From text analysis to image interpretation， the Topic Model （TM） consistently plays a pivotal role. With its robust semantic mining capabilities， topic model can effectively capture latent spectral and spatial information from Remote Sensing （RS） images. Recent years have seen the widespread adoption of topic models to address challenges in RS image interpretation， including semantic segmentation， target detection， and scene classification. Thus， clarifying and summarizing the present application status of topic models in remote sensing imagery is pivotal for advancing remote sensing image interpretation technology. This paper initially presents the foundational theory of topic models， followed by a systematic overview of their typical applications in remote sensing imagery. In addition， experimental comparisons and analyses are performed across various typical remote sensing image interpretation tasks， illustrating the extensive applicability of topic models in the realm of remote sensing and the efficacy of distinct topic models in enhancing our comprehension of remote sensing imagery. Subsequently， we have outlined the limitations of topic models and explored the potential and prospects of integrating them with deep learning.