Soil moisture is a key hydrometeorological variable that can be derived from both modeling simulations and satellite observations. This study compares Global Land Data Assimilation System (GLDAS) output over the Murray Darling Basin against retrievals from a newly developed remote sensing product using the AMSR-E sensor onboard NASA's Aqua satellite. GLDAS is comprised of a number of land surface models, two of which include the Community Land Model (CLM) and NOAH land surface scheme, which provide a temporally and spatially consistent characterization of the hydrological cycle. GLDAS derived estimates are 3-hourly products with 0.25-degree spatial resolution, while satellite based observations offer twice-daily instantaneous retrievals at similar spatial scales. The models represent different soil moisture averaging depths (roughly 2, 5, and 10 cm in CLM and 10 cm in NOAH) and retrievals from AMSR-E Cband approximate the soil moisture in the top 1.5 cm layer. The spatial distribution and coherence of soil moisture are investigated seasonally and under both wetting and drying conditions. From the spatial aspect, AMSR-E observations and GLDAS simulations show similar seasonal patterns, while simulated soil moisture is slightly higher during summer and autumn over the north-eastern Murray Darling Basin (MDB). This may be explained by the positive biases of GLDAS forcing precipitation data. From the temporal perspective, the best match between AMSR-E soil moisture and model simulations is found over the regions with strong precipitation in warm months, e.g. north-eastern MDB. Over the regions with high precipitation during cool months, AMSR-E soil moisture is systematically higher than model simulations. For the regions with extremely low annual rainfall, the peak values in soil moisture between AMSR-E and model simulations match very well, while low values of soil moisture display the greatest differences. Generally, the agreements between AMSR-E observations and GLDAS simulations vary under different wetting and drying conditions. Both of them can represent the 'true' soil moisture to some extent. How to best blend soil moisture products derived from these two different techniques, in addition to data assimilation approaches, will be explored in future research.