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

World View

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 In SAR） data are mainly used in the monitoring of water depth

underwater topography

and disasters in coastal zones. Multi-source data fusion（e.g.

Li DAR-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.