# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 4 Results
### Accuracy Analysis of Different Model Retrieval Results  
To further assess the performance of different methods in soil moisture retrieval from multi-satellite fusion phases, this study incorporated three accuracy metrics: the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). Figures 11-13, respectively, display the linear correlations between the retrieval results of the four models and the soil moisture reference data for the P041, P037, and LM sites. Table 2 provides the specific values of these accuracy metrics for the different models at the three sites.  
Figure 11: Linear regression analysis of soil moisture versus reference values for each model retrieval at site P041.  
Figure 10: Comparison of soil moisture for each model retrieval at site LM, with reference data.  
As illustrated in Figures 11-13, and Table 2, the soil moisture retrieval results from the RF, SVM, RBF, and CNN models, developed using data from the P041, P037, and LM sites, all show strong correlations with the soil moisture reference values. Among these, the RF model yielded the best and most stable results, with correlation coefficients of 0.94, 0.95, and 0.98, respectively. The results from the SVM, RBF, and CNN models were slightly inferior, with correlation coefficients at the P041 site being 0.89, 0.81, and 0.83, respectively; at the P037 site they were 0.89, 0.85, and 0.84, respectively; and at the LM site they were 0.95, 0.95, and 0.91, respectively. Notably, compared to the P041 and P037 sites, the RBF model showed significant improvement in retrieval accuracy at the LM site. At the P041 site, the RMSE for the four models ranged from 0.03 to 0.075 cm\({}^{3}\)/cm\({}^{3}\), and the MAE ranged from 0.02 to 0.06 cm\({}^{3}\)/cm\({}^{3}\). At the P037 site, the corresponding RMSE ranged from 0.025 to 0.04 cm\({}^{3}\)/cm\({}^{3}\), and the MAE ranged from 0.02 to 0.03 cm\({}^{3}\)/cm\({}^{3}\). At the LM site, the corresponding RMSE ranged from 0.003 to 0.009 cm\({}^{3}\)/cm\({}^{3}\), and the MAE ranged from 0.002 to 0.007 cm\({}^{3}\)/cm\({}^{3}\). Among these, the Random Forest model performed the best, achieving  
Figure 12: Linear regression analysis of soil moisture versus reference values for each model retrieval at site P037.  
Figure 13: Linear regression analysis of soil moisture versus reference values for each model retrieval at site LM.  
the lowest RMSE and MAE across all models. Compared to the other three models, at all three sites, the RMSE was reduced by at least 50%, 17.9%, and 58.9%, and the MAE was reduced by at least 48%, 18.2%, and 62.5%, respectively.  
The comparative analysis of retrieval accuracies across the SVM, RBF, and CNN soil moisture retrieval models at the three sites showed that none of these models achieved better R values than the RF soil moisture retrieval model. This underscores the efficacy of the multi-satellite data fusion RF retrieval model proposed in this study. The RF model quantifies each segment's contribution to the model by calculating reductions in impurity during the retrieval process. It effectively utilizes segments with the highest contributions, which allows it to suppress gross errors caused by single satellites and fully leverage segments strongly correlated with soil moisture. This approach enhances the reliability and robustness of the model compared to traditional methods.