**5. Discussion**

From Figure 7, there were more blank points in the retrieval map of soil moisture in this study, which may be due to the lack of values in the 3 × 3 km grid, resulting in blank values when 3 × 3 km was aggregated to 36 × 36 km. Therefore, the number of surface reflectivity points of CYGNSS contained in each grid at the 3 km spatial resolution in the modeling time was counted (Figure 9). The number of grids with surface reflectivity points fewer than 20, between 20 and 40, between 40 and 80, and greater than 80 accounted for 40%, 19.7%, 29.3% and 11% of the total number of grids, respectively. As shown in Figure 9, a large number of null values appeared in the central and eastern parts of the study area; the western part presented more grids with fewer surface reflectivity points; grids with fewer than 20 grid points accounted for 40% of the total number of grids in the study area during the modeling time. The absence of values for surface reflectivity points of CYGNSS made the retrieved soil moisture results show the phenomenon of a blank value, probably because some of the surface reflectivity points of CYGNSS were removed by the method of a water body, especially in the central and eastern regions, as well as in the seaward

regions, where a large number of water bodies exist. However, after analyzing the results without using the removal method of water bodies, it was found that this method was not the main reason for the appearance of few reflection points and null places, but rather the non-coherent accumulation times of CYGNSS receivers and the irregular distribution of specular reflection points resulted in the sparse spatial distribution of surface reflectivity points of CYGNSS at 3 km spatial resolution and the low distribution of observation points.

**Figure 9.** The map of CYGNSS points in the 3 km grid during the modeling time.

The slope was calculated based on the SMAP product and the corresponding surface reflectivity of CYGNSS after removing the average value. A temporal resolution of 2~3 days for SMAP and the property of random distribution of CYGNSS would reduce the number of mutually matched values. Meanwhile, if there were fewer than three matching values in the modeling time, the corresponding slope was not calculated. However, the number of grids with fewer than three in this part was 1% of the total number of grids. Therefore, the slope of the linear equation is another reason for the existence of blank values in the retrieval results of soil moisture (Figure 10).

**Figure 10.** Slope map at the 3 km grid during the modeling time.

The soil moisture results obtained in this study varied across the measured soil moisture networks (Tables 2 and 3), and these were projected onto maps for further analysis, as shown in Figures 11 and 12. The ubRMSE for most of the measured sites were below the mean value of 0.061 cm3/cm3, accounting for about 60% of the total; the rest were maintained between 0.061 cm3/cm3 and 0.100 cm3/cm3; the measured sites with an accuracy greater than 0.1 cm3/cm3 accounted for 7% of the total. The distribution of sites of soil moisture retrieval larger than 0.1 cm3/cm3 showed that these sites were basically in the forest. The lush vegetation has a significant effect on the reflectance signal, as shown by the lower value of surface reflectivity of CYGNSS, which subsequently reduced the sensitivity to soil moisture. Counting the sites with ubRMSE below 0.061 cm3/cm3, most of them

were in low vegetation areas such as grasslands and wetlands, indicating that the retrieval method in this study still maintains a certain sensitivity to soil moisture in areas with low vegetation cover. In the measured soil moisture network, especially the SNOTEL network, all the results of soil moisture retrieval had poor accuracy performance in this network. The analysis revealed that the sites of the SNOTEL network are mainly located in high altitude and heavily vegetated areas. Due to vegetation and high altitude, the accuracy of the area where the measured network is located was poor. Compared with the correlation between SMAP and the measured data, the correlation between this study and the measured data was mainly distributed between 0.4 and 0.8, indicating that the correlation was weak. The effect of factors such as vegetation and high altitude resulted in little variation in the soil moisture, which may account for the low correlation.

**Figure 11.** The ubRMSE map for the retrieved soil moisture in this study.

**Figure 12.** The correlation comparison between the SMAP product and retrieved soil moisture.

## **6. Conclusions**

With the advantages of wide signal sources, many sampling points and high spatialresolution for spaceborne GNSS-R, the removal of the influence of water bodies and the correction of the Fresnel reflection coefficient could effectively improve the accuracy of soil moisture retrieved by CYGNSS. According to the problem of water impact, in this paper an improved method to remove the influence of water bodies was proposed. A normalization method for the Fresnel reflection coefficient was proposed to correct the surface reflectivity of CYGNSS data by analyzing the change in the Fresnel reflection coefficient under different influencing factors. Finally, based on the linear algorithm, the results of the retrieved soil moisture were obtained, and the accuracy was compared and verified by the product data and the measurement data.

The improved method of water removal proposed in this paper can effectively remove observations affected by water bodies. The normalization method of the Fresnel reflection coefficient could effectively attenuate the effect of influencing factors on the Fresnel reflection coefficient, but at larger incident angles (greater than 65◦) the normalization effect became worse. Compared with the results of the official CYGNSS product, the average ubRMSE of soil moisture retrieved by the method in this paper was improved by 10%, and the correlation was similar overall. Based on all measured data, the average ubRMSE for retrieval results of soil moisture in this paper was 0.061 cm3/cm3, with an average correlation of 0.4.

The direct removal of observations affected by water bodies is currently the most common approach, which leads to a reduction in the number of sampling points. Retaining observations and studying more efficient removal models are future research topics. The normalization method of the Fresnel reflection coefficient only considers typical soil types. Due to the complexity of soil composition in the natural environment, a unified correction model will be developed in the future by collecting more data.

**Author Contributions:** Q.W., J.S. (Jiaojiao Sun) and J.S. (Jinguang Shang) collected the original data; Q.W. and X.C. jointly designed the study and wrote the manuscript; X.C. and J.S. (Jiaojiao Sun) helped with the revision and discussion; T.J. and Z.L. provided supervision. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China (No. 42104037), the China Postdoctoral Science Special Foundation (No. 2022T150487), the Natural Science Foundation of Hubei Province for Distinguished Young Scholars (No. 2022CFA090), the Special Fund of Hubei Luojia Laboratory (No. 220100001), the Natural Science Foundation of Hubei Province of China (No. 2022CFB146), the Key Laboratory of Marine Environmental Survey Technology and Application, the Ministry of Natural Resource (No. MESTA-2020-B007) and the Special Fund of Hubei Luojia Laboratory (No. 220100003).

**Data Availability Statement:** The CYGNSS data used in this study were derived from official CYGNSS website (https://cygnss.engin.umich.edu/data-products/, accessed on 6 November 2021), Soil Moisture Active and Passive (SMAP) data used in this study came from NASA (https://nsidc. org/data/smap/smap-data.html, accessed on 6 November 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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