## 5 Conclusions  
In this paper, the performance of the bistatic GNSS-R soil moisture retrieval was examined and analyzed on the basis of a machine learning aided method. We took the first step to utilize the feature of the XGBoost to analyze the input variable importance in GNSS-R, which has quite high operating efficiency and prediction accuracy. A simulation data set was built and used for testing and training. The range of the parameters was set as close as staying to the experimental situation. In the meaning time, several optimization parameters (estimators, samples, and col-sample-tree), also for different typical types of soil compositions were changed to verify the stability of the results. It was reported that the variable \(SNR\) showed the highest contribution than the other variables (\(\theta\), \(P_{n}\), and \(G^{\prime}\)) in the GNSS-R input vectors, either when we retrieved the permittivity or obtained soil moisture content for different soil types. It means that the received SNR is a predominant variable and much more sensitive to the obtained permittivity and soil moisture content with the importance of minimum 40%, and a maximum of 70%. Moreover, the variable \(\theta\) showed the least importance (below 10%) in the GNSS-R soil moisture retrieval. In some extremely case (changing the parameter of the algorithm), the importance of variable \(\theta\) is nearly zero means that it is almost not sensitive to the obtained permittivity and soil moisture content. Whatever we adjusted the parameter of the algorithm, the order of the variable importance is quite stable. Here we must note one point that the variable with low importance does not mean that it is not necessary for the retrieval procedure. For example, a variable with higher contribution and importance means that the accuracy of the value is quite crucial for retrieving in GNSS-R and this variable is quite sensitive and important for obtaining satisfying results. The uncertainty of a variable with high importance causes a higher bias than the variable with low importance. From a practical perspective, this is quite significant for interpreting data and solving the problem, particularly when doing the GNSS-R experiment and the retrieval results are unsatisfying. Figure 18: Variable importance sensitivity to different types of soils, Grugliasco (**a**) and Agliano (**b**), when \(estimators=4000\), \(n=5000\), \(testsize=0.5\) and \(0.6\). In order to further validate and discriminate the characteristics of the different input variables. Two GNSS-R ground-based campaigns with different soil conditions and compositions were carried out to do the performance analysis, which corresponds to the soil composition of the simulated data set. The permittivity of the ground-truth measurement was given by TDR measurement. The figure of skyplot provides information about the elevation angles. Combined the information of the GNSS-R and TDR measurement, we used a polynomial regression method to fit the input variables (\(SNR\), \(\theta\)) with the permittivity and soil moisture results respectively, for evaluating the variation rate of retrieved results with respect to each input variable. It also showed that the input variable \(SNR\) was a quite sensitivity parameter, which mostly impacts the soil moisture results than the variable \(\theta\). For the two typical soil types, another conclusion was that the increasing rate of SMC (or permittivity) with respect to \(SNR\) in silty clay loam soil (higher permittivity condition) was higher than in loamy sand soil (lower permittivity condition). This paper focused on the understanding of the input variables importance through the XGBoost algorithm and the ground-truth measurement, to investigate the performance of the bistatic GNSS-R soil moisture retrieval method. The quantification of variables importance is not only an important issue for constructing a soil moisture retrieval model but also a critical issue in GNSS-R experiments to interpret data and understand the potential phenomena.