Super-resolution reconstruction technology has found extensive applications across various fields, yet challenges persist in reconstructing remote sensing images. To balance local details and global structures in super-resolution remote sensing, we propose a novel model that integrates multiscale receptive fields with a multilevel hybrid transformer. The generator network combines multiscale receptive fields with a multilevel hybrid transformer architecture, progressively enhancing image features from low to high resolution, notably improving reconstruction quality and detail restoration. The discriminator network incorporates independent global, multiscale, and hierarchical discriminators for a comprehensive and refined evaluation of reconstructed remote sensing image quality, enhancing training stability and accelerating convergence. Experimental results on five publicly available datasets demonstrate that the proposed method achieves the highest structural similarity (0.988) and feature similarity index (0.993) in the 4× magnification task, surpassing state-of-the-art methods such as SRFormerV2 and SRTransGAN. Notably, the model showed remarkable improvement in FSIM, with reconstructed remote sensing images exhibiting finer textures and sharper features, providing robust support for subsequent remote sensing applications.