# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 5 Discussion and Conclusions  
Obtaining large-area data with a high resolution and precision is a bottleneck in SM remote sensing. In complex terrains, SM is more spatially heterogeneous and fine-scale SM data are required. This study explored the SM spatial downscaling and information fusion methods suitable for complex terrains. Three ML algorithms, a GABP neural network, an RF, and a CNN, were compared by fusing multiple factors, and in situ SM data were fused into the downscaled results by way of a GDA combined with a spatial interpolation. The main conclusions are as follows:  
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2. Themost correlated predictors were slope, elevation, day sequence, and longitude. The day sequence, ET, latitude, LST, and EVI were more effective in autumn than in summer and in the NW area than in the SE area. Most of the CCs were relatively high in the NW area. The EVI was a more effective indicator of vegetation than the NDVI. The aspect was more effective in the NW area than in the SE area. The elevation, day sequence, latitude, longitude, and slope had a much higher importance than the other predictors in the RF model. It is important to include spatiotemporal information and terrain as inputs in downscaling SM over complex terrains. 3. The GABP neural network showed advantages in data accuracy and spatial and temporal fineness when compared to the RF and CNN. The GABP neural network results maintained the dynamic range and mean level of the original SMAP with statistical consistency, whereas the RF and CNN reduced the dynamic range. These three methods could reduce the error of SMAP, and the data were more accurate in autumn than in summer. The GABP neural network had the smallest error among the three, especially in summer. RF was prone to abrupt warp and weft changes in space, and the CNN had a spatial smoothness. The GABP neural network model can better capture the mean states and details of variation in time and space, and it has advantages in high-elevation areas and large undulating terrains over the RF and CNN. 4. In the in situ SM fusion, the GDA had a lower RMSE and MAE than the GRA when applying the same interpolation method. The GDA_Kriging interpolation method could better preserve the dynamic range, statistical characteristics, and spatial details of the HRSM data than the GDA_IDW method. The fusion results improved the dynamic range of the HRSM and was closer to the in situ SM. The GDA_IDW method tended to smooth the spatial variations. Figure 9: Spatial distributions of the mean SM during summer and autumn of 2017 for HRSM, GDA_Kriging, and GDA_IDW in Sichuan. Despite the aforementioned contributions, this study still has the following limitations: (1) ML is often considered a black box that is difficult to explain and verify in terms of physical mechanisms. In future studies, physical models can be combined with ML models. Physics can be used to design loss functions [41; 61], initialize auxiliary models, structure design models, model residual errors, and create hybrid models [62]. Incorporating physical constraints and partial prior knowledge into the optimization direction and network structure can better utilize limited data resources, leading to improved model accuracy and design effectiveness [63]. (2) The LST data in this study were obtained from MODIS's thermal infrared remote sensing, which has a high spatial and temporal resolution and inversion accuracy. However, because of the influence of clouds, this method is only applicable to cloud-free areas. (3) The training set did not include cloud areas, and the SMAP products exhibit a \"dry bias\" in high, cold mountainous areas [4]. Therefore, the downscaled results were lower than the in situ SM, and the model is more suitable for low SM situations. (4) When the SM is downscaled to the same fine spatial resolution, there will be more redundant information in flat areas. In future studies, the spatial scale should be adjusted adaptively according to terrain complexity.