## 4 Results and Discussion
### Prediction of Time-Series Synthetic NDVI  
Based on the above analysis of the three fusion schemes, we decided to use data in the same year (except for 2008, 2012, and 2013), in adjacent years (2008, 2012), and data with 2-yr intervals (2013) (data shown in Table 1) to predict the synthetic NDVI. We fused TM and MODIS NDVI by using the STARFM method and predicted NDVI for each 8-d intervals during 2005-2013. A total of 180 NDVI scenes (20 scenes/yr for 9 yr) were predicted. Figure 5a,b show the mean and standard deviation of MODIS NDVI and synthetic NDVI at 8-day intervals during 2005-2013, respectively. Figure 5c shows the relationship between the MODIS NDVI and synthetic NDVI results that were shown in Figure 5a,b, respectively. Figure 5d shows the relationship between the standard deviations of the MODIS NDVI and synthetic NDVI results that were shown in Figure 5a,b, respectively. Figure 5e shows the maximum NDVI values of MODIS and synthetic images during the growing seasons, which represent the highest vegetation productivity within a year. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Mean** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Entropy** & \
\begin{tabular}{c} **Average** \\ **Gradient** \\ \end{tabular} &
\begin{tabular}{c} **Mean Absolute** \\ **Difference** \\ \end{tabular} \\ \hline TM\_NDVI & 0.240 & 0.049 & 3.619 & 0.014 & / \\
**Pre\_NDVI\_Scheme1** & **0.244** & **0.053** & **3.653** & **0.013** & 0.019 \\ Pre\_NDVI\_Scheme2 & 0.245 & **0.045** & 3.566 & 0.012 & **0.018** \\ Pre\_NDVI\_Scheme3 & 0.247 & 0.060 & 3.725 & 0.016 & 0.022 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy assessment of the fused images. TM_NDVI is the observed TM NDVI image and Pre_NDVI represents the predicted NDVI images at 30-m resolution that were derived from STARFM with Schemes 1, 2, and 3. Numbers in bold represent the best fit among the three schemes. Figure 5: Temporal variations of the regional mean and standard deviation (shaded area) of NDVI for grasslands in the study areas during 2005–2013 (growing season). (**a**) MODIS NDVI; (**b**) synthetic NDVI; (**c**) scatter plots between MODIS NDVI and synthetic NDVI at 8-d intervals; (**d**) scatter plots between the standard deviation of MODIS NDVI and synthetic NDVI at 8-d intervals; (**e**) MODIS NDVI\({}_{\text{max}}\) (black) and synthetic NDVI\({}_{\text{max}}\) (gray). Error bars represent the standard deviations of MODIS NDVI\({}_{\text{max}}\) (black) and synthetic NDVI\({}_{\text{max}}\) (gray). The means of the synthetic NDVI values were almost identical to those of the MODIS NDVI (Figure 5c). The predicted NDVI (30 m) therefore retained the high-frequency temporal information from the MODIS NDVI time series. The predicted images had larger standard deviations and represent larger dispersions of the imagery (Figure 5d), which indicates that the synthetic images may contain more detailed information. The differences between the standard deviation of MODIS NDVI and synthetic NDVI in this study were less than those reported in the study of [PERSON] _et al._[18]. In their study, these differences could be clearly observed in graphics similar to the ones in Figure 5a,b, and the shadow area of the synthetic NDVI series was much larger than the MODIS NDVI series. This can be explained by the homogeneous landscape for grasslands in Xilinhot, our study area. In contrast, in the study of [PERSON] _et al._[18], the study area was a mixed fragmentized landscape consisting of forests, shrublands, and residential areas. Our results indicate that the accuracy of time-series NDVI derived from the STARFM algorithm was reliable and that the STARFM could effectively predict NDVI time series at higher spatial and temporal resolutions for the grasslands.