## 3 Taxonomy
Bayesian framework provides more flexibility in modeling the relationships between the input and predicted images using defined principles which allows an intuitive interpretation of fusion processes [70]. In Bayesian-based data fusion, the key is how to model the relationship between observed images (i.e., the input images) and unobserved image (i.e., the image to be predicted). In spatiotemporal data fusion, there are two types of relationships. One is the relationship between observed coarse image and fine image observed at the same date which we name as scale model, and the other one is the relationship between coarse images observed at different dates which we name as temporal model. The scale model incorporates knowledge of point spread function which maps the pixels of fine image to the pixels of the coarse image [42]. The temporal model describes the dynamics of land surface, including both gradual change such as vegetation seasonality and sudden land cover change such as forest fires and floods. Existing Bayesian-based spatiotemporal data fusion methods used different methods to model these relationships. In the Bayesian Maximum Entropy (BME) method, covariance functions were used to link coarse the Advanced Microwave Scanning Radiometer for Earth observing system (AMSR-E) sea surface temperature (SST) with 25 km resolution and fine MODIS SST with 4 km resolution [20]. In a unified fusion method, the low-pass filtering method was used to model the relationship between coarse and fine images and a linear model to model the temporal relationship [70]. In the NDVI-BSFM method, linear mixing model was used to link MODIS and Landsat NDVI and multi-year average NDVI time series was used to represent the temporal relationship [46]. In a recent Bayesian data fusion approach, integration of bilinear interpolation of coarse image and high-pass frequencies of fine image were used to model the scale model, and the joint covariance from the observed coarse images was employed as temporal model [42]. Learning-based methods employ machine learning algorithms to model the relationship between observed coarse-fine image pairs and then predict the unobserved fine images. Up to date, dictionary-pair learning [29], extreme learning [58], regression tree [69], random forest [49], deep convolutional neural networks [68], and artificial neural network [15] have been used for spatiotemporal data fusion. Dictionary-pair learning-based algorithms establish correspondences between fine- and coarse-resolution images based on their structural similarity which can be used to capture the main features, including land cover type changes, in the predictions. The Sparse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM) is perhaps the first to bring dictionary-pair learning techniques from natural image super-resolution to spatiotemporal data fusion [29]. SPSTFM establishes a correspondence between the change of two coarse and fine image pairs. Following SPSTFM, [PERSON] and [PERSON] developed another dictionary-pair learning-based fusion method which uses only one pair of fine- and coarse-resolution images [35]. This method trains a dictionary pair on the input fine- and coarse-resolution image pair, and then downscales the coarse-resolution image at the prediction date by a sparse coding technique. To solve the problem of insufficient prior knowledge in the sparse representation, a new spatiotemporal fusion model is constructed with the semi-coupled dictionary learning and structural sparsity [48]. Furthermore,the explicit down-sampling process from fine to coarse images were considered by compressed sensing which can further improve accuracy of the dictionary-based spatiotemporal fusion [37]. The error-bound-regularized sparse coding (EBSPTM) method is another dictionary-pair learning method which imposes a regularization on the error bounds of the dictionary perturbations to model the possible perturbations of the overcomplete dictionary [32].