## 1 Introduction
The SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM) [27] was the first learning-based algorithm, which established a correspondence between the fine resolution and coarse resolution images through the dictionary pair learning to generate the predicted fine resolution images. Following SPSTFM, [PERSON] [28] developed another dictionary-based algorithm that only required a pair of coarse resolution and fine resolution images. One of the important improvements of the learning-based methods is that it can predict land cover change events. Although many fusion methods of learning based have recently been proposed, such as Error-Bound-regularized SParse coding (EBSPTM) [29], Extreme Learning Machine (ELM) [30], and Hybrid Color Mapping (HCM) [31], the learning-based fusion methods are relatively novel, which have not been widely used [32], In addition, learning-based methods only consider the statistical relationships between the fine resolution images and coarse resolution images instead of any physical properties of remote sensing signals [28], which predicted fine resolution images cannot exactly remain spatial detail feature and shapes of objects. Previous literature shows that existing fusion methods have two main improvements including monitoring the abrupt change events and accurately capturing the spatial details of land surface. To solve the problems, hybrid methods were proposed in recent years, and the Flexible Spatiotemporal DAta Fusion method (FSDAF) [32] was a typical hybrid method. FSDAF was based on spectral unmixing analysis and a thin plate spline (TPS) interpolator and combined the weighted function to predict fine resolution images. Because the information of land cover change can be captured by using a TPS interpolator for the MODIS images at the prediction date, the FSDAF can monitor abrupt change events and has been widely used recently. However, the FSDAF did not consider mixed pixels for Landsat images, and its predicted fine resolution images lacked intra-class variability and spatial details. Specifically, the FSDAF has the following three limitations: (1) it directly performs the hard-classification for Landsat image, and its predicted Landsat-like images lack intra-class variability and spatial details; (2) it directly assigns the temporal changes of coarse resolution images to fine resolution images without considering the differences between Landsat and MODIS images; and (3) it introduces a homogeneity index (_HI_) to guide the residual distribution, which is derived from the classification map at the base date. The _HI_ will not be suitable for guiding the residual distribution when there are land cover changes and misclassifications [33]. In addition, more input parameters were required to be set before the operation of FSDAF, which increased the computational complexity. To solve the above limitations of the FSDAF method, this study developed a comprehensive and automated fusion method, an enhanced FSDAF (EFSDAF), and tested it by fusing the Landsat images and MODIS images. Compared with FSDAF, EFSDAF has the following strengths: (1) it considers the mixed pixels in Landsat images and uses a globally representative spectral linear mixture model (SVD) of the Landsat pixels for spectral unmixing; (2) it adjusts the differences between Landsat images and MODIS images using a linear model; and (3) it proposes a new residual index (_RI_) to guide the residual distribution based on the interpolator results of MODIS images at the base date and prediction date. In addition, EFSDAF is an automated spatiotemporal fusion method that does not require additional input parameters compared to FSDAF. In this study, we tested the EFSDAF in the areas of vegetation phenology and flood events and compared it with three other popular fusion methods: STARFM, STRUM, and FSDAF.