# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 4 Results and Analysis
### Comparisons of the Downscaled Results
The in situ data were wetter, but the spatial variation trend was consistent with the others. The three downscaled results showed the spatial heterogeneity of SM, which was absent in the original 36 km SMAP data because of their coarse spatial resolution. As shown in Figure 6b, among the three downscaled SM datasets, GABP has a wider dynamic range, which can more accurately depict the spatial distribution of the SMAP and provide a finer spatial variability. The texture features presented by the GABP SM clearly show topographic changes, such as mountain elevation and slope. In the low-lying and flat Sichuan Basin, all three models can represent the spatial distribution of SMAP. In the Western Sichuan Plateau and the transition zone with large topographic fluctuations, GABP shows clear advantages in terms of data consistency and detail representation, which can better retain the spatial distribution and quality of SMAP. In Figure 6c, the distribution of the RF is also consistent with SMAP, but it shows abrupt changes at some longitudes and latitudes. By analyzing the importance values of the RF model (Figure 2b), the large influence of the longitude and latitude makes the regression trees show obvious differences in inductive preferences on both sides of some longitude and latitude lines, because the longitude and latitude values show an isometric monotonic increase in space. In Figure 6d, the CNN also has a similar spatial variation to SMAP, but both are too smooth, and the heterogeneous information is far less than that of GABP and the RF. The CNN algorithm fuses the neighborhood information through spatial convolution, but the training set at a scale of 36 km has less effective correlation information, which smooths the local features. Therefore, the CNN model has limitations when applied to spatial downscaling. As shown in Figure 6f-h, the transitional zone from the Sichuan Basin to the Western Sichuan Plateau increases sharply. The downscaled results of the GABP neural network shown in Figure 6f can more accurately represent the fluctuation of SM with topography and mountain texture,  
Figure 6: Spatial distributions of the mean SMs for (**a**) in situ SM, (**b**) GABP, (**c**) RF, (**d**) CNN, and (**e**) SMAP in Sichuan during summer and autumn of 2017. The local amplifications corresponding to (**c**), (**d**), and (**e**) in the red box area are (**f**) GABP, (**g**) RF, and (**h**) CNN. and it can overcome the sudden changes in the longitude and latitude of the RF in Figure 6g and the spatial smoothness of the CNN in Figure 6h. In conclusion, the GABP neural network method shows a more accurate feature expression in SM downscaling in complex terrains in this study. This is because most of the CCs of predictors are relatively high in the NW area, and predictors have little or no influence except for elevation, longitude, and slope in the SE area as Figure 3b shows. The GABP neural network can self-adjust parameters, self-evolve, and self-adapt, making it more accurate in describing complex relationships. This study is a regression problem, and the basis of a neural network is a multisource nonlinear regression. RFs, on the other hand, are essentially decision trees that are more suitable for classification scenarios. For regression problems, they are based on the average of multiple decision trees. A CNN captures spatial relationships in the adjacent area. However, since the training set is 36 km in space, it is too coarse to transfer this relationship to 1 km.