Potentials of Satellite Microwave Data in Mapping Soil Water Content in Semi-Arid Regions Hosni Ghedira Director, Environmental Remote Sensing and Image Processing Laboratory NOAA-CREST, City University of New York, USA Email: ghedira@ccny.cuny.edu Soil water content, or soil moisture, is being increasingly used as input to hydrological models. Having an accurate estimation of soil moisture with acceptable resolution and revisit times is indispensable for an efficient hydrological modeling and an improved runoff forecasts. Additionally, improved estimates of spatial and temporal variation of surface moisture will significantly enhance our ability to predict more accurately the magnitude and the timing of extreme events such as flashfloods and droughts. Actually, most of hydrological and climate prediction models that require soil moisture information are using maps obtained by gridding standard point measurements or derived from physically-based models. With the actual field measurement techniques, it is very difficult to have a spatial measurement of soil moisture, as it varies spatially and its value is generally affected by the heterogeneous aspect of soil surface characteristics. Various remote sensing techniques have been evaluated and proven to be a valuable source of information for different hydrological applications. The actual remote sensing sensors offer the potential of measuring new hydrologic variables particularly valuable for flood forecasting and hydrological modeling. Soil moisture is one of those parameters, which plays a central role in a wide variety of hydrological system processes. Soil moisture is an important component of the hydrological cycle. The capability to observe soil moisture frequently and over large regions could significantly improve our ability to estimate some hydrological parameters such as evaporation, transpiration, infiltration, and drainage classes, which are very useful in drought forecasting and monitoring. Soil moisture estimation based on active microwave data only may face several challenges since the microwave sensors are sensitive to other land cover characteristics such as vegetation density, surface roughness, and soil texture. Our research team at the Environmental Remote Sensing and Image Processing Laboratory (ERS-L) at the City University of New York have developed a new algorithm to estimate the soil moisture in semi-arid watersheds with sufficient temporal and spatial resolution. This tool had improved our ability to better understand the dynamic of an important component of the water budget: the soil moisture. Two types of microwave remote sensing sensors have been used in this research: passive microwave from SSM/I sensor for global estimation (low resolution ~ 25 x 25 km) and SAR from RADARSAT Satellite (SCANSAR mode) for local estimation (high resolution 50 x 50 m). The developed model has been successively tested in Arizona (USA) and produced soil moisture maps with 10% accuracy. The output of this model is to be assimilated as additional input to the advanced hydrologic prediction system (AHPS) operated by the US National Weather Service (NWS/NOAA) for flood warning and water resource forecasts.