# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 6 Conclusions  
To minimize the impact of human intervention on the reliability of soil moisture retrieval, this study proposed a soil moisture retrieval method based on Random Forest and multi-satellite data fusion GNSS-IR. Phase characteristic parameters were extracted using the SNR observation data from the L2 band at PBO sites P041 and P037, as well as SNR observation data from the L2 band collected in Lamasquere, France. These parameters from different satellite arcs were fused, and a Random Forest model was employed to establish the soil moisture retrieval model. Additionally, Support Vector Machine, Radial Basis Function neural network, and Convolutional Neural Network models were incorporated and compared against soil moisture reference values. The analysis led to the following conclusions:  
1. Using multi-satellite fusion data for soil moisture retrieval effectively enhances data utilization and retrieval accuracy. At station P041, the average R for the four models reached 0.87, with RMSE values ranging between 0.03 and 0.075 cm\({}^{3}\)/cm\({}^{3}\), and MAE values between 0.02 and 0.06 cm\({}^{3}\)/cm\({}^{3}\). At station P037, the average R for the four models was 0.88, with RMSE values between 0.025 and 0.04 cm\({}^{3}\)/cm\({}^{3}\), and MAE values between 0.02 and 0.03 cm\({}^{3}\)/cm\({}^{3}\). At the LM site, the average R for the four models reached 0.94, with RMSE ranging between 0.003 and 0.009 cm\({}^{3}\)/cm\({}^{3}\), and the MAE between 0.002 and 0.007 cm\({}^{3}\)/cm\({}^{3}\). 2. The Random Forest model, which uses the MDI algorithm to measure the contribution of arcs, significantly improved the robustness of the retrieval results. Compared to the SVM, RBF neural network, and CNN models, the Random Forest model exhibited the best retrieval accuracy. Quantitative results indicate that, at station P041, the R with reference values reached 0.94, with RMSE and MAE around 0.032 cm\({}^{3}\)/cm\({}^{3}\) and 0.025 cm\({}^{3}\)/cm\({}^{3}\), respectively. At station P037, the R reached 0.95, with RMSE and MAE around 0.028 cm\({}^{3}\)/cm\({}^{3}\) and 0.022 cm\({}^{3}\)/cm\({}^{3}\), respectively. At the LM site, the R reached 0.98, with RMSE and MAE around 0.003 cm\({}^{3}\)/cm\({}^{3}\) and 0.002 cm\({}^{3}\)/cm\({}^{3}\), respectively. 3. The Random Forest model, based on the MDI algorithm, demonstrated strong reliability in measuring the importance of arc segments. There was a significant positive correlation between arc segment importance and correlation coefficients. The RF model could adaptively identify arc segments favorable for soil moisture retrieval using the MDI algorithm, and allocated weights according to their importance, exhibiting robustness and generalization performance. 4. The Random Forest model based on the MDI algorithm demonstrated strong retrieval performance. Comparative experimental results show that the retrieval results of the RF model using the MDI algorithm were superior to those of the RF model based on the MDA algorithm. 5. The Random Forest model based on the MDI algorithm can effectively diminish the impact of terrain undulations on soil moisture retrieval. Experimental results indicate that, in areas with flatter terrain, the correlation between phase data and soil moisture is higher and more stable. Furthermore, the Random Forest model utilizing the MDI algorithm can proficiently identify these data, thereby reducing the influence of terrain factors on soil moisture retrieval. The experimental results suggest that, compared to previous soil moisture retrieval methods, the proposed GNSS-IR soil moisture retrieval model can adaptively allocate arc weights using the MDI algorithm, significantly reducing the need for manual intervention and extensive data preprocessing. It also exhibits strong robustness. **Author Contributions**: Conceptualization, Y.J. ; formal analysis, Y.J. and T.W. ; funding acquisition, R.Z. and B.S. ; data curation, B.Z., S.N., H.J. and K.C. ; writing--original draft preparation, Y.J. ; writing--review and editing, R.Z., B.S. and J.T.