Spatiotemporal fusion is a simple, feasible, and economical method for balancing the temporal and spatial resolutions of satellite images. However, the current spatiotemporal fusion methods have some disadvantages, such as their insufficient feature extraction abilities and unsatisfactory fusion image effects. To obtain higher-quality spatiotemporal fusion images, a spatiotemporal data fusion method based on deep learning is proposed. This method combines an attention mechanism and a residual learning strategy to design an asymmetric spatial channel attention residual network (SCRNet). For different input images, the SCRNet extracts rich feature information to fuse high-quality images in a more emphatic and scientific manner than other approaches. Specifically, we design an efficient and flexible spatial channel attention mechanism that not only focuses on spatial information but also takes channel information into account. The optimized residual network can further enhance the ability of the network to extract feature information. In addition, we design a compound loss function and propose an edge loss to further enrich the extracted feature information and improve the resulting image quality. Experimental results on two datasets show that the proposed method outperforms existing fusion methods in subjective and objective evaluations.