A large amount of earth observation data with the high spatial and temporal resolution is employed in many earth science applications. The spatiotemporal image fusion method provides a feasible and economical solution for generating high spatiotemporal resolution data. However, some of the existing learning-based methods are poor in extracting deep image features and utilizing the detail features of high-resolution image. A spatiotemporal fusion method is proposed for remote sensing images based on multi-level feature compensation. It uses two branches to perform multi-level feature compensation and proposes a residual module fused with a channel attention mechanism as the basic unit of the network, which can extract and utilize the deep features of high-resolution input images in more detail. An edge loss is proposed based on the Laplacian operator, which saves the computational cost of pre-training and achieves a good fusion effect. The proposed method is experimentally evaluated by using Landsat and Moderate-resolution Imaging Spectroradiometer(MODIS) satellite images collected from two regions in Shandong and Guangdong. Experimental results show that the proposed method bears higher quality in both visual appearance and objective metrics.