With the rapid development of convolutional neural networks (CNNs) and Transformer models, significant progress has been made in remote sensing image super-resolution (RSSR) reconstruction tasks. However, existing methods have limitations in effectively handling multi-scale object features and fail to fully explore the implicit correlations between channel and spatial dimensions, thus restricting further improvements in reconstruction performance. To address these issues, this paper proposes an adaptive dual-domain attention network (ADAN). The network integrates self-attention information from both channel and spatial domains to enhance feature extraction capabilities. A multi-scale feed-forward network (MSFFN) is designed to capture rich multi-scale features. At the same time, an innovative gated convolutional module is introduced to further enhance the representation of local features. The network adopts a U-shaped backbone structure, enabling efficient multi-level feature fusion. Experimental results on multiple publicly available remote sensing datasets show that the proposed ADAN method significantly outperforms state-of-the-art approaches in terms of quantitative metrics (e.g., PSNR and SSIM) and visual quality. These results validate the effectiveness and superiority of ADAN, providing novel insights and technical approaches for remote sensing image super-resolution reconstruction.