The pursuit of superresolution (SR) with large upscaling factors, such as $8\times$ , for enhancing the spatial resolution of low-resolution (LR) remote sensing images is a persistent and challenging problem. To address this issue, we propose the progressive feature enhancement SR (PFESR) network with an $8\times$ upscaling factor. Given the limited high-frequency information provided by a single LR image, we propose an improved style transfer technology to generate auxiliary details that aid in the recovery of high-resolution (HR) images. Additionally, multiscale texture features are extracted through the visual geometry group (VGG) feature extraction (VFE) block. To efficiently fuse various features, we combine hard and soft attention mechanisms. Finally, we use a hierarchical fusion block to address the progressive fusion problem of multiple scale features. Experiments on three datasets demonstrate that our method achieves the state-of-the-art performance and exhibits good robustness in $8\times$ and higher scale SR tasks.