## 6 Strengths and Limitations of SCD  
The increasing availability of dense time series satellite images makes it possible to detect land-cover changes at improved temporal resolutions. Most previous change detection methods are based on bi-temporal or multi-year image data, which can identify changes only at the resolution of years. The proposed SCD method is designed for dense time series and it can detect land-cover changes at the resolution of days. Traditional change detection methods use annual images to avoid seasonal variations, which cannot be directly applied to dense time series. In contrast, SCD accounts for seasonal variations in dense time series and differentiate land-cover change from phenological change in a year. In addition, like BFAST, SCD can detect a variety of land-cover changes rather than focusing on forest changes as in previous studies using dense time series of images (e.g., [6,9]). The SCD method detects change dates by analyzing time series differences on yearly and 16-day scales coherently. It detects the years that include land-cover changes before detecting change dates by applying a two-sample KS test, which can reduce error in the time of detected change to less than a year. In addition, the SCD method does not require much prior information. The method can be applied to observations spanning any two consecutive years to detect change dates in the second year, regardless of the starting time point; observation records of full year in each one is not required though observations spanning over half a year is necessary to guarantee the accuracy. This is advantageous for detecting change dates in time series with continuously updated observations. In addition, the parameter settings do not  
Figure 8: Maps of detected changes from 2000 to 2012 generated by the proposed method (**a**) and BFAST (**b**). The extent of the map corresponds to the sub-area as shown in Figure 1. The color of a pixel is coded by its value according to the color bar on the bottom left. The pixel values equal the number of detected changes during the entire time period. Zero means no change. need to be calibrated for different land-cover classes or different time periods. Lastly, SCD is computationally efficient by using a hierarchical approach and thus can be easily applied to large data sets. The two parameters are critical for detecting change dates accurately and can be determined through a training process. The significance level for a KS test determines whether or not changes occurred within a year. The result of the KS test directly affects the accuracy of the number of detected changes and indirectly influences the accuracy of the time of detected change (in which year a change occurred). When a time series has great intra-annual variation, such as crops with rotation or double cropping, accurately detecting land-cover change will be challenging. Changes are sensitive to the significance level in such cases. The other parameter is the scale factor used to determine the change dates in a year. Using the maximum of the absolute difference between two years' records, it defines the threshold at which a land-cover change can be detected. The value of the scale factor is the same for all time series as selected by the training data, but the maximum is unique for different years or time series. Therefore, the threshold is adaptive to the characteristics of a particular time series in a certain year. The idea of studying the difference between two time series is borrowed from the bands-difference method for traditional change detection. In the SCD, the threshold is used to determine change points in the time domain. The training samples used to determine the parameters in this study consisted of only simulated time series. Real time series with reference information may better serve for training but are very limited in terms of sample size.