This paper presents a two-stage spatiotemporal fusion method for obtaining dense remote sensing images with both high spatial and temporal resolution. Considering the large resolution differences between fine- and coarse-resolution images, the proposed method is implemented in two stages. In the first stage, the input fine- and coarse-resolution images are preprocessed to the same intermediate resolution images, respectively. Then, a linear interpolation model is introduced to fuse these resampled images for predicting preliminary fusion results. In the second stage, a residual dense network is used to learn the nonlinear mapping between the preliminary fusion results and the real fine-resolution data to reconstruct the final fine-resolution data. Two data sets with different land surface types are employed to test the performance of the proposed method. Experimental results show that the proposed method is advantageous in such areas with phenological changes, and even for the data sets with land cover changes being the main type, it still has a good ability to predict spatial structure information of images.