Novel view synthesis of remote sensing scenes from satellite images is a meaningful but challenging task. Due to the wide temporal span of image acquisition, satellite image collections often exhibit significant appearance variations, such as seasonal changes and shadow movements, as well as transient objects, making it difficult to reconstruct the original scene accurately. Previous work has noted that a large amount of image variation in satellite images is caused by changing light conditions. To address this, researchers have proposed incorporating the direction of solar rays into neural radiance fields (NeRF) to model the amount of sunlight reaching each point in the scene. However, this approach fails to effectively account for seasonal variations and suffers from a long training time and slow rendering speeds due to the need to evaluate numerous samples from the radiance field for each pixel. To achieve fast, efficient, and high-quality novel view synthesis for multi-temporal satellite scenes, we propose SatGS, a novel method that leverages 3D Gaussian points for scene reconstruction with an appearance-adaptive adjustment strategy. This strategy enables our model to adaptively adjust the seasonal appearance features and shadow regions of the rendered images based on the appearance characteristics of the training images and solar angles. Additionally, the impact of transient objects is mitigated through the use of visibility maps and uncertainty optimization. Experiments conducted on WorldView-3 images demonstrate that SatGS not only renders superior image quality compared to existing State-of-the-Art methods but also surpasses them in rendering speed, showcasing its potential for practical applications in remote sensing.