# Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
## 3 Results and Analysis
### Accuracy Assessment Results of Image Harmonizing
However, the maximum probability values of them are 3.9, 6.2 and 4.9 for GF-1 image, MODIS image and harmonized image, respectively. This indicates that the harmonized images can carry on the fine spatial resolution of the GF-1 images. In terms of the range of NDVI values, it is obvious that the ranges of GF-1 and harmonized images are broader than MODIS image, which reveal that there is a finer spatial detail expression ability in low vegetation coverage area and high vegetation coverage area on GF-1 image and harmonized image than on the MODIS image. Figures 7 and 8 show the spatial details of NDVI pattern for GF-1 image, MODIS image and harmonized image on DOY 178 and DOY 227, respectively. Figures 7a and 8a indicate the position of zoomed subset areas for analyzing of NDVI pattern on DOY 178 and DOY 227. Figure 7b-d are the NDVI calculated from harmonized image using the improved Kalman filter, the original GF-1 NDVI images, the harmonized image using ESTARFM model respectively. We can see that from Figure 7b-d, there is a similar NDVI spatial variation pattern on harmonized images (Figure 7b,d) with the original GF-1 NDVI images (Figure 7c), northwest of this zoomed area is lower than other regions. This reveals that the harmonized images carry on the spatial details of the original GF-1 NDVI images. Figure 7e,g are the \(\Delta PR_{K}\) between the original GF-1 NDVI images (Figure 7c) and harmonized images using the improved Kalman filter (Figure 7b) and ESTARFM model (Figure 7d) respectively. We can see that the NDVI of harmonized images using the improved Kalman filter are higher in vegetation area than that in the original GF-1 NDVI images (negative difference value), lower in non-vegetation area (positive difference value). Diversely, the NDVI of harmonized images using ESTARFM model are mostly higher than GF-1 image (negative difference value) in the whole zoomed area. In addition, Figure 7f shows  
Figure 5: Temporal sequences showing the spatial variation in the original GF-1 NDVI images (**a**), the harmonized NDVI images by improved Kalman filter (**b**), and the original MODIS NDVI images (**c**), during the summer corn growing season (form DOY 156 to DOY 252). that the \(\Delta PR_{K}\) ranging between harmonized images using the improved Kalman filter and the original GF-1 NDVI images is \(-0.2\)-\(0.2\). Figure 7h shows the \(\Delta PR_{K}\) ranging between harmonized images using ESTARFM model and the original GF-1 NDVI images is \(-0.25\)-\(0.25\). This reveals that the harmonizing accuracy using improved Kalman filter is higher than that using ESTARFM model. Similar conclusions can be obtained by Figure 8.  
Figure 6: The NDVI probability density distribution of summer corn planted area (the X-axis is the NDVI value and the Y-axis represents the probability density of each NDVI value) for the original GF-1 NDVI images (**a**), the harmonized NDVI images by improved Kalman filter (**b**) and the original MODIS NDVI images (**c**), during the summer corn growing season (form DOY 156 to DOY 252). Figure 7: Spatial-temporal fusion results and predicted residuals of DOY 178: **(a**)** the GF-1 image (R\(\backslash\)G\(\backslash\)B=Band 3\(\backslash\)2\(\backslash\)1); **(b**)** the harmonized NDVI image by the improved Kalman filter; **(c**)** the original GF-1 NDVI images; **(d**)** the harmonized NDVI using ESTARFM model; **(e**)** the \(\Delta PR_{K}\) of the Figure 7b,\(c\); **(f**)** the probability density distribution of \(\Delta PR_{K}\) in Figure 7e; **(g**)** the \(\Delta PR_{K}\) of the Figure 7d,\(c\); **(h)** the probability density distribution of \(\Delta PR_{K}\) in Figure 7g; **(f**)** and **(h)**: the X-axis is the \(\Delta PR_{K}\) value and the Y-axis represents the probability density of each \(\Delta PR_{K}\) value. Furthermore, the quantitative assessment is done to validate the image harmonizing accuracy using the improved Kalman filter and ESTARFM model.