## 1 Introduction  
High spatial resolution remote sensing images with highly frequent observations are significant for monitoring dynamic changes of land surface [1; 2; 3; 4], especially for monitoring short-term and abrupt change events such as floods and forest fires [5; 6]. However, due to technical and budget limitations, existing single satellite sensors cannot simultaneously generate high temporal and high spatial resolution images [2; 7; 8]. For example, the remote sensing images acquired from Landsat, SPOT,and Sentinel 2 satellites have a high spatial resolution (10-30 m), but they have a long coverage period of 5-30 days (hereinafter called \"fine resolution\" images). The other satellites images (e.g., MODIS and VIIRS) have a daily sampling frequency, but spatial resolution ranges between 250 and 1000 m (hereinafter called \"coarse resolution\" images). To overcome this constraint, spatiotemporal fusion methods of remote sensing data have been developed to synthesize high spatial and temporal resolution images for monitoring the dynamic changes of land surface by fusing coarse resolution images and fine resolution images [9; 10]. In the past decade, the synthetic high spatiotemporal resolution images have been widely used in vegetation phenology monitoring [9; 11], urban surface temperatures [12; 13; 14], urbanization [15], crop yield estimating [16; 17], and monitoring sudden and short-term change events (e.g., flood) [18]. According to the literature, the existing spatiotemporal fusion methods can be classified into three categories [19; 20; 21]: weight function-based, unmixing-based, learning-based. Weight function-based methods estimate the fine resolution images by weighting the similar neighboring information of all input images. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) [7] was the earliest proposed and most widely used spatiotemporal fusion method, which blended the Landsat and MODIS imagery to synthesize Landsat-like images by using a weighted function, but STARFM cannot predict heterogeneous landscape. Then, the Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) [2] and the Enhanced STARFM (ESTARFM) [8] were developed to improve STARFM's performance in disturbance areas and heterogeneous areas, respectively. However, both STAARCH and ESTARFM required two pairs of MODIS and Landsat images for input data, which are less suitable in cloudy places. Unmixing-based methods predict the fine resolution images by unmixing the coarse resolution images based on the spectral mixing theory. The Multi-sensor Multi-resolution Technique (MMT) [22] was originally proposed based on unmixing, and existing fusion methods based on unmixing were an improvement to MMT. Methods, such as the Spatial Temporal Data Fusion Approach (STDFA) [23] and the Spatial and Temporal Reflectance Unmixing Model (STRUM) [24], were typical unmixing-based methods. The advantages of unmixing-based methods have high computational efficiency, which can generate the time series of images with high spatiotemporal resolution. However, the fusion methods based on unmixing only unmix the coarse resolution images, which did not consider mixed pixels phenomenon of fine resolution images, and its predicted fine resolution images lacked intra-class variability and spatial details [19; 25]. In addition, most of both the weight function-based methods and unmixing-based methods supposed the land cover will not change between base date and prediction date, which cannot monitor the abrupt change events. Learning-based methods have been proposed in recent years, which employ the machine learning algorithms to perform feature learning between coarse resolution images and fine resolution images [26], and simulating the fine resolution images based on the structural similarity of the input images.