Surface soil moisture data from satellite retrievals and land model integrations of observed meteorological forcing data typically exhibit very different mean values and variability. Yet both have been shown to contain consistent and useful information in their seasonal cycle and anomaly signals that can be merged and maximized in a data assimilation system. A simple and effective method of bias removal is to match the cumulative distribution functions (cdf) of the satellite and model data while preserving the anomalies. However, accurate cdf estimation typically requires a long record of satellite data. We demonstrate here that by using spatial sampling with a 2 degree moving window we can obtain statistics based on a one-year satellite record that are a good approximation of the desired local statistics of a long time series. This key property opens up the possibility for operational use of current and future satellite soil moisture data. We will also present a merged dataset resulting from assimilation of satellite retrievals from the Scanning Multichannel Microwave Radiometer (SMMR) into the NASA Catchment land surface model for the period 1979 to 1987. The assimilation system is based on the Ensemble Kalman filter (EnKF). Our results show that the merged dataset produced by the assimilation system agrees significantly better with ground measurements than either model soil moisture or satellite retrievals alone.