# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 3 Methodology  
High-resolution features were first upscaled to 36 km to match with the 36 km SMAP in time and space. The unified 36 km source and target data composed the training and validation sets for model training. The downscaling of SM was achieved by feeding 1 km predictors to the models trained with the 36 km data [13; 29]. A flowchart of this study is shown in Figure 2, including the data processing, downscaling fusion, and in situ SM fusion. The data preprocessing procedure was used to generate a 36 km training and validation set and a 1 km test set through spatiotemporal matching. The two key steps in the preprocessing procedure were data structuring and spatiotemporal matching. Data structuring converted the data into csv files whose columns were longitude, latitude, and variable values through data stitching, unification of projection, coordinate system transformation, resampling, clipping, and sorting. Both time matching and space matching utilized the nearest neighbor method to match different variables. Time matching took SMAP as the benchmark after the time of all kinds of data were uniformed to Coordinated Universal Time. Since there was no SMAP in the test set, LST was taken as the benchmark. Space matching used the coarse scale variable as the benchmark. In this study, the summer and autumn of 2017 and Sichuan Province were used as the experimental time and space. After data preprocessing, 8670 samples on a coarse scale of 36 km were obtained. The validation set was randomly selected from 30% of these samples. In the downscaling fusion, ML algorithms were used to train the samples. The GABP neural network optimized itself in two ways. First, it used the fitness function as the optimization index to find the optimal initial weights, thresholds, and number of hidden \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Variable Types** & **Variables** & **Temporal Resolutions** & **Spatial Resolutions** & **Units** \\ \hline SM & SMAP SM & 50 h & \(\sim\)36 km & \(\text{m}^{3}/\text{m}^{3}\) \\  & In situ SM & 1 h & — & m\({}^{3}/\text{m}^{3}\) \\  & Elevation & — & \(\sim\)30 m & m \\ Space information & Longitude & — & 0.001 \({}^{\circ}\) & \({}^{\circ}\) \\  & Latitude & & & \\ Time information & Day sequence & — & — & — \\  & AM/PM & 1 day & \(\sim\)1 km & K \\  & LST & 8 days & \(\sim\)500 m & kg/m\({}^{2}\) \\  & LCT & 1 year & \(\sim\)500m & — \\ Land-surface environment variables & NDVI & 16 days & \(\sim\)1 km & — \\  & EVI & Slope & — & \(\sim\)30 m & \({}^{\circ}\) \\  & Aspect & & & & \\ \hline \hline \end{tabular} LST: land-surface temperature, ET: evapotranspiration, NDVI: normalized difference vegetation index, EVI: enhanced vegetation index.  
\end{table} 
Table 1: Overview of the data used in this study. layer neurons through the genetic algorithm. Second, it optimized the weights of each layer through error backpropagation until the target accuracy was achieved. A CNN can extract abstract features and reduce complexity through weight-sharing (convolution) and downsampling (pooling) strategies. Its model parameters can be trained using the gradient descent method. Researchers have used CNN models to achieve the super-resolution reconstruction of remote sensing images [56; 57]. An RF integrates multiple independent decision trees based on two different random processes: sample disturbance and feature disturbance. The RF regression model considers multiple independent variables as affecting the dependent variable. After tuning the hyperparameters and learning curves, the optimal models for the GABP neural network, CNN, and RF methods were obtained. The optimal GABP neural network model consisted of a three-layer network. The tansig activation function was used in the hidden layer, and the purelin function was used in the output layer. Trainlm was chosen as the training function, which is suitable for small- and medium-sized networks and has a faster convergence. Additionally, multiple tests were conducted to determine other parameters.