In response to the high hardware requirements associated with the deployment of current deep learning-based remote sensing image super-resolution reconstruction models， this paper presented a lightweight， re-parameterized residual feature remote sensing image super-resolution reconstruction network. Firstly， a residual local feature module was designed using re-parameterization to effectively extract local image features. Simultaneously considering the occurrence of similar features within images， a lightweight global context module was devised to associate similar features in images， enhancing the network's feature representation capability. The channel compression rate of this module was adjusted to reduce the model's parameter count and improve its performance. Finally， a multi-level feature fusion module was employed before the upsampling module to aggregate deep features and generate a more comprehensive feature representation. Tested on the UC Merced remote sensing dataset， this algorithm exhibits a parameter count of 539 K for ×3 super-resolution， a PSNR of 30.01 dB， a SSIM of 0.844 9， and an inference time of 0.010 s. In comparison， the HSENet algorithm has a parameter count of 5 470 K， a PSNR of 30.00 dB， an SSIM of 0.842 0， and an inference time of 0.059 s. Experimental results demonstrate that this algorithm outperforms the HSENet algorithm， featuring fewer parameters， faster execution， and notable improvements in PSNR and SSIM. Testing on the DIV2K natural image dataset reveals that this algorithm exhibits advantages in PSNR and SSIM compared to other algorithms， demonstrating its strong generalization capability.