## 4 Experiment and Analysis
### Model Validation  
In order to test the feasibility of the above-trained GA-BP multi-data fusion soil moisture inversion model to retrieve soil moisture in areas other than modeling, we input 10 surface environmental elements from six specific sites that were not involved in modeling. In the trained GA-BP neural network model, the output is GA-BP inverted soil moisture. The GA-BP retrieved soil moisture was compared with the GNSS-IR soil moisture of the characteristic site. Four indicators (Pearson correlation coefficient R, RMSE, bias, and ubRMSE), were used to analyze their accuracy. The results are shown in Table 4.  
As shown in Table 4, five Pearson correlation coefficients R are more significant than 0.6 for the six test stations. The highest correlation is 0.9329, which has a robust correlation. The worst correlation is only 0.0143 for the stations in barren areas. Only one station has an ubRMSE greater than 0.06, and four stations are less than 0.04. It can also be seen that the stations with the land cover type of grassland have better inversions and fewer errors in flat areas, and there are no gross errors. In addition to the rainfall factor, the model is influenced by NDVI due to its significant influence. For this reason, in areas with no vegetation cover, desert, there is no vegetation. It also means that there is no reasonable NDVI, which influences the final inversion results. Although the test results of one or two stations did not meet the expectations, and the errors were relatively large. However, the vast majority of the tested stations achieved good results with minor errors. This shows that the trained GA-BP multi-data fusion soil moisture inversion model is feasible and accurate for inverting soil moisture in areas other than modeling. In order to verify the correlation between NASA-USDA soil moisture, GNSS-IR soil moisture, and GA-BP model inversion of soil moisture. We compare and analyze the NASA-USDA soil moisture corresponding to the latitude and longitude of the 50 modeling stations, the GNSS-IR soil moisture, and the soil moisture retrieved by the GA-BP model. Because the units of the three are different, the error of the three cannot be calculated. Therefore, this paper only uses the Pearson correlation coefficient R to verify, and the comparison results are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Stations** & **R** & **RMSE** & **Bias** & **ubRMSE** & **Land Cover Type** \\ \hline ANGUS\_PROP & 0.4040 & 0.0403 & \(-\)0.0392 & 0.0092 & Barren \\ CALCITYAPT & 0.8824 & 0.0331 & 0.0273 & 0.0188 & Open bushes \\ CARRIZORAN & 0.9516 & 0.0490 & 0.0141 & 0.0470 & Grassland \\ MOONEYYN & 0.8199 & 0.0734 & \(-\)0.0635 & 0.0367 & Grassland \\ MT\_GLEASON & 0.8800 & 0.0643 & 0.0170 & 0.0620 & Savanna \\ WICKSRANCH & 0.8342 & 0.0331 & 0.0071 & 0.0324 & Grassland \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy statistics of the six unmodeled measurement stations. As seen in Table 5, 39 stations out of 50 with Pearson correlation coefficient r more significant than 0.6 between NASA-USDA soil moisture values and GNSS-IR based inversion of soil moisture, accounting for 78% of the total number stations. Meanwhile, among these stations, less than 0.6, the NASA products of 7 stations are unchanged during the inversion period, resulting in poor inversion results. The actual number of stations with fluctuations is 43, which shows that the actual effect of our algorithm is better than expected. There are 31 stations that correlate greater than 0.8, accounting for 72% of the fluctuating stations. The correlation between the soil moisture and NASA-USDA soil moisture values obtained from the inversion of this paper is strong, with 40 stations correlating 0.6, accounting for 80% of the total number of stations. There are 34 stations that correlate greater than 0.8, with an average correlation of 0.7770.