## 4 Results and Discussion  
### Accuracy Assessment of Synthetic NDVI Based on STARFM  
Figure 4 shows the validation results for the three fusion schemes described in Section 3.2, that is, data fusion within one year (Figure 4a,d) (2007), with adjacent years (Figure 4b,e) (2007 and 2006), and with 2-yr intervals (Figure 4c,f) (2007 and 2005). Red colors represent over-estimations and blue colors indicate under-estimations for synthetic NDVI. Figure 4d-f show the scatter plots between Landsat NDVI and synthetic NDVI for the three schemes. As we only considered grasslands in Xilinhot, all other vegetation types have been masked out.  
There was good agreement between the observed Landsat NDVI images and synthetic NDVI data in the three schemes. The average relative errors between observed Landsat NDVI images and synthetic data were below 10% (8.12% for Scheme 1, 7.58% for Scheme 2, and 9.41% for Scheme 3). However, the pixels near lake shores, along riverbanks (mainly in the northern and central parts of the study area), and in other sparsely vegetated areas (mainly in the southern parts of the study area)  
Figure 3: Flow chart for the support vector machine (SVM) algorithm.  
showed larger differences between synthetic NDVI and NDVI derived from observed TM. The study of [PERSON] _et al._[PERSON] _et al._ (2014) also pointed out that over-estimations of synthetic images were mainly located at pixels near riverbanks and lake shores in the mid-eastern parts of New Orleans, USA, and these were due to abrupt surface changes caused by urban flooding [PERSON] _et al._ (2014). Cloud contamination can also cause bias between observed NDVI and synthetic NDVI ([PERSON] _et al._, 2015). Although the MODIS product used in this study give the 8-day optimal observations, it can still not fully remove the atmospheric noise [PERSON] _et al._ (2015), which could affect the accuracy of the fusion result. While in most of the study area, the grass sceneries were homogenous and the three schemes showed good fusion results. However, the fusion results of Scheme 1 (within one year) and Scheme 2 (with adjacent years) were slightly better than Scheme 3, as they had higher coefficients of determination (\(R^{2}\)) and lower root-mean-square errors (\(R^{2}\) = 0.72, RMSE = 0.021 for Scheme 1; \(R^{2}\) = 0.77, RMSE = 0.024 for Scheme 2; \(R^{2}\) = 0.67, RMSE = 0.037 for Scheme 3) (Figure 4).  
We further chose five statistical indices (_i.e._, mean value, standard deviation, entropy, average gradient, and mean absolute difference) to assess the quality of the predicted synthetic NDVI products (Table 4). There were no considerable differences between the observed and predicted NDVI images for the three schemes, and the first four indices were nearly identical. The average differences were all below 0.025. In addition, the fused images in the same year (Scheme 1) and in the adjacent years had more consistent results with observed images than those from Scheme 3, as indicated by the five indices. In another woodland area, similar coefficient of determinations (0.66-0.94) between the observed TM NDVI and predicted NDVI were found by [PERSON] _et al._[PERSON] _et al._ (2014). Overall, our predicted NDVI images were fairly reliable and could be used for accurate estimations of AGB. In the following work, we chose different data-fusion schemes according to the availability of TM images.  
Figure 4: Difference maps and scatter plots between the observed Landsat NDVI images (taken 19 May 2007) and the synthetic NDVI images (taken during 17–24 May 2007) predicted by (Left) (**a.d**) Scheme 1; (Middle) (**b.e**) Scheme 2, and (Right) (**c.f**) Scheme 3.