Soil moisture is one of the main physical quantities with a key role in water resources accounting research. Due to the spatial nature of this quantity and limitations of ground-based or remote sensing technology, the reliability of soil moisture data is of practical concern. Blending multiple sources of soil moisture data helps to combine the strengths and mitigate the weaknesses exhibited by each individual source. We build on the Bayesian hierarchical spatial model by Chiu and Lehmann (2011) to incorporate multiple sources of remotely sensed data on soil moisture. This model-based approach accounts for covariates, and can handle the various spatial resolutions among the data sources without manual aggregation or resampling. This unified approach also provides insights into the reliability (uncertainty) of each data source and of the blended product. We also briefly introduce an extension for model-based spatial aggregation of areal data.