A coastal zone is a special geographical zone that connects marine and terrestrial systems. Itis closely related to human existence and development. However, its natural and ecological environments are extraordinarily vulnerable and sensitive. Climate changes and human activities have large impacts on coastal zones, including the deterioration of the ecological environment. With technical advancement, remote sensing has become an important method in the geo-environmental monitoring of coastal zones, as well as in the planning, management, and protection of coastal zones. This paper reviews the main data sources, methods, and limitations of the applications of remote sensing techniques(i.e., land use/cover, soil quality, vegetation, coastal line, water color, water depth, underwater topography, and disaster) in the geo-environmental monitoring of coastal zones. The prospects for future development are also discussed. Moderate or low resolution(e.g., MODIS, Landsat TM/ETM+, and SPOT), hyperspectral resolution(e.g., ground-based ASD reflectance, Hyperion, Hymap, and CASI), and high resolution(e.g., Quickbird, WorldView, and Pleiades) remote sensing data have been widely used in the monitoring of land use/cover, soil quality, vegetation, coastal line, and water color in coastal zones. Airborne laser radar, microwave, and synthetic aperture radar(e.g., ALOS PALSAR and InSAR) data are mainly used in the monitoring of water depth, underwater topography, and disasters in coastal zones. Multi-source data fusion(e.g.,LiDAR-hyperspectral and high-resolution hyperspectral) provides a new method for improving monitoring accuracy. The classification and extraction or quantitative retrieval of land use/cover, soil quality, vegetation, coastal line, water color, water depth, underwater topography, and disasters are the main processes in the geo-environmental monitoring of coastal zones. The main methods for classification and extraction are maximum likelihood, vegetation index, support vector, artificial neural network, object-oriented, decision tree, and random forest. The main methods for retrieval are statistical regression, physical modeling, and semi-empirical modeling. The cloudy and rainy environment in coastal zones is the biggest limitation in high-quality optical imagery and the continuous monitoring of land use/cover, soil quality, vegetation, coastal line, and water color. The retrieval of coastal soil quality with airborne and satellitebased hyperspectral images and the retrieval of the biochemical parameters of coastal vegetation have received minimal attention. The universality of water color models is mainly affected by atmospheric correction and study area. The retrieval accuracy of water depth is not guaranteed owing to the indirect measurement of water depth. Acquiring remote sensing data at random times and sites in the presence of sudden and catastrophic incidents in coastal zones remains difficult. Finally, this study proposes the following research prospects to further develop and improve the geo-environmental monitoring of coastal zones with remote sensing techniques: strengthening the multidiscipline collaboration on research methodologies; developing multiple sensors and monitoring platforms for monitoring measures; focusing on multi-source data fusion and assimilation in data processing; emphasizing data mining, intelligence, and physical models in information extraction; and paying attention to the integrated management and sustainable development of coastal lines in information application.