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The objective of this study is to classify the land cover types and analyze the land cover trend by incorporating phenological variability throughout a range of natural ecosystems using time-series remotely sensed images. First, a breaks for additive seasonal and trend (BFAST) approach is used to extract the phenology information from the time series. Second, a dynamic time warping (DTW) approach is adopted to screen the additional interpreted samples used for training. Third, some ensemble learning classifiers and the support vector machine (SVM) are performed to classify the land cover types based on the BFAST-derived phenology components. Finally, some inter-annual phenological markers are extracted to facilitate the land cover trend analysis by taking the climate fluctuations and anthropogenic forcing into consideration. The experimental results with normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data collected by the Moderate Resolution Imaging Spectrometer (MODIS) indicate that the classification accuracy is significantly improved by using the phenology information and the phenological markers can lead to a better understanding of the regional land cover change.