## 2 Materials and Methods
### Data Pre-Processing  
The objective of the method is to obtain soil moisture products with a high spatial and temporal resolution by fusing ground-based GNSS-IR data with surface environmental parameters extracted from optical remote sensing. The specific process of the multi-data fusion model in this study is described below, and the flow chart of the method is shown in Figure 2.  
(1) Data processing. Download the soil moisture retrieved by GNSS-IR technology through the International Soil Moisture Network (ISMN). Use Google Earth Engine (GEE) to obtain image data of surface environmental elements (latitude, longitude, NDVI, temperature, rainfall, land cover type, slope, aspect, elevation, and shadow) of the experimental area (1 January 2014-1 March 2014). GEE's image pyramid strategy specifies the output image with a spatial resolution of 500 m and a temporal resolution of 1 day. (2) Build the data set. According to each GNSS station's latitude and longitude information, the corresponding image value of each GNSS station is extracted. Ten surface environment elements are used as the input of the GA-BP neural network model to form the input data set. Take GNSS-IR soil moisture as the training target (output data) to construct an output data set. This makes the input layer of the GA-BP neural network have 10 neurons, while the output layer has only one neuron. (3) Model building. Import the modeling input data set and the modeling input data set into Matlab, and divide all the data into 70%, 15%, and 15% as the training set, validation set, and test set for model construction. Use the divided training set and confirmation set to train the GA-BP neural network model, and use the test set to test the accuracy of the trained model. Save the GA-BP neural network model (trained qualified neural network) whose accuracy reaches the threshold. (4) Accuracy verification. First, the reliability of the neural network model that reaches the threshold is tested by the tenfold cross-validation method. Secondly, a verification input data set formed 10 kinds of surface environment elements corresponding to the GNSS stations not involved in the modeling. Input the validation data set into the trained neural network model and output the GA-BP inversion soil moisture data set. The GA-BP inversion soil moisture data set is compared and analyzed with the GNSS-IR soil moisture corresponding to the stations not involved in the modeling. If the accuracy meets the requirements, the neural network model trained to reach the threshold is reliable and effective. (5) Production soil moisture map. Each 500-m square in the experimental area corresponds to a latitude and longitude coordinate, and 10 kinds of surface environmental elements corresponding to all latitude and longitude coordinates in the experimental area are extracted through the latitude and longitude coordinates to form a map input data set. Input the mapped input data set into the GA-BP neural network model that reaches the threshold and obtain the GA-BP inversion soil moisture data set for mapping through the training output. Import the GA-BP inverted soil moisture data set used for mapping into ArcGIS, and use ArcGIS \"Point to raster\" function to convert all the GA-BP inverted soil moisture data sets used for mapping into raster images (Soil moisture map). (6) Soil moisture map verification. The soil moisture map (500 \(\times\) 500 m) and NASA-USDA (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) products, NDVI (500 \(\times\) 500 m), and rainfall (500 \(\times\) 500 m) were compared and analyzed. Due to the different units and resolutions of the four Same. We only analyze whether the generated soil moisture map is qualified by changing the map spots and the value between the same areas. On this basis, we extracted 392 sites based on the latitude and longitude of each NASA grid center and analyzed the correlation between the NASA soil moisture at the sites and the soil moisture retrieved by GA-BP. Further, evaluate the performance of GA-BP inversion of soil moisture.