Active and passive microwave remote sensing systems have shown the ability to measure the soil moisture content in the near-surface layer under a variety of topographic and land cover conditions.  The successful mapping of soil moisture from active microwave sensors with high spatial resolution (up to 25 m) would be an advantage to agricultural and hydrological application.  However, passive microwave sensors provide low spatial resolution (40 to 50 km) has application in meteorological and weather prediction modeling.  The aim of this seminar is twofold: First, discuss soil moisture retrieval from active microwave (RADARSAT-1) data using Neural Network and Fuzzy Logic methods.  The effect of soil texture, land cover, vegetation parameters on soil moisture retrieval was investigated.  Second, analyze the variability of large scale in-situ OK Mesonet and AGRMET (Agricultural Meteorology model) soil moisture data using geostatistical (variogram and kriging) technique.  The validation of low resolution satellite soil moisture products is difficult using only field point measurements.  In such case, Geostatistical approach could be used to scale up existing field soil moisture measurement networks to the resolution of footprints of satellite image.