Soil moisture (SM) is a very important physical parameter in land surface processes. The low-resolution microwave products are extremely useful for large-scale studies, but are limited in their ability to resolve landscape-level variability. In this study, to overcome this limitation, we adopted three different deep learning based downscaling methods and performed a comparative analysis. Firstly, a downscaling method based on a deep belief network (DBN) model with a fully connected structure was employed to fit the complex nonlinear relationship between soil moisture and the auxiliary surface parameters. Then, to improve this simple point-to-point relationship, a neighborhood constraint based improved DBN model was developed to consider the effect of local spatial correlation among the surface factors on soil moisture. Finally, to maintain the integrity of the spatial structure in the process of model construction, a residual network (ResNet) model consisting of several residual dense blocks (RDBs) was introduced to use convolutions to obtain more complete and powerful spatial feature maps. The Tibetan Plateau was selected as the study area because of its importance to the global water cycle. The four soil moisture monitoring networks in the research area were used to verify the effectiveness of the proposed methods in the downscaling of soil moisture products, and four quantitative indicators were used to evaluate the effectiveness of the methods. From the verified results, it is shown that the three deep learning methods can maintain a higher correlation and better reduce the bias, on the whole, and the ResNet model, in particular, shows a higher stability. From the perspective of the spatial patterns and details, when compared to back-propagation neural network (BPNN) and random forest (RF) models, the three deep learning methods can provide more feasible spatial patterns and details, as well as reducing the uncertainty in the central regions of the Tibetan Plateau. The ResNet model shows a superior ability to enhance the texture details with lower uncertainty, due to the fact that it can meet the requirements of fitting the soil moisture to complex surfaces. Furthermore, the results of the ResNet model can also effectively capture the temporal changes of the microwave soil moisture product well.