###### Abstract
Landsat time series at high temporal resolution have been used to monitor the areas of land change at a regional scale, which has accurately reflected the forest [15], wetland [16] and coastline [17] changes in the long term. In addition, the Landsat time series could provide information on the ecosystem cover and condition, which helps the understanding of the ecosystem function, resilience and dynamics [18]. Nevertheless, the research of the long-term evolution of the wetland ecosystem in subtropical regions has been seriously hampered by the insufficient high-quality and long-term continuous wetland change monitoring data, because of frequent cloudy and rainy weather. To get high spatial and temporal resolution data, a number of Spatial and Temporal satellite Image Fusion (STIF) models have been developed to blend the MODIS (coarse resolution and short interval) with Landsat images (finer resolution and long interval), such as the Flexible Spatiotemporal Data Fusion (FSDAF) model [19], the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) model [20], the enhance STARFM (ESTARFM) model [21] and the Robust Adaptive Spatial and Temporal Image Fusion model [22]. The STARFM has been one of the most widely used spatial-temporal fusion models, which generates high spatial and temporal resolution images at the prediction time based on the input image pairs on the base date. It considers the influence of the spatial distance and spectral similarity of neighboring pixels, as well as the temporal variation [23]. But the STARFM may cause prediction errors in heterogeneous landscapes with rapid land surface dynamics (e.g., Dongting Lake wetland), as the base images of the STARFM cannot capture transient or sudden changes of landcover Besides, the STARFM requires the temporal variation information of pure pixels for fusion. If there is no pure pixel in the moving window, the fusion quality will be poor. The ESTARFM and its modified version (i.e., mESTARFM) have been proposed to reduce the prediction errors induced by searching spectrally similar neighbor pixels in the STARFM [21; 23]. However, the second problem results in collinearity in the equation system, which makes the fused images cannot be used in continuous dynamics monitoring of heterogeneous wetland. The FSDAF model integrates unmixing based methods, spatial interpolation, and the STARFM into one framework, which has better capability for monitoring dynamic disturbance in heterogeneous area and provides a new possibility for long-term dynamic monitoring [19]. How to accurately and effectively identify the vegetation disturbance with different durations and spatial distribution is challenging and critical. Conventionally, post-classification comparison [24; 25; 26; 27], image differencing- [28] and transformation-based methods [29], were used to monitor the vegetation disturbance and land cover change based on one pair of remote sensing images across a large timescale. Most of these methods entail high quality images and complex data preprocessing, which cannot be always available in the vegetation monitoring, because of the seasonal phenology of vegetation. Furthermore, these methods ignore ecosystem changes and temporal context and regularity, as they extract the changes by comparing two time-phase images. Therefore, many temporal decomposition techniques have been proposed to track the temporal behaviors of vegetation and account for the abrupt, seasonal and trend changes in the ecosystem. For instance, the Landsat-based detection of trends in disturbance and recovery (LandTrendr) method uses Landsat time series data to segment the trend signals for change detection based on a piecewise linear model [30]. Breaks for additive seasonal and trend (BFAST) [31] and detecting breakpoints and estimating segments in trend (DBEST) [32] algorithms were used successively to extract the periodic variations and trend of time series and identify the abrupt changes by the Seasonal-Trend decomposition procedure based on Loess.