Because the reconstructed images of existing remote sensing image super-resolution algorithms have problems such as lack of texture information and loss of high-frequency details, we propose a remote sensing image super-resolution reconstruction algorithm based on a multiscale residual network. First, in the shallow feature extraction stage, we design a tandem convolution group containing different scale convolution kernels to improve the model’s ability to extract shallow information. Second, we design an enhanced attention residual block to extract deep features so that the model can capture more texture information. To further improve the performance of the model, during the feature enhancement stage, we design a pyramid-polarized attention convolution module, which uses a more efficient attention mechanism and pyramid convolution to capture long-distance dependencies in the image. To enhance the stability of the model, we use a Charbonnier loss function based on pixel loss. Finally, we use the Adan optimizer to optimize the model to improve the convergence of the model and achieve better reconstruction results. The experimental results show that our algorithm can better reconstruct the texture detail of remote sensing images and, compared with existing super-resolution reconstruction algorithms, the proposed method can achieve better subjective visual effects in multiple remote sensing image datasets.