# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## 6 Conclusions  
The accuracy of spatial-temporal data fusion results depends on both the data fusion model and input data used in the fusion process. In order to give guidance for choosing the optimal image pairs, this paper evaluates the accuracy of fusing data from different combinations of MODIS-Landsat data sources using the STARFM data fusion model as an example. The predictive skill of different pair images is assessed in terms of input data consistency, vegetation phenology, and land cover types for two vegetated areas including crops and forests. The results show that MODIS images with smaller view zenith angle always produce better fusion results. The combination of Terra NBAR and Landsat 7 produces better results than other combinations in red and NIR wavebands. However, SLC-off gaps in  
Figure 13: MSD of Landsat reflectance (red and NIR) and NDVI for site 1 (**a**) and site 2 (**b**). A higher MSD means a more heterogeneous landscape.  
Landsat 7 ETM+ image limit data fusion application. The combination of Aqua NBAR and Landsat 8 is also a good option. Since the Terra or Aqua daily NBAR at 250 m resolution needs to be produced in-house, the standard collection six MODIS daily NBAR products at 500 m resolution can be ordered directly and will be good for operational uses. This conclusion is drawn based on the analysis of the Landsat-MODIS image pairs and, thus, can be applied to other spatial-temporal data fusion models.  
Data fusion results are affected by the selection of image pairs and by different land covers. The prediction accuracy for forests is higher than crops in our study areas. Generally, pairs from the growing season have a better predictability during the growing season than the non-growing season and vice versa. With the increase of the time interval between pair and predicted dates, the pair predictability will decrease. While the prediction accuracy based on the non-growing season pairs decreases more slowly than that based on the growing season pairs due to rapid changes in the vegetation on the land surface during the growing season. Especially, the derived NDVI from the fused surface reflectance are overestimated for the non-growing seasons when using the growing season image pair, and underestimated for the growing season slightly when using the non-growing season pairs. Data fusion accuracy can be improved if some key image pairs during different vegetation phenology stages are included. The evaluation on the two automatic pair selection strategies (the nearest date, ND, and higher correlation, HC) reveals that the HC strategy works better than the ND strategy. With the increase of image pairs, the difference between two strategies decreases. In general, our results show that the spatial-temporal data fusion can be improved by choosing suitable image pairs.  
[PERSON] processed the data and prepared the draft; [PERSON] helped fuse the remote sensing data and modify the STARFM program; and both [PERSON] and [PERSON] helped analyze the results. All authors contributed to paper revisions.  
This work was partially supported by the National Natural Science Foundation of China (grant nos. 41571341 and 41331171), the NASA Science of Terra, Aqua and Suomi NPP program, the NASA LCLUC program, and the US Geological Survey (USGS) Landsat Science Team program. USDA is an equal opportunity provider and employer.  
The authors declare no conflict of interest.