## 1 Introduction  
As remote sensing satellites can image Earth periodically, time series remote-sensing data analysis has become a main technique for many remote-sensing applications. Remote-sensing data with coarse spatial resolution [1; 2; 3; 4] are the main data sources for time series analysis. The temporal resolution of coarse spatial resolution data is one or two days. The high temporal resolution made them very suitable for time series analysis. Those data were widely used in many fields, like marine environment monitoring [5], crop area mapping and production estimation [6, 7], land cover and land change [8, 9, 10], vegetation phenology monitoring [11, 12], climate change [13], urban dynamic monitoring [14], and ecological dynamics monitoring [15], using time series monitoring analysis approaches. However, the spatial resolution of those data is coarser than 250 m, so that the recorded signals generally represent a mixture of land cover types, which are not suitable for fine time series analysis. Landsat data is another kind of data widely used in time series analysis. The spatial resolution of Landsat data is 30 m. The high spatial resolution made it very suitable in time series analysis in regional scale. Landsat data was also successfully used in many fields, such as land cover and land change [16, 17, 18, 19], urban dynamics monitoring [20], water environment monitoring [21], and interannual changes monitoring of lake area [22], using time series monitoring analysis technology. However, the returning cycles of Landsat data are 16 days and the probability of acquiring cloud-free images for a given time can be very low [23]. Thus, there are a lack of sufficient valid observations to build up high spatiotemporal resolution time series. With the recent launch of new satellites and the development of data fusion approaches, we are entering a new era of high spatiotemporal resolution remote sensing. In recent years, China has launched two moderate-resolution satellites, the Huanjing satellite constellation (HJ) and Gaofen satellite no. 1 (GF-1). These satellites belong to a new wide field-of-view class of satellites that can scan the surface with a width larger than 700 km. The observations cover the entire earth with an interval of two or four days and a spatial resolution of higher than 30 m. Thus, the HJ and GF-1 satellites enable the establishment of high temporal and spatial resolution time series. The data-fusion approach is to enhance the high spatial and temporal remote-sensing data. Recently, several data fusion approaches have been proposed to combine moderate-resolution data with high temporal and coarse spatial resolution data to generate daily moderate-resolution data. These approaches have been widely used in monitoring land surface and environmental processes. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) proposed by [PERSON] _et al._[24] is the most widely used model for blending Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. It was applied successfully in the production of daily land surface temperature data, forest disturbance mapping, and studies of environment monitoring [25, 26, 27, 28, 29]. However the STARFM method showed a low accuracy in complex heterogeneous areas. To address this problem, [PERSON] _et al._[30] proposed an enhanced STARFM (ESTARFM). The accuracy of STARFM and ESTARFM were assessed by [PERSON] _et al._[31]. Data fusion was also built up on linear mixed models [32, 33, 34]. The Spatial and Temporal Data Fusion Approach (STDFA) proposed by [PERSON] _et al._[35, 36] is a widely used spatial and temporal data fusion model based on unmixing methods. It was applied successfully for generating daily 30 m leaf area index [37] and land surface temperature data [38]. A comparison shows that STARFM and ESTARFM are more suitable for complex heterogeneous regions while unmixing methods are more suitable for cases that downscale the spectral characteristics of medium-resolution input imagery [39].