To address the inadequacy of existing remote sensing image super-resolution reconstruction models in long-term feature similarity and multi-scale feature relevance, this study proposes a novel remote sensing image super-resolution reconstruction algorithm based on a cross-scale hybrid attention mechanism. Initially, the study introduces a global layer attention (GLA) mechanism and employs layer-wise attention to weight and merge global features across different levels, thereby modeling the extended dependency between low-resolution and high-resolution image features. Concurrently, it designs a cross-scale local attention (CSLA) mechanism to identify and integrate local information patches in multi-scale low-resolution feature maps that correspond with high-resolution images, enhancing the model’s ability to restore image details. Finally, the study proposes a local information-aware loss function to guide the image reconstruction process, further improving the visual quality and detail preservation of the reconstructed images. Experiments on UC-Merced datasets demonstrate that the proposed method outperforms most mainstream methods in terms of average PSNR/SSIM across three magnification factors and exhibits superior quality and detail preservation in visual results.