Global land surface soil moisture maps are essential in a number of research topics such as land surface energy balance, mass exchange between the land surface and atmosphere and improving global weather forecasting models. Since it is not practical to provide such maps using ground measurements, land surface soil moisture remote sensing has been a hot research topic in the last several decades (Zhan et al., 2008). On the one hand, a number of soil moisture retrieval methods using microwave satellite measurements have been developed and, as a result, a number of soil moisture products have been produced from different satellite sensors. On the other hand, none of these products can meet the quality requirements for most of the hydrological applications (Wagner et al., 2007). There is still a much need to improve the quality of global soil moisture products from satellite sensors. The Soil Moisture Operational Product System (SMOPS) at National Oceanic and Atmospheric Administration (NOAA) is designed to produce a satellite-based global soil moisture observation data product that will provide constraint in surface soil moisture estimates in Global Forecasts System (GFS) and N. American mesoscale Model (NAM) at the National Centers for Environmental Prediction (NCEP). All microwave soil moisture remote sensing satellites, currently in space or to be launched in near future, do not have a full daily global coverage. To increase the spatial coverage of daily soil moisture retrievals, SMOPS provides a soil moisture data layer that merges soil moisture retrievals from multiple satellites in addition to the individual soil moisture retrievals from each of the available satellites. These soil moisture products are either produced using SMOPS' own algorithm from different microwave satellite observations, or some existing soil moisture products. Cumulative Distribution Functions (CDF) generated from historical data are used to merge soil moisture products from different satellite sensors to a reference product. The merged soil moisture product not only has both spatial and temporal consistencies, but also it makes the best use of available products for an improved spatial and temporal coverage. Currently, the existing soil moisture products used in SMOPS include those from European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) program and the Advanced Scatterometer (ASCAT) on EUMETSAT's Metop satellite. Beside these existing products, SMOPS also uses an AMSR-E soil moisture product produced by its own algorithm for the merged product. A compete daily global coverage is usually achieved in the merged product using these three products. In this paper, the new Advanced Microwave Imaging Radiometer (AMSR-E) soil moisture product from SMOPS is also introduced, and comparisons between this product and some other soil moisture product and ground measurements are presented. This product uses the Single Channel Retrieval (SCR) method that is mainly based upon an algorithm developed by Jackson (1993). In this approach, brightness temperature from a single microwave sensor channel, usually around 10 GHz, is converted to emissivity that is further corrected for vegetation and surface roughness effect. The Fresnel equation is then used to determine the dielectric constant and a dielectric mixing model is used to obtain the soil moisture. Previous research has shown that the SCR soil moisture retrievals from WindSat demonstrated good temporal and spatial dynamics and better agreement with the in situ measurements comparing with the NASA AMSR-E baseline soil moisture data product (Liu et al., 2008). Figure 1. A 3-day composite of NOAA SMOPS AMSR-E soil moisture product. Figure 1 shows a 3-day composite of NOAA SMOPS AMSR-E soil moisture product. This map generally shows a good dynamic range of retrieved soil moisture values from very dry (almost 0 percent) to saturation. The spatial patterns are also consistent with global dry/wet patterns of climate regimes. Soil moisture time series from SMOPS AMSR-E product and three other products are also compared. These three products are NASA AMSR-E baseline soil moisture product, ESA's SMOS soil moisture product and EUMETSAT's ASCAT soil moisture product. The results show that all soil moisture products show similar spatial and temporal dynamics but with different magnitudes. Among them, NASA AMSR-E has the smallest dynamic range while there is no significant different between the other three. Field observations are also used to evaluate the SMOPS AMSR-E soil moisture product. The results show that, in most cases, the Root Mean Square Error (RMSE) between the field measurements and the SMOPS AMSR-E soil moisture product is below 0.05.