## 2 Literature Survey  
We collected spatiotemporal data fusion methods published in refereed journals of the remote sensing field. These 58 papers were published in 11 major international journals in the field of remote sensing (Table 2). Methods published in conference proceedings or other journals may have been omitted in this literature analysis, but majority of existing spatiotemporal data fusion methods should be included in Table 2. Among these 11 journals, 15 out of 58 methods were published in Remote  
Figure 2: (**a**) Yearly literature counts of journal papers introducing spatiotemporal data fusion methods and (**b**) citation counts of these papers in Web of Science. The search was conducted on 10 March 2018. Sensing of Environment, a top journal in the field of remote sensing, which confirms that as one of new data processing techniques, spatiotemporal data fusion has attracted a lot of attention and has been widely accepted by the remote sensing community. It should be noted that papers on spatiotemporal data fusion applications were not included in this section. They are discussed in Section 5. For convenience, papers introducing new spatiotemporal fusion methods are referred as methodology papers hereinafter, and papers only applying spatiotemporal fusion methods to study scientific or practical problems are referred as application papers. Currently, \"spatiotemporal data fusion\" is a term commonly used to name this type of data fusion technology. This term can well distinguish this type of fusion methods from traditional pan-sharpening methods which only improve images in spatial and spectral domains. However, in the early years, the names are quite inconsistent. Developers named their methods in different ways. This can be reflected by the words used in the titles (Figure 3a) and keywords of the 58 methodology papers (Figure 3b). \"fusion\", \"spatial\", and \"temporal\" are frequent words used in titles and keywords which exactly reflect the nature of spatiotemporal data fusion, i.e., technology to improve spatial and temporal resolutions of multi-source satellite images. \"MODIS\" and \"Landsat\" are also frequently used in the titles and keywords, suggesting that most spatiotemporal data fusion models were proposed to fuse MODIS and Landsat data or used them as representatives of coarse images and fine images respectively. Although most spatiotemporal fusion methods were applied to data from satellites listed in Table 1, it is worthwhile to mention that spatiotemporal fusion methods can also fuse high-resolution images from commercial satellites, especially for small-scale studies. For instance, the 5 m RapidEye images was fused with 30 m Landsat images to improve the detection of local forest disturbance [71]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source Titles** & **Records** & **Percentage** & **Literature** \\ \hline Remote Sensing of Environment & 15 & 25.86\% & [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28] \\ IEEE Transactions on Geoscience and Remote Sensing & 14 & 24.14\% & [8; 29; 40; 41] \\ Remote Sensing & 10 & 17.24\% & [42; 43; 44; 45; 46; 47; 48; 49; 50; 51] \\ International Journal of Remote Sensing & 5 & 8.62\% & [52; 53; 54; 55; 56] \\ IEEE Geoscience and Remote Sensing Letters & 4 & 6.90\% & [57; 58; 59; 60] \\ International Journal of Applied Earth Observation & 2 & 3.45\% & [61; 62] \\ and Geoinformation & 2 & 3.45\% & [63; 64] \\ Journal of Applied Remote Sensing & 2 & 3.45\% & [65; 66] \\ Sensors & 2 & 3.45\% & [65; 66] \\ IEEE Journal of Selected Topics in Applied Earth & 2 & 3.45\% & [67; 68] \\ Observations and Remote Sensing & 1 & 1.72\% & [69] \\ GIScience and Remote Sensing & 1 & 1.72\% & [70] \\ Remote Sensing Letters & 58 & 100\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of methodology papers of spatiotemporal data fusion published in 11 major journals in the field of remote sensing. The search was conducted on 10 March 2018. The literature citation map was plotted by VOSViewer (Figure 4).