As a result of the increasingly convenient access to high temporal resolution data, and even video remote sensing data, a large amount of historical data have accumulated in recent years. Accordingly, change detection technology using remote sensing time series data has achieved rapid development and has become a hot research field in remote sensing, especially after the successful launch of "GF-4","Jilin No.1", and Skysat satellites. Thus, change detection research with time series remote sensing data has entered a brand new stage. This review systematically summarizes the research progress and application of Remote Sensing Series Data Change Detection（RSSDCD）. Considering the significance and advantage of applying time series analysis in change detection, we start this work by identifying the time series change detection methods in other fields. Then, according to the requirements of RSSDCD, we divide the methods into two categories: methods for anomaly detection for emergencies and methods for the detection of gradual and constant changes in land use/cover types. This review presents the latest progress and methods for these two types of purposes and presents discussions about their advantages and disadvantages. The remote sensing time series data exhibit the following characteristics: seasonality, instability, locality, multi-scale,time-space autocorrelation, multi-dimension, and huge quantity. This review introduces an anomaly detection method based on empirical mode decomposition and a land use/cover gradual change detection method based on hidden a Markov model. Instances for both approaches are offered as references for related research and application. A conclusion about the latest trends and existing issues in this field is drawn after tracking recent research on RSSDCD. Future works are also discussed.

time series;change detection;anomaly detection;land use/cover;empirical mode decomposition;hidden Markov model