As deep learning models advance, their use in Super Resolution (SR) tasks has become pivotal for enhancing remote sensing low-resolution (LR) satellite images. However, the decision-making processes within these neural networks remain opaque, especially in remote sensing applications where model transparency is critical. This thesis focuses on applying Explainable Artificial Intelligence (XAI) techniques, particularly Local Attribution Maps (LAMs), to analyze and interpret the internal behavior of both general-purpose and remote sensing specific SR neural networks. General-purpose models like SRGAN, EDSR, ESRT, and HAT, although highly effective in SR tasks, were originally designed for broader image enhancement challenges. In contrast, HSENet and MEN are tailored for the unique complexities of remote sensing, such as varied textures and intricate scene features.