# Analysis of Key Issues on GNSS-R Soil Moisture Retrieval Based on Different Antenna Patterns
## 3 Results and Discussion of Key Issues in Application Scenarios
### Air-Based Observation in Multi-Antenna Pattern  
Next, using the air-based GNSS-R experiment conducted in Zhengzhou on 30 May 2014 as an example, the common key issues in the application of air-based observation in multi-antenna pattern are further analyzed. The experiment was conducted using the Yun-12 aircraft-borne GNSS-R load developed by the National Space Science Center of the Chinese Academy of Sciences to observe the regional farmland soil moisture and the water surface height of the Yellow River. The flight height was approximately 500 m, and the grazing angles of the signals from four satellites (PRN 18, 24, 21 and 22) during the flight were rather large. Specifically, the left-handed reflected signal was used to estimate soil moisture. The spatial location of the specular reflection point was determined according to the procedure shown in Figure 3. Then, the corresponding soil moisture estimate was superimposed on the remote sensing image for analysis, as shown in Figure 11a. In this figure, the base map is the GF-1 WFV pseudo-color composite image (transit date: 26 May 2014) with a resolution of 16 m, and the specular reflection points with different colors indicate different soil moisture ranges, as shown in the figure legends. In this study, we focused on the key issues common to GNSS-R soil moisture remote sensing; for an analysis of the soil moisture estimation accuracy, please refer to the literature [27].  
The average grazing angle of the signals of the four selected satellites was 58\({}^{\circ}\). According to Formula (5), the resolution pixel of this air-based GNSS-R test was 420 m\({}^{2}\) on average. Using the inverse distance weighted interpolation method on the observation area, the soil moisture value for a spatial resolution of 20 \(\times\) 20 m was obtained, as shown in Figure 11b. In this figure, as the color changes from red to green, the soil moisture estimates increase. The northwestern part of the study area is the Yellow River, and there is a pond in the east. The color boundaries in the grid image accurately depict the boundaries of the water and the bank, indicating that the GNSS-R technology can also be applied to land cover classification. The remaining parts of the map are mainly red and yellow, which is related to the fact that no precipitation occurred two weeks before the test and that the soil throughout the study area was thus rather dry.  
Using formulas in Section 2.3, the detection depth of each soil moisture estimate can be calculated, and the detection depths histogram of all the sampling points is shown in Figure 12. This figure shows that the detection depths of most sampling points were 10-20 cm. Because the soil was rather  
Figure 10: Daily soil moisture throughout the year 2012 and the detection depth of the GNSS signal at Station P041.  
dry throughout the study area during the aviation experiment, the soil moisture estimates of most sampling points were lower than 0.1 cm\({}^{3}\)/cm\({}^{3}\). This again indicates that the GNSS signals detection depths is closely related to soil moisture. This conclusion can provide reference information to verify the accuracy of airborne GNSS-R. Specifically, when laying out the soil moisture meters to collect the measurement data, different measurement depths can be set according to the characteristics of the uneven spatial distribution of soil moisture. Thus, a more targeted accuracy evaluation of the GNSS-R soil moisture estimates can be performed.