## 3 Taxonomy
Given its simplicity and code available to the public, STARFM has been a popular method with 398 citations. However, it has two major issues: the assumption is not valid in heterogeneous landscapes and the weight function is empirical. Most other weight function-based methods improved STARFM with regards to the above two issues, or modified STARFM to fusion other products, such as land surface temperature (LST) [21], vegetation indices [50] and classification results [39], instead of reflectance. For example, the spatial temporal adaptive algorithm for mapping reflectance change (STAARCH) detects the change points \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Category**} & \multicolumn{1}{c}{**Methods 1 and References**} \\ \hline \multirow{2}{*}{Unmixing-based} & MMT [31]; LAC-GAC NDVI integration [52]; Landsat-MERIS fusion method [62]; \\  & ESTDFM [44]; STDFA [63]; MSTDFA [66]; OB-STVIUM [17]; MERIS-Landsat fusion [57] \\ \hline \multirow{8}{*}{Weight function-based} & STARFM [8]; semi-physical fusion approach [19]; ESTARFM [11]; STAARCH [27]; \\  & SADFAT [21]; Topographically corrected downscaling [18]; STITFM [23]; \\  & STARFM-Sensor difference [54]; mESTARFM [45]; Bilateral Filter method [59]; \\  & ET fusion model [53]; STI-FM [64]; operational STARFM [33]; Three-step method [16]; \\  & STEM-LAI [61]; ATPPK [34]; ATPPK-STARFM [67]; STNLFFM [30]; Fast \\  & spatiotemporal fusion [55]; RWSTFM [47]; DBUX [25]; ISKRFM [56]; Temporal \\  & high-pass modulation and edge primitives method [36]; Decision-level fusion [39]; \\  & STVIFM [50]; Soil moisture downscaling [51] \\ \hline \multirow{2}{*}{Bayesian-based} & BME [20]; Unified fusion [70]; NDVI-BSFM [46]; Bayesian data fusion approach [42]; \\  & Spatio-Temporal-Spectral fusion framework [38] \\ \hline \multirow{4}{*}{Learning-based} & SPSTFM [29]; One-pair image learning method [35]; EBSPTM [32]; ELM-based \\  & method [58]; WAIFA [15]; bSBL-SCDL [48]; CSSF [37]; Regression tree-based \\ \cline{1-1}  & method [69]; Evapotranspiration downscaling [49]; STFDCNN [68]; MRT [40]; \\ \cline{1-1}  & Integrated sparse representation-based fusion [41] \\ \hline \multirow{2}{*}{Hybrid} & FSDAF [26]; NDVI-LMGM [43]; Regularized spatial unmixing method [60]; \\  & STIMFM [22]; STRUM [24]; USTARFM [65]; BLEST [28] \\ \hline \hline \end{tabular}  
* Names of these methods were extracted from the literature. If the methods were not named in the literature, the words describing the methods’ major characteristics were used to name them here.  
\end{table} 
Table 3: Categories of the existing spatiotemporal data fusion methods. from dense time series of coarse images to improve STARFM's performance when land cover type change exists [27]. Enhanced STARFM (ESTARFM) introduces a conversion coefficient to improve STARFM's accuracy in heterogeneous areas [11]. SADFAT modified STARFM to blend LST data by considering annual temperature cycle and urban thermal landscape heterogeneity [21]. The original STARFM was also modified to an operational framework by integrating BRDF correction, automatic co-registration, and automatic selection of input data pairs [33]. ATPPK-STARFM first downscales 500 m MODIS images to 250 m before implementation of STARFM to increase the performance in abrupt changes and heterogeneous landscapes [67]. ISKRFM integrates image inpainting and steering kernel regression to detect land-cover changed regions and determine the weight function. These two steps improve the land cover issue and empirical weight function in the original STARFM method [56]. Bayesian-based methods use Bayesian estimation theory to fuse images in a probabilistic manner. In the Bayesian framework, the spatiotemporal fusion can be considered as a maximum a posterior (MAP) problem, i.e., the goal of spatiotemporal data fusion is to obtain the desirable fine image at the prediction date by maximizing its conditional probability relative to the input fine and coarse images [38].