The field of remote sensing is witnessing a significant shift towards more versatile and efficient foundation models, particularly tailored for diverse atmospheric conditions and rapid response scenarios. Innovations in pre-trained models are addressing the limitations of high spatial resolution, cloud-free imagery by incorporating multispectral data and atmospheric corrections. These models, such as SatVision-TOA, are demonstrating superior performance in tasks like cloud retrieval and land surface monitoring, thanks to their ability to learn from a variety of atmospheric and aerosol conditions. Additionally, there is a growing emphasis on onboard AI capabilities, enabling near-real-time data analysis and rapid fine-tuning of models in decentralized satellite networks, as exemplified by the use of MobileSAM in disaster response scenarios. This approach not only reduces data transmission delays but also enhances the model's adaptability under orbital constraints. Furthermore, advancements in computational efficiency and interpretability are being driven by novel approaches inspired by physical processes, such as heat conduction, which are being applied to high-resolution remote sensing images to improve model performance and reduce computational overhead. Lastly, the optimization of satellite systems for dynamic natural disaster monitoring, such as tropical cyclones, is progressing with the integration of agility and reconfigurability in satellite constellations, leading to more effective and flexible observation strategies.