This study investigates various super-resolution (SR) methods for enhancing the spatial resolution of satellite imagery. Traditional interpolation techniques, such as nearest-neighbor, bilinear, bicubic, and Lanczos, are compared with modern deep learning-based models, including EDSR, ESPCN, FSRCNN, and LapSRN. The research demonstrates that deep learning approaches outperform traditional methods in terms of image quality, as indicated by higher PSNR and SSIM values. Additionally, the integration of geographic information systems (GIS) data into the SRGAN framework is explored, further improving image reconstruction by processing collateral data with residual scaling in RRDB blocks. The results show that combining advanced SR methods with optimized computational resources provides significant improvements in satellite image resolution, making it highly relevant for environmental monitoring, urban planning, and disaster management. Future research should focus on optimizing these models for specific applications and exploring emerging technologies for further enhancements.