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Global Level-3 surface soil moisture (SM) maps derived from the passive microwave SMOS (Soil Moisture and Ocean Salinity) observations at L-band have recently been released. In this study, a comparative analysis of this Level 3 product (referred to as SMOSL3) along with another Surface SM (SSM) product derived from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) at C-band is presented (this latter product is referred to as AMSRM). SM-DAS-2, a SSM product produced by the European Centre for Medium Range Weather Forecasts (ECMWF) Land Data Assimilation System (LDAS) was used to monitor both SMOSL3 and AMSRM qualities. The present study was carried out from 03/2010 to 09/2011, a period during which both SMOS and AMSR-E products were available at global scale. Three statistical metrics were used for the evaluation; the correlation coefficient (R), the Root Mean Squared Difference (RMSD), and the bias. Results were analysed using maps of biomes and Leaf Area Index (LAI). It is shown that both SMOSL3 and AMSRM captured well the spatio-temporal variability of SM-DAS-2 for most of the biomes. In terms of correlation values, the SMOSL3 product was found to better capture the SSM temporal dynamics in highly vegetated biomes ("tropical humid", "temperate humid", etc.) while best results for AMSRM were obtained over arid and semi-arid biomes ("desert temperate", "desert tropical", etc.). Finally, we showed that the accuracy of the remotely sensed SSM products is strongly related to LAI. Both the SMOSL3 and AMSRM (marginally better) SSM products correlated well with the SM-DAS-2 product over regions with sparse vegetation for values of LAI <= 1 (these regions represent almost 50% of the pixels considered in this global study). In regions where LAI>1, SMOSL3 showed better correlations with SM-DAS-2 than AMSRM: SMOSL3 had a consistent performance up to LAI = 6, whereas the AMSRM performance deteriorated with increasing values of LAI. This study reveals that SMOS and AMSR-E complement one another in monitoring SSM over a wide range in conditions of vegetation density and that there are valuable satellite observed SSM data records over more than 10 years, which can be used to study land atmosphere processes. (C) 2014 Elsevier Inc. All rights reserved.