A methodology to conduct a joint analysis of modeled soil moisture fields from the Joint UK Land Environment Simulator (JULES) and a data set of multiwavelength observations is presented. It consists of building a statistical model capturing the relationships between the land surface model estimates and the satellite observations, and then using the satellite observations (mapped into soil moisture predictions by the statistical model) to evaluate the fields estimated by the land surface model. Two statistical models are tested and predict very similar soil moisture (global correlation and root-mean-square deviation (RMSD) of ~ 0.98 and ~ 0.02 m3/m3). A characterization of prediction uncertainty shows errors ranging between 0.01 and 0.10 m3/m3, depending on biome and season. The satellite prediction and JULES soil moisture agree relatively well (global correlation and RMSD of ~ 0.92 and ~ 0.05 m3/m3), but for some regions and periods, clear differences exist. Conducted tests modifying either the predicted soil moisture or the JULES estimates show that this methodology can effectively change soil moisture toward more correct values. It can then be expected that some of the differences are the result of the satellite information modifying the modeled soil moisture fields toward more realistic values. However, proving this is difficult given the present uncertainties in modeled and observed global soil moisture products.