# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
); chenkangyi@myswjtu. moisture. [PERSON] and colleagues estimated soil moisture using phase delay, and their experimental results indicated that its correlation was more stable than that of amplitude [6]. Subsequently, [PERSON] et al. conducted further experiments and discovered a clear linear relationship between the initial phase of the reflected signal and surface soil moisture under bare soil conditions [7]. In recent years, the application of machine learning models for soil moisture monitoring has prompted many scholars to enhance the accuracy of soil moisture retrieval models using machine learning algorithms. [PERSON] et al. utilized the Helmert variance component estimation (HVCE) method to integrate weights of dual-frequency carrier phases, and they established soil moisture retrieval models using both linear and machine learning approaches. The results indicated that the integration of dual-frequency data through the HVCE method significantly improves the retrieval accuracy for single satellite data, and the machine learning model demonstrated superior performance compared to the linear model [8]. Subsequently, to validate the performance of other machine learning models in soil moisture retrieval, [PERSON] et al. developed a soil moisture retrieval model based on a Support Vector Machine (SVM) and employed a genetic algorithm to automatically determine the optimal model parameters, thereby maximizing model performance [9]. [PERSON] et al. constructed a phase correction model to mitigate environmental effects and used a BP neural network model for soil moisture retrieval, which enhanced the accuracy of single satellite data [10]. All of the above studies are based on machine learning models combined with single-star data to retrieve soil moisture, however, during the observation process, there are spatial and temporal differences between different satellites, which leads to low robustness of the single-star-based retrieval results. For this reason, many scholars have carried out research on the method of soil moisture retrieval using multi-satellite fusion data. [PERSON] et al. established a soil moisture retrieval model based on a multi-satellite fusion least squares support vector machine, and compared and analyzed the accuracy of soil moisture retrieval using single and multiple GNSS satellites, and the results showed that the multi-satellite fusion model performed better than the single-satellite model [11]. Subsequently, due to the influence of environmental and other factors, the effectiveness of the bare soil-based retrieval algorithm will be reduced, and [PERSON] et al. proposed a multi-feature soil moisture retrieval method based on multilayer perceptron (MLP), which synthesized a variety of factors and excluded part of the anomalous satellite data by setting a threshold to control the quality of the data, and then the MLP was used to establish a multi-satellite fusion retrieval model, and the results showed that the accuracy of the proposed model was better than the linear model [12]. In summary, the existing studies have achieved good results in satellite selection and soil moisture retrieval model construction, however, these results have a common problem, that is, a large number of manual interventions are needed in the experimental process to attenuate the instability of the retrieval accuracy caused by different satellite data. Aimed at the above problems, this study proposes a new method for retrieval of soil moisture based on the fusion of multi-satellite data with Random Forest. Based on the conventional GNSS-IR soil moisture retrieval algorithm, the MDI algorithm is introduced to adaptively assign the weights of different satellite data to establish a soil moisture retrieval model with better data utilization and robustness. Subsequently, the 2012 and 2017 datasets of the U.S.