## 1 Introduction
Moreover, the complex modeling process and uncertainty of the experimental environment (such as the inconsistency of the direct and the reflected receiving channel, the noise of the signal receiver, and so on) have a direct influence on the accuracy of the soil moisture estimation. Therefore, there is an urgent need to evaluate the contribution and sensitivity of the input variables, which could be quite significant in doing experiments and interpreting behavior. The soil moisture retrieval using GNSS-R can be regarded as a nonlinear regression problem and received data can be taken as many input features (variables). Besides the traditional methods, the latest XGBoost based on the Boosting algorithm [42], which is good at variable importance estimation was introduced here to evaluate the variable contribution in GNSS-R. The Boosting algorithm is a popular and effective integrated learning algorithm in the field of data mining. By weighting and superimposing each weak classifier to form a strong classifier, the prediction error is effectively reduced and the classification results with higher accuracy are obtained. Based on the boosting algorithm, an algorithm called Gradient Boosting was proposed to continuously reduce the residuals and further reduce the residuals of the previous model in the gradient direction to obtain a new model. After that, an improved Gradient Boosting algorithm, Extreme Gradient Boosting (XGBoost) was proposed in 2015 [42]. In recent years, XGBoost has been widely used in-store sales forecasting, hazard risk prediction, power load forecasting, and other fields [43; 44; 45]. The most important reason for its success is that it is scalable in all scenarios. The scalability of XGBoost is determined by the optimization of several important models and algorithms, including a new tree learning algorithm for processing sparse data and a reasonable weighted quantile sketch process. The weight of the instance is allowed to be processed in the learning of the approximate tree. At the same time, parallel and distributed computing can continuously improve the learning rate of the tree, thus exploring a faster model. More importantly, XGBoost utilizes non-core computing, enabling the user to process hundreds of millions of samples. Different from the traditional decision tree algorithm, XGBoost adds regular terms such as leaf node weight and tree depth to the cost function. On one hand, it can control the complexity of the model; on the other hand, it can prevent over-fitting phenomenon [46]. At the same time, it uses a second-order Taylor expansion approximation to the cost function, which makes the approximation of the objective function closer to the actual value, thus improving the prediction accuracy. In recent years, the XGBoost algorithm has achieved excellent results due to its high operational efficiency and prediction accuracy in the field of machine learning and data mining [47]. In this paper, XGBoost learning method is aided to understand the behavior and the contribution of the input variables of GNSS-R. By utilizing the XGBoost algorithm to evaluate the contribution of the input variables (such as SNR, receiver noise... ), the sensitivity of the input variables to the retrieval results is shown. In addition, the results of ground-truth measurements (corresponding to two typical soil types and different soil conditions) are used to confirm the analysis performed with XGBoost learning method and investigate the performance of GNSS-R retrieval. The variation rate of the retrieved results with respect to input variables is analyzed. This knowledge can help the soil moisture retrieval and modeling process. The paper is organized as follows: In Section 2, the GNSS-R soil moisture retrieval and XGBoost algorithm are presented. Section 3 is focused on the results performed by XGBoost and shows the statistical data analysis obtained from ground-truth experiments. Finally, discussions of the results and conclusions are drawn in Section 4.