The remote sensing (RS) image super-resolution (SR) algorithm aims to reconstruct a high-resolution (HR) image with rich texture details from a given low-resolution (LR) image, improving the spatial resolution. It has been widely concerned in RS image processing and application. Most current deep-learning-based methods rely on paired training datasets. However, most datasets are often based on bicubic degradation. This single construction way limits the performance of the pretrained network. Moreover, SR is an ill-posed problem in that multiple SR images are constructed from a single LR input. This article proposes a region-aware network (RAN) for RS image SR to alleviate the above issues. First, we introduce the contrastive learning strategy to mine the latent degraded representation of the image and serve as the prior knowledge of the network. Considering the RS images are acquired in specific scenes that have apparent self-similarity. Then, we propose a region-aware module (RAM) based on attention mechanisms and the graph neural network to explore region information and cross-patch self-similarity. Extensive experiments have demonstrated that the proposed RAN adapts to RS image SR tasks with various degradations and performs better in constructing texture information.