The calculation of surface depth based on multiview stereo (MVS) satellite imagery is of significant importance in fields such as military and surveying. The challenge in extracting depth information from satellite imagery lies in the fact that these images often exhibit similar colors, necessitating the development of algorithms that can integrate shape and texture information. Moreover, the application of classical convolutional neural network (CNN) MVS is limited by its inability to capture long-range terrain relationships, which presents a bottleneck in existing surface depth estimation algorithms. To address the above problems, we propose the Distribution Contrast Network for Surface Depth Estimation from Satellite MultiView Stereo Images (DC-SatMVS), a novel satellite MVS network. In order to learn short-range and long-range features, we designed separate CNN and ViT branches. To emphasize the importance of shape and texture, we propose the Distribution Contrast Loss mechanism. This mechanism supervises the model training based on the similarity between the predicted depth and the ground truth depth distribution. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance. We produce a remarkable 18.14% reduction in root mean square error compared to the Sat-MVSF on the WHU-TLC dataset. To validate the generalization performance of our framework, we trained and tested it on the DTU dataset, a common MVS dataset, and achieve SOTA results in this dataset as well.