Due to the different acquisition conditions, large variations in the feature distributions of two temporal domains generally exist, known as temporal domain shift. The temporal domain shift is primarily influenced by coupled dual-factor: global style variations (such as illumination and weather conditions) and local style variations (such as the inherent phenological properties of land cover classes). In this article, we first formulate the temporal domain shift problem as an issue of dual-factor coupled interference on feature distributions in remote sensing (RS) community. To address this issue, we propose a semantic-guided style transfer (SGST) framework seamlessly integrating global feature alignment with local feature semantic matching. We use an adaptive segmentation model to provide pseudosegmentation maps and feed them into the style transfer model as semantic guidance. Under semantic guidance, a semantic-constrained style normalization (SCSN) module is designed to achieve style transfer at both global and local levels. Furthermore, a dual learning approach is introduced to make the style transfer model and the adaptive segmentation model promote each other. As a result, the style transfer model generates high-quality style-transferred images, and the adaptive segmentation model progressively predicts more accurate pseudosegmentation maps. Extensive experiments demonstrate the superiority of our proposed framework over state-of-the-art methods in terms of both perceptual quality and quantitative performance.