<p>Reference-based super-resolution (RefSR) technology holds significant potential for enhancing the spatial resolution of remote sensing images. However, existing methods still face notable limitations in the effectiveness of large-scale texture transfer and struggle to guarantee the fidelity of re-constructed content when local variations exist between low-resolution (LR) images and reference (Ref) images. To address these problems, we propose a novel Mamba and U-Net-based method for RefSR, named Mamba-UNet. First, the LR image is spectrally decomposed into high-frequency (HF) and low-frequency (LF) components. The HF component is then enhanced by a Mamba module with long-sequence modeling capability, which can adaptively transfer textures from any location in the Ref image, thereby effectively resolving the blurring problem in large-scale texture transfer. Second, an adaptive multi-source data fusion module is designed to dynamically evaluate the land-cover consistency between the LR and Ref images through a channel attention gating mechanism. Once a local mismatch is detected, this mechanism automatically reduces the weight of Ref features and strengthens the dominance of the LR image’s own low-frequency content, thus ensuring the fidelity of content reconstruction. Finally, the gated fused features are fed into an attention-augmented U-Net for high-quality image reconstruction. Experimental results demonstrate that compared to state-of-the-art RefSR methods, the proposed method exhibits superior reconstruction performance and stronger robustness in quantitative metrics and visual quality assessments on the SECOND and CNAM-CD datasets. The code is available at <a href="https://github.com/wzh226/AM-UNET.">https://github.com/wzh226/AM-UNET.</a></p>