- Climate change and drought frequency is a major concern in the Middle East. A significant lack of surface data is apparent. Remotely sensed images have the capability of providing regional scale information and containing historical data. While the literature is abundant with many models for extracting energy balance and surface data from imagery few studies integrate the various images and necessary parameters for energy-balance studies. For this study we synergize multi-platform and multi-spectral data using Landsat and Spot data for mapping vegetation and albedo, ERS-1 and 2 for mapping aerodynamic roughness and soil water-content. Soil types are taken from existing soil survey maps corrected by the use of multi-spectral classification. Higher resolution, airborne data are used for focused studies such as the use of a P-band scatterometer to characterize the wetting and drying components of specific soils, and high resolution SAR to characterize the loss of water from agricultural fields. This study is accompanied by an extensive effort to provide well calibrated ground truth supporting data. The ground data include TDR measurements of soil water-content and salinity extracted from the dielectric properties. The soil was further classified to types including the physical and chemical properties and surface roughness. Ultra-spectral measurements using an ASDI spectrometer from 350-2500nm were conducted for monitoring crop water stress. The studies are focused on seasonal variations and extreme climate changes, i.e., very wet and extreme drought conditions. Overall, we have preliminary results demonstrating the capability to map the aerodynamic properties, vegetation extent, and the changes in water content. For example, we find that in the small Mashash watershed the water content decreases from 20 to 4%. Vegetation, another important energy-balance parameter was mapped using the WDVI method with Landsat TM and Spot data together with soil information from an existing GIS database on soils. Irrigated fields provided the highest biomass index and open Loessy soils with natural vegetation provided the lowest seasonal dependence. The results are being used to map the spatio-temporal variability of the energy balance in the semi-arid zone.