## 3 Results and Analysis  
### Data Fusion Assessments  
Before the data fusion process, we selected three images for the STARFM algorithm to illustrate the accuracy of the results and thus determine availability. The experimental data were HJ-1 data from areas of interest 1 and 2, and the corresponding time MOD09 GA data.  
Figure 5 shows three pairs of HJ-1 and MODIS images acquired on 26 April 2012, 4 May 2012, and 8 May 2012. The first row in Figure 5 shows three MODIS reflectivity images using band 2-1-4 as the red-green-blue composite, and the second row shows three HJ-1 reflectivity images using band 4-3-2 as the red-green-blue composite. The pairs of MODIS and HJ-1 images acquired on 26 April 2012 and 8 May 2012 were selected as base images, and the STARFM algorithm was used to predict the 4 May 2012 HJ-1 fusion image.  
After STARFM algorithm calculation, we compared the predictions with the 4 May 2012 observed HJ-1 reflectivity image. Figure 6 shows the observed and predicted images produced by the STARFM algorithm in regions of interest 1 and 2. The boundary information of road, water, and vegetation can be expressed clearly in the fusion image, and there is high similarity between the predicted and the measured image.  
There are many commonly used error evaluation indicators for model evaluation; we used several of these indicators to refine the results of the fusion evaluation, including root mean square error (RMSE), mean absolute percent error (MAPE), average absolute difference (AAD), and coefficient of determination (\(R^{2}\)).  
RMSE was used to measure the deviation between the result and the reference value; the smaller the RMSE, the closer the result is to the reference value.  
MAPE indicates the accuracy between the result and the reference value; if this value is small, it indicates high accuracy, according to the model predictive ability evaluation table proposed by [PERSON] et al. [34]. Generally, when MAPE \(\leq\) 20, the forecast is valid.  
Average absolute deviation (AAD) is the average of the absolute values of the difference between the result and the reference value.  
R is a direct reflection of correlation between the result and the reference value. If R is positive, the correlation coefficient is positive, on the contrary, if R is negative, the two are negatively correlated. The larger the absolute value of R, the higher the degree of correlation, the greater the determination coefficient, the closer the relationship between the two sets of data.  
Figure 7 shows scatter plots of the predicted and actual observations. Table 3 shows the analytical values for the predicted and actual observations. In the four scatter plots, the points are distributed on both sides of the y = x line. The distribution of the red and NIR band points of forest are closer to the straight line and more uniform, whereas the distributions of farmland are concentrated above the straight line. This means that the fusion values are slightly higher than the actual values. According to Table 3, the MAPE values of farmland are within 20%, and the \(R\) values are high, which indicates that the  
Figure 4: (**a**) Probability distribution diagram of model residual error, where KDE stands for kernel density estimation, N (0, 1) represents normal distribution, and Hist represents the model residual histogram; (**b**) autocorrelation function graph of residuals.  
The results show that the fusion images have high spatial resolution, meanwhile, preserving periodic vegetation changes. The fusion images are close to the real observation images, and can be used to study the temporal variation of vegetation. Therefore, image fusion is performed to obtain complete 2-day HJ-1 reflectivity data.