This study assesses two approaches to combine observations from active and passive satellite microwave instruments in a soil moisture data assimilation system. In the first approach, labeled 'joint retrieval assimilation', a single soil moisture product is simultaneously retrieved from active and passive microwave observations, and then assimilated into the NASA Catchment land surface model. In the second approach, labeled 'separate retrieval assimilation', separate soil moisture products are retrieved from each of the active and passive microwave observations, before being simultaneously assimilated into the model. In both approaches, a Neural Network (NN) is used to retrieve soil moisture from passive microwave AMSR-E brightness temperatures and/or active microwave ASCAT backscatter observations. A spatially distributed (3D) ensemble Kalman filter is used for the assimilation over the contiguous United States from August 2007 until September 2011. The analysis skill of both assimilation approaches is evaluated against in situ observations from 60 SCAN stations and compared to the model open loop (no assimilation) skill. When averaged across the 60 sites, the skill obtained from both assimilation experiments is very similar. For surface soil moisture, the average correlation and anomaly correlation are 0.69 and 0.58, respectively. These metrics are slightly better than those of the open loop, by 0.05 for the correlation and by 0.03 for the anomaly correlation. The root zone soil moisture estimates from the assimilation were also slightly improved compared to the open loop (by 0.03 for the average correlation and by 0.01 for the average anomaly correlation). Locally, there are differences between the skill values of the two assimilation experiments. These are related to differences in the skill of the assimilated retrieval products and how well these differences are captured by the observation errors specified in the data assimilation. (C) 2017 Elsevier Inc. All rights reserved.