Most of the existing methods of remote sensing image super-resolution are unable to fully explore the self-similarity information at hybrid scales and the correlation between cross-scale regions. Moreover， these methods ignore the ability of the frequency domain to perceive the high-frequency information of the images. To address this problem， a Spatial Adaptation and Frequency Fusion Network （SAF²Net） was proposed. Firstly， SAF²Net introduced a hybrid-scale spatially-adaptive feature modulation， which adopted a feature pyramid-like approach to obtain discriminative features at different scales and enriched the expression ability of multi-scale features. Subsequently， a global multi-scale field selection block was designed to extract the correlation features of cross-scale regions. On this basis， a spatial adaptively selection block and a frequency separation selection block were introduced to fuse space-frequency complementary information to enhance local features， improving the model's ability to model the high-frequency content of images. Multiple sets of experiments are conducted on two remote sensing image datasets， which indicates that the quantitative evaluation metrics obtained by SAF²Net outperform those of other comparative methods. Taking the UCMerced dataset with 3 times super-resolution as an example， the proposed method improves PSNR and SSIM by 0.11 dB and 0.003 3， respectively， in compared with the next best method HAUNet. In terms of the subjective visual quality， SAF²Net is able to recover more clear texture details. The experimental results demonstrate that the SAF²Net proposed is capable of mining the hybrid-scale global information from two different perspectives as well as fusing the space-frequency complementary features effectively， which exhibits competitive performance in the remote sensing image super-resolution task.