This paper evaluates a methodology to integrate surface soil moisture retrievals from SMOS and AMSR-E into a single, consistent dataset retrieved by the Land Parameter Retrieval Model (LPRM). In a first step, the SMOS LPRM soil moisture retrievals were used as the baseline for optimizing the internal parameterization (i.e. surface roughness and single scattering albedo) of the AMSR-E LPRM retrievals. Secondly, to overcome the uniqueness of these datasets a linear scaling approach was applied resulting in a consistent soil moisture dataset. The new parameter set from the first step is similar for the two (low) frequencies of AMSR-E (i.e. C- and X-band) further improving their inter-comparability for both soil moisture and vegetation optical depth. Soil moisture retrievals from these AMSR-E frequencies were globally merged based on the availability of brightness temperatures that are free from RFI contamination (resulting in AMSR-E LPRMN). This new product was evaluated against both the SMOS LPRM product in the overlapping period (July 2010 to October 2011), as well as the standard, publicly available AMSR-E LPRM dataset (AMSR-E LPRMV3) for an almost 9 year period (January 2003 to October 2011). For the overlapping period, the AMSR-E and SMOS LPRM products show high temporal correlation coefficients (0.60 < R < 0.90) and low root mean square errors (rmse < 0.04 m3 m− 3) for NDVI values up to 0.60. Their agreement tends to drop over the well-known challenging areas such as the arctic region and tropical rainforest. A detailed evaluation over in situ sites from 5 in situ networks worldwide showed that AMSR-E LPRMN often outperforms SMOS LPRM in sparsely vegetated areas, with generally higher correlation coefficients in areas with NDVI < 0.3, and in general a lower unbiased rmse (ubrmse). In line with theoretical expectations, SMOS LPRM outperforms the AMSR-E LPRM product over the more densely vegetated areas. The newly developed AMSR-E LPRMN product was also compared against AMSR-E LPRMV3, revealing a significant increase (from 0.48 to 0.55) in temporal correlation coefficient over 16 in situ networks. This finding was confirmed through a large scale (50°N–50°S) precipitation based verification technique, the so-called Rvalue, which shows a superior performance of the newly developed AMSR-E LPRMN product. Additionally, the linear scaling of AMSR-E LPRMN to the SMOS LPRM leads to further reducing the ubrmse from 0.09 to 0.06 m3 m− 3 and the average bias from 0.14 to 0.00 m3 m− 3 over these stations. The AMSR-E LPRMN was furthermore compared against the top layer of two re-analysis models (i.e. from the Modern-Era Retrospective analysis for Research and Applications-Land and ERA-Interim/Land models) generally demonstrating increased correlation coefficients and reduced ubrmse with the exception of the challenging areas. As a result, this study shows the significant potential of SMOS LPRM to be a successful integrator to build a long term soil moisture record based on multiple passive microwave sensors.