The algorithm used for estimating soil moisture based on data collected by the passive microwave electronically scanned thinned array radiometer (ESTAR) requires the use of ancillary data. ESTAR produces a spatially distributed map product, therefore, processing to soil moisture is facilitated by using a geographic information system (GIS). The GIS consists of raster data files of ESTAR brightness temperature on each day at an 800 m pixel size, land cover at 30 m resolution, Normalized Difference Vegetation Index (NDVI) at 30 m resolution, soil texture at 1 km, and soil temperature at 10 cm under sod. Soil temperature is used to normalize brightness temperature. Data collected at the 112 Oklahoma Mesonet stations at 10 cm under sod were used as input to a grid resampling program to produce an 800 m product for each day. The land cover data base was developed from several Landsat TM images and was used for screening specific classes, estimating bulk density, and vegetation correction. Vegetation correction consisted of two steps; determining the vegetation type in order to estimate the vegetation parameter and using a land cover-NDVI function to estimate the vegetation water content. The results at 30 m were integrated up to 800 m. Soil texture was derived from a version of the STATSGO soils data base that provides a surface texture classification at 1 km (Penn State Univ.). Percent sand and percent clay were assigned to each class based on the soil classification triangle. These data planes were used with the daily acquisitions by the ESTAR instrument and a verified soil moisture retrieval algorithm to generate soil moisture images at an 800 m resolution. Other data bases will be integrated with these as the research progresses