## 4 Results  
### Fused Monthly NDVI Time Series  
The base image pairs for each month of the last 20 years were selected as candidates to fuse NDVI images by the FSDAF algorithm. The fused NDVI images were used to fill the missing pixels or replace the contaminated pixels of the original Landsat data, but they are not directly utilized for time-series analysis. On one hand, this filling process ensured the best consistency between the derived and the actual data. On the other hand, fusion was only performed in the assigned region (cloudy and bad pixels), which saves the time of FSDAF fusion. Consequently, the final synthetic NDVI image ought to have higher accuracy and quality than the fused image. To avoid confusion, the fusion results were termed as fused images and the final synthetic images were referred to as reconstructed images. The 3D volumetric view of the fused NDVI time series stack in a sub-region of the study area in the past 20 years is shown in Figure 5. The fused image has abundant visual characteristics and details in the heterogeneous region.  
In order to assess the consistency of NDVI trajectories between MODIS and the fused NDVI, the NDVI values of three vegetation types (forest, wetland herbs and crop) were calculated and compared. Figure 6 depicts the NDVI time-series relationship between MODIS and the fusion results of the forest, wetland herb and cropland. The trends between MODIS and FSDAF NDVI time series in three vegetation types are similar. The trend of the higher-resolution FSDAF data changes more dramatically than that of the lower-resolution MODIS NDVI in some periods, because higher resolution images capture more detailed landscape changes. The pixel mixing problem of the surface target in coarse-resolution MODIS images may also cause the difference [9]. Obviously, the finer spatial resolution data derived from the FSDAF algorithm provides an opportunity to monitor detailed landscape changes, including less discernable deforestation, degradation or other disturbances.  
Besides, MODIS and Landsat NDVI images in December 2017 were used to assess the fusion accuracy on the corresponding dates (Figure 7). The fused dense NDVI stack preserves a same spatial resolution with Landsat images and a temporal resolution of one month. Generally, the fusion data show a close correspondence with the actual NDVI image and possess the resembling spatial characteristics of Landsat NDVI. The correlation analysis was also performed between the fused and actual Landsat NDVI in December 2017. As shown in the scatter density plot of Figure 8, high correlation (\(p<0.001\), \(RMSE\) (Root Mean Squared Error) = 0.053, \(R^{2}=0.93\)) between the fused and actual NDVI was achieved, despite the land cover change occurred between the base and predicted dates. The results demonstrate that the cross-fusion and the FSDAF method can capture the differences between the base and predicted dates and estimate the interpolated NDVI time series. Therefore, they can be used in the following change detection.  
Figure 5: The 3D volumetric view (size: \(500\times 500\times 240\) pixels) of the dense NDVI time series stack in a sub-region of the study area.  
Figure 6: The MODIS and FSDAF NDVI time series data of (**a**) forest, (**b**) wetland herb and (**c**) cropland.