## 1 Introduction  
The forest is an important subsystem in the ecosystem that provides a variety of valuable timber, raw materials and other supplies necessary to human's production and life and ecological balance. China, despite its vast territory, lacks forests and greenery, which makes it ecologically fragile. Since the construction of the Three Gorges Dam, the ecological environment on both sides of the Three Gorges reservoir has been changing continuously owing to various natural and human factors. Therefore, it is of great significance to use long-term remote sensing time series imagery to dynamically monitor the forest land resources on both sides of the reservoir. Long-term time series analysis based on remote sensing have been proven extremely useful in monitoring forest resources changes [1; 2; 3; 4]. Currently, free satellite images are becoming increasing available to the public. For example, beginning in 2008, all Landsat data can be obtained at no charge. Data from a new satellite, Sentinel-2, are also free of charge now [5]. As a result, the study on time series-based analysis has become increasingly popular in the past decade. Landsat data, with its long-term data archive and good data quality, is the most commonly used data for long-term surface monitoring [4; 6]. However, Landsat data alone is not sufficient for appropriate time series analysis due to some issues. For example, the Landsat satellites revisit the same area every 16 days, which makesthe time interval of the image time series too long. In addition, in some areas, such as the tropics, Landsat satellite data may be contaminated by clouds and shadows. These issues make the available image sequences sparse and reduce the accuracy of dynamic monitoring [6; 7]. Multi-source data fusion is a feasible solution to the aforementioned problems. A number of methods for fusing the multi-source data [8; 9; 10; 11; 12; 13] have been introduced in recent years and successfully applied to detect forest change caused by natural or human factors [1; 14; 11]. Because Sentinel-2 data are free of charge at the global scale and have similar wavelengths to Landsat data, the area-to-point regression kriging (ATPRK)-based image fusion approach is implemented on Landsat 8 Operational Land Imager (OLI) and Sentinel-2 data to coordinate their spatial resolutions for continuous global monitoring [12]. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [8], developed in 2006 for predicting daily surface reflectance with Landsat spatial resolution and Moderate Resolution Imaging Spectroradiometer (MODIS) temporal frequency, has been widely adopted and became the foundation for many other methods [9; 15]. Although recent results of the STARFM method suggest new opportunities for producing remotely sensed data with both high spatial and temporal resolutions, such spatio-temporal fusion methods usually require at least one pair of Landsat-MODIS images (or images from other similar instruments) acquired on the same day to guide the downscaling process of MODIS (or other coarse-resolution data) on other days [8]. This remains a challenge in some regions, such as cloudy areas. In addition, there might be significant uncertainties in the process of downscaling from the observed spatial resolution of 500 m to the target resolution of 30 m. As cloud cover limits optical satellite time series observations, a pixel-based Multi-sensor Time-series correlation and Fusion approach (MulTiFuse) [11] that exploits the full observation density of optical and Synthetic Aperture Radar (SAR) time series has been presented to improve tropical forest monitoring. It has provided an opportunity to use the medium resolution optical and SAR satellite imagery in a beneficial way for improved forest monitoring in tropical regions. Although MulTiFuse is originally applied to Landsat Normalized Difference Vegetation Index (NDVI) and PALSAR data, it is not restricted to medium resolution optical and SAR data and can be used to fuse a variety of time series from other sensors [11].