## 1 Introduction  
High spatial and temporal resolution remote sensing technology plays an important role in land-cover detection, crop growth monitoring and phenological parameter inversion [1]. Unfortunately, it is impossible to obtain high temporal resolution and high spatial resolution images simultaneously from one sensor mounted on a satellite [2; 3]. For example, Landsat series multi-spectral images at 30-m resolution have wide applications in extracting vegetation indices, monitoring land cover dynamic changes and ecological system variation studies. This system is widely used because of its finer spatial resolution, rich archive, and free availability [4; 5; 6]. However, the 16-day revisit cycle and the influence of bad weather, such as rain and clouds, make it difficult to acquire the continuous and cloudless remote sensing images that may be required to monitor certain Earth surface changes [7; 8]. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra/Aqua satellites can provide remote sensing images at 1-day temporal, 250-1000 m spatial resolution, which is a potential alternative used in monitoring the Earth surface land coverage at large scales [9; 10; 11]. However, the low spatial resolution of MODIS data confines its application in fragmented and extremely heterogeneous landscapes [12]. If high spatial resolution and temporal resolution characteristics could be achieved at the same time, the advantages of Landsat and MODIS would be integrated and significantly improve the applicability of remote sensing technology to monitor land surface changes [13; 14]. In recent years, researchers have developed a series of data fusion models to generate high spatial and temporal resolution remote sensing data. The unmixing-based method is one of these methods, which utilizes the Linear Spectral Mixture (LSM) model to extract endmembers reflectance at sub-pixel scale [15]. This method has previously been applied to fuse MODIS and Landsat images to produce high temporal and spatial resolution data [16; 17; 18; 19; 20; 21; 22]. [PERSON] _et al._[23] utilized the model to unmix Thermal Infrared Radiance for subpixel land surface temperature retrieval, and [PERSON] _et al._[24] proposed the MSTDFA model based on the LSM theory to generate daily synthetic Landsat imagery by combining Landsat and MODIS data. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) proposed by [PERSON] _et al._[25] is another effective fusion method to predict the phenology reflectance changes. [PERSON] _et al._[26] proposed a regularized spatial unmixing (RSpatialU)-based method, which introduced the prior class spectra estimated by STARFM to reduce the unmixing method error among the mixed pixels. The combination of the unmixed-based model and STARFM can utilize the advantages of the two modes to produce more accurate fusion data. Inspired by STARFM, [PERSON] _et al._[22] proposed a method entitled Spatial and Temporal Reflectance Unmixing Model (STRUM) which incorporates STARFM and unmixing-based fusion methods by combining the temporal change information obtained from the residue of two date MODIS images to improve the accuracy of unmixing-based method based on Bayesian theory. The fusion data generated by these methods are applicable to research in crop monitoring [20], environmental process detection [27], and mapping of forest disturbance [28], _etc._  
STARFM originated from the idea of capturing quantitative changes in radiometry caused by phenology through fusion of Landsat and MODIS data and has been widely applied for land surface detection. The method is applied to predict Landsat-like daily surface reflectance with one or several pairs of Landsat and MODIS images acquired on the same day and MODIS observations from the predicted date by weighing similar pixels for the central pixel [25].