## 4 Results
### Daily Time Variation of Dielectric Constant
Figure 5c,d demonstrate the statistical results by plotting the scattering features using the results presented in Figure 5a,b: Figure 5c shows the scattering plot using the reflectivity from CYGNSS and soil moisture from SMAP; Figure 5d is used to determine the correlation between the dielectric constant from CYGNSS and SMAP. The Root Mean Square Error (RMSE) and unbiased RMSE (ubRMSE) are used to capture the degree of correspondence between the estimated values and reference values [36], as well as to verify the developed method in the correlation plots. Figure 5a presents an RMSE of 5.73, while Figure 5b depicts a much lower RMSE of 0.13. The RMSE value using reflectivity and soil moisture is reasonable, since it is comparable to the results shown in previous research [12], and the RMSE of the dielectric constant has not been investigated in the published GNSS-R research. These results indicate that: (i) the dielectric constant obtained from GNSS-R using the devised method has greater equivalence than reflectivity as a function of soil moisture from the SMAP results; (ii) the calculated dielectric constant is estimated well with CYGNSS data; and, (iii) it is possible to quantitatively produce a dielectric constant using GNSS-R measurements. Figures 6 and 7 illustrate additional comparison results between CYGNSS and SMAP satellite data that were measured at two other locations with substantially different environments. The observation results from TxSON, Texas, and Little Washita, Oklahoma in the U.S. have been investigated, and the equivalent analysis was performed as in Figure 5. As seen in Figures 6 and 7, the computed dielectric constant and reflectivity results using CYGNSS data have comparable results with the dielectric constant and soil moisture from SMAP. The RMSE from the dielectric constant results is 6.65 at TxSON and 6.40 at Little Washita, which show comparable, but minimally distinct results from Yanco. This implies  
Figure 7: (**a**) Time series plot of dielectric constant at measurement site at Little Washita in United State using CYGNSS and SMAP; (**b**) time series plot of reflectivity and soil moisture at the same location with reflectivity from CYGNSS and soil moisture from SMAP using (**a**); (**c**) scattering plot of CYGNSS-derived reflectivity and SMAP using (**a**); and, (**d**) scattering plot of CYGNSS-derived dielectric constant and SMAP dielectric constant using results of (**b**). that computing the dielectric constant using GNSS-R is required for considering regional characteristics, such as roughness, topography, season, weather, etc. Seasonal variation is investigated in the following section and the topographic effects are analyzed by [50]; yet, the more combined analysis of the regional characteristics is necessary for obtaining precise dielectric constant and soil moisture using GNSS-R.  
Table 1 lists additional results of confirmation metric, including unbiased RMSE for the three sets of analyses. The ubRMSE values are also calculated to remove the possible biases incurred in either mean or amplitude fluctuations in the retrieval process [36], and the data for calculating RMSE for the first and ubRMSE for the third row in the table are used in the computation. The second row represents the RMSE calculation and the fourth row depicts the ubRMSE computation that were used for data when Figure 5, Figure 6, and Figure 7b are drawn. The results indicate that the dielectric constants observed at the three different locations are similar to the SMAP results, as confirmed by the statistical analysis, and, when biases are reduced, the GNSS-R estimated dielectric constant is even more similar to the observation results from SMAP. The Pearson correlation coefficient is also calculated and evaluated for the three analysis locations.