Recent advances in radar remote sensing techniques illustrate the potential for monitoring soil moistureconditions at spatial and temporal scales required for regional and local modeling efforts. This research examined thefeasibility of producing accurate and spatially distributed estimates of soil moisture using a time series of ERS2 radar imagesfor a tallgrass prairie ecosystem in northeast Kansas. Methods used included field data collection of soil moisture, digitalimage interpretation of optical (NOAA AVHRR and LANDSAT TM) and radar (ERS2) imagery, and environmental modelingin a raster geographic information system (GIS) and image processing environment. Critical to this study was determiningthe scattering behavior of overlying vegetation, or the contribution of vegetation backscatter (.oveg) to the total backscattercoefficient (.ototal), which was simulated using a modified water cloud model. By removing .oveg from .ototal, the amount ofbackscatter contributed by the soil surface (.osoil) was isolated and the linear relationship between .osoil and volumetric soilmoisture determined. Singledate correlations averaged r = 0.62 and r = 0.67 for a burned and unburned watershed,respectively, within the study area. While previous studies have questioned the sensitivity of Cband radars to nearsurfacesoil moisture conditions, these results show that ERS2 data may be capable of monitoring soil moisture conditions over evenextremely dense natural grassland vegetation.