## 4 Discussion  
A major focus on GNSS-R soil moisture currently is to evaluate the sensitivity of different observables to SM. Previous work was mainly focused on satellite remote sensing of soil moisture, concerning the dataset from the newly launched satellites UK TechDemoSat-1 (in short TDS-1) and NASA CYGNSS (Cyclone Global Navigation Satellite System). The reflection power obtained from the spaceborne sensors was compared to SMAP/SMOS products. A strong, positive linear relationship was found existing between the reflective power/reflectivity and the SM [35; 38], also reported in this paper. The correlation of different GNSS-R observables to SM was found conclusive on a global scale. Apparently, experiments of in-situ sensors with smaller spatial scale require more studies. Besides the spaceborne mission, the ground-truth experiment is also a commonly used and favorable tool to implement the GNSS-R application. We focus on the evaluation of the region of interest for different types of terrains using the ground-truth measurement, to evaluate the effect of the influence of uncertainty of received SNR and the elevation angle to SM. From the GNSS-R bistatic retrieval perspective, the GNSS-R parameters were regarded as input data, and the TDR data were taken as the output for the linear fit process. We have to note that, especially, in the case of ground-truth measurement, there are some factors (e.g., the interference of the equipment, the behavior of the radiation patterns, and the complex environmental conditions) that will affect the received signal. The uncertainty of the input parameters (bias of the elevation angle and SNR) may lead to some bad retrieval results that sometimes are hard to interpret. The in-situ GNSS-R measurement was done and the data were post-processed to obtain the permittivity and soil moisture content. We showed the correlation between the GNSS-R and TDR results. It was found that the good correlation between the TDR and SMC retrieval results concerned the satellite that the bar was directly pointing to. As we have mentioned before, the TDR measurements were done in the footprint of the antenna, which just corresponded to the Fresnel zone of the satellite that obtained the expected results. Future studies could be the evaluation of permittivity by TDR equipment implemented, which is implemented nearby or outside the footprint, to investigate the influence of the antenna pattern on SMC retrieval. Besides that, the differences of wave propagation, penetration depth and attenuation factor in the two sites need to be carefully considered before planning the ground-based measurement. The clay mineral can include \"water\" in its mineralogical network. This might be the reason why you could not retrieve the expected SMC, although many of them cause small bias. In particular, when the soil was saturated, the GPS can only sense one or two centimeters of the soil [29]. The most commonly used method of analyzing the quality of the soil moisture is a linear robust fit [35]. In order to reveal the potential relationship between incident angle, SNR and the SM, all the incident angles of satellites with corresponding SNR were collected to do the linear robust fit to show the dependence of the variables to SM. The input of the linear fit is the GNSS-R input data, and the output is the TDR results. The highest sensitivity of SNR to SMC (TDR) can be observed, being 3.84 dB/%, which was higher than reported [35] but it could be reasonable since all the satellites were taken into account in this paper and the output TDR values were very critical for only two sites. Unlike the previous research, the aim of this paper was to investigate the degree to which the retrieval performance can be influenced by the uncertainty of the input data. Different from the traditional approach, another purpose of this paper was to utilize the XGBoost algorithm for the GNSS-R data by adopting the data mining concept.