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Due to the lack of paired datasets in the field of remote sensing image super-resolution reconstruction,current methods obtain low resolution images by bicubic interpolation,in which the degradation model is too idealized,resulting in unsatisfied reconstruction results in real low resolution remote sensing images situations.This paper proposes a super resolution reconstruction algorithm for real remote sensing images.For datasets that lack paired images,this paper builds a more reasonable degradation model,in which a prior of degradation in the imaging process (like blur,noise,down sampling,etc.) is randomly shuffled to generate realistic low-resolution images for training,simulating the generation process of low-resolution remote sensing images.Also,this paper improves a reconstruction algorithm based on generative adversarial networks(GAN) to enhance texture details by introducing attention mechanism.Experiments on UC Merced dataset show a promotion of 1.407 1 dB/0.067 2,0.821 1 dB/0.023 5 compared with ESRGAN and RCAN on the evaluation index of PSNR/SSIM,experiments on Alsat2B dataset promote 1.758 4 dB/0.048 5 compared with the baseline,which show the effective of the degradation model and reconstruction architecture.