MLTRANS: Machine learning models have shown great success in learning complex patterns such as the spatial distribution of soil properties, making predictions about areas not covered. In contrast, the ability to apply what is learned to other domains is poorly developed. To date, the models have very limited applicability to domains outside the training learning environment. Similar to empirical regressions, the rule sets, e.g. for decision tree methods like Random Forest, only apply to the range of values covered by training data. Training data as high quality and extensive as possible for each additional domain is needed again. Advances in Deep Learning (DL), e.g. Convolutional Neural Networks, Transfer Learning, and combined approaches in Feature Selection (FS) offer here extended possibilities to constrain dimensionality, especially for smaller datasets, to minimize overfitting to the training data, and to improve transfer to adjacent domains. In this application, we incorporate these developments and attempt to predict soil properties for areas outside the learning environment.