## 3 Taxonomy  
In a previous work [26], typical spatiotemporal data fusion methods were classified into three groups: unmixing-based, weight function-based, and dictionary-pair learning-based. However, this classification scheme cannot cover all spatiotemporal fusion methods. In the current review, existing spatiotemporal data fusion methods are categorized into five groups based on the specific techniques used to link coarse and fine images: unmixing-based, weight function-based, Bayesian-based, learning-based, and hybrid methods (Table 3). For convenience, the images with coarse spatial resolution but high frequency are referred as \"coarse images\" and pixels in these images as \"coarse pixels\", while the images with high spatial resolution but low frequency are called \"fine images\" and their pixels, \"fine pixels\". All the existing methods need to input one or more pairs of coarse and fine images and a coarse image at prediction date. The output is a synthetic fine image at prediction date. Ummixing-based methods estimate the value of fine pixels through unmixing the coarse pixels based on the linear spectral mixing theory. The multisensor multiresolution technique (MMT) proposed by [PERSON], [PERSON], [PERSON], and [PERSON] (1999) is perhaps the first unmixing-based method to fuse satellite images acquired at different times and with different spatial resolutions. Other unmixing-based methods can be considered as improved versions of MMT. There are four steps in MMT to predict a fine-resolution image: (1) classify the input fine image to define endmembers; (2) compute endmember fractions of each coarse pixel; (3) unmix the coarse pixels at the prediction date within a moving window; (4) assign unmixed reflectance to fine pixels [31]. MMT has two major challenges: large errors in the spectral  
Figure 4: Citation map of the literature in Table 2. The size of nodes indicates the count of citations. The citation relationship was derived from Web of Science. unmixing and lack of within-class variability for fine pixels inside one coarse pixel. The subsequent unmixing-based methods are geared towards addressing these two challenges. For instance, LAC-GAS NDVI integration method used locally calibrated multivariate regression models to better reserve the within-class NDVI spatial variability [52]. [PERSON] et al. introduced constraints into the linear unmixing process to ensure that the solved reflectance values were positive and within an appropriate range [57]. STDFA estimated reflectance change through unmixing endmember reflectance at both input and prediction date in a moving window and then added the estimated change back to the base fine-resolution image to get the prediction [63]. STDFA was further improved by using adaptive moving window size [66]. The Landsat-MERIS fusion method modified the cost function to prevent the solved endmember reflectance from being greatly different from a predefined endmember reflectance [62]. OB-STVIUM employed multi-data segmentation to better define the endmember fractions for improving the estimation of the fine-scale pixel values [17]. Weight function-based methods estimate fine pixel values through combining information of all input images by weight functions. Up to present, this category has the most methods developed (Table 3). The spatial and temporal adaptive reflectance fusion model (STARFM) is the weight function-based method developed first [8]. STARFM assumes that changes of reflectance are consistent and comparable at coarse and fine resolutions if pixels in coarse-resolution images are \"pure\" pixels, in that, one coarse pixel only includes one land cover type. In this case, changes derived from coarse pixels can be directly added to pixels in fine-resolution images to get the prediction. However, this ideal situation cannot be satisfied when coarse pixels are mixed, having a mixture of different land cover types. Therefore, STARFM predicts pixels with a function that gives a higher weight to purer coarse pixels based on information from neighboring fine pixels.