This study presents the use of a Soil Aridity Index (SAI) as a substitute to the Aridity Index (AI) for the assessment of desertification risk at the national and more detailed scales. The SAI expresses the mean annual number of days when the moisture control section is dry in soils with a Mediterranean type of climate. This value was first estimated using software based on the Erosion/Production Index Calculation model, then a multiple regression was found between SAI and long-term mean annual air temperature, total annual rainfall and soil available water capacity. The index was calculated for 13 000 Italian soils, mapped at 1:250 000 scale, and classified according to the influence of increasing water scarcity on agriculture and forestry. SAI classes were tested against (i) AI classes and (ii) vegetation density in natural and natural like areas, evaluated by means of a Normalized Difference Vegetation Index analysis. SAI classes showed good agreement with AI classes, but separated areas with different vegetation cover more consistently than AI classes, and at a much more detailed scale. The SAI, being influenced by both soil and climate, is a useful indicator of vulnerable lands, where increased rainfall deficit and enhanced soil erosion could lead to desertification.

The Experimental Institute for Soil Study and Conservation has been endorsed of the realization of a new atlas of the desertification risk in Italy at 1:250,000 scale by the Italian Ministry of Environment. The methodology proposed is based on the use of indicators of pressures, state and response. Since soil stores water and mitigates drought risk and temperature excursions in the root zone, pedoclimatic regimes were considered as an useful state indicator. Aim of this work was to test a pedoclimatic GIS and the use of the EPIC model at the national and regional scales to assess soil drought risk. The pedoclimatic GIS stores data about 140 climatic stations, 207 soils and 259 elaborations. This data were elaborated to evaluate pedoclimatic regimes of the whole country. A series of 656 meteorological stations were elaborated to characterize aridity index. The aridity index was obtained applying the Hargreaves-Samani methodology (1982) on the long term mean monthly temperature and rainfall values and it was spatialized by means of the inverse distance weighed (IDW) tool of ArcGIS. Soil moisture regime was obtained using EPIC (Environmental Policy Integrated Climate, former Erosion-Productivity Impact Calculator, Sharpley and Williams, 1990) daily outputs, while soil temperature regime from the algorithm proposed by Costantini et al.

Accurate knowledge of the current and future spatial variation in surface and root zone soil moisture at high resolution is critical for achieving sustainable land and water management. The fundamental limitation is that the spatial and temporal variation in soil moisture is not well known, nor easy to measure or predict. Consequently, a prototype soil moisture monitoring, prediction and reporting system is being developed for Australia, with the Murrumbidgee as the demonstration catchment. The system will provide current and future soil moisture information and its uncertainty at 1km resolution, by combining weather, climate and land surface model predictions with soil moisture data from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite, launched on 2 November 2009; the first-ever dedicated microwave soil moisture mission. A significant component of this project is developing and testing the soil moisture retrieval algorithms to be used by SMOS and verifying the SMOS data for Australian conditions.

Over the past two years, two field campaigns have been conducted, with their data being accessible through this website. The first two campaigns focused on studying potential calibration sites in Central Australia (SMOS Arid-Zone Experiments - SAZE-Oz) and in the Murrumbidgee River catchment (Australian Airborne Cal/Val Experiments for SMOS - AACES).

Objectives
• Overall: Integrate satellite precipitation and soil
moisture data into WAOB GLADSE
• Develop products
• Develop (Web) services
• Measure effects
• Implement in WAOB operational environment
• Sustain post-project long-term use

The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA Earth Observing System (EOS) Aqua satellite provides global passive microwave measurements of terrestrial, oceanic, and atmospheric variables for the investigation of water and energy cycles. Soil moisture and other land surface variables are key variables in understanding land surface hydrology and in modeling ecosystems, weather, and climate.

This gridded Level-3 land surface product (AE_Land3) includes daily measurements of surface soil moisture and vegetation/roughness water content interpretive information, as well as brightness temperatures and quality control variables. Ancillary data include time, geolocation, and quality assessment. Input brightness temperature data, corresponding to a 56 km mean spatial resolution, are resampled to a global cylindrical 25 km Equal-Area Scalable Earth Grid (EASE-Grid) cell spacing. Data are stored in HDF-EOS format, and are available from 19 June 2002 to the present via FTP.

Citing These Data
The following example shows how to cite the use of this data set in a publication. For more information, see our Use and Copyright Web page.

Njoku, E. G. 2004. AMSR-E/Aqua Daily L3 Surface Soil Moisture, Interpretive Parameters, & QC EASE-Grids. Version 2. [indicate subset used]. Boulder, Colorado USA: NASA DAAC at the National Snow and Ice Data Center.