSI). The assimilation procedure is able to discern the soil and the vegetation components, and requires ground observations of air temperature and humidity, solar radiation and wind velocity. ACHAB utilizes LST maps from different sensors, in this work images from MSG-SEVIRI and Terra-MODIS were used. The simulation was performed over the whole Italian territory for five years (2005-2009) with about 4 km of spatial resolution. A climatological analysis of the SMSI maps was performed and reliability index maps were provided for the period of analysis. This study was realized in the framework of “OPERA – Protezione Civile dalle Alluvioni”, a project of the Italian Civil Protection aimed to the operational use of satellite data for floods prediction and water resources management. Key words: soil moisture, remote sensing, MSG, data assimilation. 1. INTRODUCTION The importance of the knowledge of the soil moisture state is largely recognized in hydrological, meteorological and agricultural applications (Fast and McCorcle, 1991; Engman, 1992; Entekhabi et al., 1993; Entekhabi et al., 1996; Saha, 1995; Su et al., 1995; Walker, 1999; Kluse and Allen Diaz, 2004; Brocca et al., 2010a). However, direct measurements of this variable at spatial and temporal scales significant for the characterization of the water balance on large areas are not possible (Steele-Dunne et al., 2010; Dong and Vuran, 2010). Due to the economical unfeasibility of large networks of soil moisture ground sensors, a largely utilized tool is constituted by remote sensing, if necessary in conjunction with ground observed data (Wang and Qu, 2009). In last years large datasets of soil moisture maps have been produced basing on microwave sensors that allowed a global cover on a daily basis with reference, for instance, to SMOS (Soil Moisture and Ocean Salinity), ASCAT (Advanced SCATterometer) and AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) datasets (De Jeu et al., 2008; Draper et al., 2009; Brocca et al., 2010b; Kerr et al., 2010; Brocca et al., 2011; Matgen et al., 2011). Such estimates, however, lack in spatial resolution, having an horizontal cell size of about 25 km and, in some case, present scarce accord with the ground measures, especially in densely vegetated areas (Parinussa et al., 2011). Moving to finer spatial scales, indirect methods are also employed in which the soil saturation is estimated as a variable related to the energy balance between the land surface and the atmosphere (Pratt & Ellyett, 1979, Carlson et al., 1994, Moran et al., 1994). Spectral measurements available from space borne sensors are not directly linked to the surface heat and moisture fluxes, but they can be used to infer physical conditions at the land surface that are intimately related to the energy balance. Among these conditions the Land Surface