Monitoring abrupt changes in satellite time series by seasonal confidence interval of regression residuals

Near real-time monitoring of abrupt changes in satellite time series is important for timely warning of land covers changes. Regression model-based method has been frequently used to detect abrupt change (outlier or anomaly) in time series data. Abrupt change is often determined by residuals test after regression. A simple and widely used residuals test technique is confidence interval (CI), which is often time-independent or constant in many studies. However, satellite time series data is characterized by seasonal variability and periodicity. Although the periodicity could be fitted well by a seasonal-trend regression model, the seasonal variability still remains in the residuals of the regression model. The seasonal variability would lead to less reliable results if abrupt changes are detected by a constant confidence interval (CCI). In order to improve the reliability of abrupt change monitoring in satellite time series, in this paper we develop a criterion namely seasonal confidence interval (SCI) of regression residuals. Experimental evaluations with some simulated and actual satellite time series data demonstrate better performance of the proposed SCI criterion than the CCI criterion for monitoring abrupt changes in satellite time series.