The increasing demand for soil information has led to the rapid development of Digital Soil Mapping (DSM) products. As a consequence, multiple soil maps are sometimes available for a particular area. Rather than selecting the best map, model ensemble offers a way to capitalize on existing soil information, and to improve the map accuracy. In this study we ensemble four topsoil texture maps of France with different resolution made by different organizations at the national, European, and global scale. We investigated two methods of model ensemble: the Granger-Ramanathan (GR) and Variance-Weighted (VW) methods. Ensemble methods based on area stratification were also tested to take into account local soil information. We also assessed the impact of the number of calibration points on the evaluation indicators. Both ensemble methods improved the accuracy of the map compared to the best of the primary maps, while the GR method outperformed the VW method. We found that the different stratification strategies did not improve the accuracy significantly when compared to the global methods. Finally, we showed that a relatively low number of calibration points is required in the merging process if the sampling is well designed. This study demonstrates that digital soil mapping products at various scales from various data sources can be combined with the ensemble method taking advantage of all existing efforts and taking care of harmonization issues. •Two ensemble methods were tested to combine soil texture maps: Granger-Ramanathan and variance-weighted•We successfully merged maps from different scales to improve maps at a national scale•Stratification did not result in improved accuracy•A reasonable number of calibration points was necessary to apply ensembles in other regions