## 6 Conclusions  
A procedure for calculating dielectric constant using GNSS-R has been tested with data from the CYGNSS satellites. Version 2.1 CYGNSS data were used for the computation method, and Level 3 SMAP soil moisture data with 36 km \(\times\) 36 km were used as a reference to validate the calculation results. A restriction of incident angle was applied in order to extract coherent scattering components from combined signals that were captured by the LHCP antenna on each CYGNSS satellite. A multi-step mathematical approach has been implemented for approximately three years of data with the objective of synthesizing calculation process to acquire dielectric constant. Reflectivity, which is the ratio of GNSS signal captured by the zenith direction antenna and nadir direction antenna, from the CYGNSS data is converted to Fresnel reflection coefficient with surface roughness information from SRTM. The Fresnel reflection coefficient was applied through the use of an equation developed to derive dielectric constant from radiometer observations. The spatial resolution of CYGNSS data was adjusted on the EASE grid v2.0 coordinates for drawing a 2D global image. The derived dielectric constant has been validated with soil moisture, and the dielectric constant was converted by the semi-empirical soil moisture model. The dielectric constant that was retrieved from CYGNSS data shows values ranging between 1 to 3; whereas, the dielectric constant retrieved from SMAP data shows values that ranged from approximately 5 to 20. Both of the dynamic ranges have different values with a range from 2 to 16 for the dielectric constant retrieved from reflected GPS signals, as presented by [16]. The dynamic range difference is potentially caused by the simplified model that is used to understand the link between land geophysical parameters, because the dielectric constant is dependent on different soil characteristics (soil moisture, soil salinity content, soil type, and/or temperature). In addition, it is understood that the dielectric constant should not be influenced by changing the observation instruments as well as retrieval models. Nevertheless, selecting the correct mathematical model is important in data analysis, especially in GNSS reflectometry, during the calculation of the dielectric constant or soil moisture because an established retrieval model is based on specific experimental results. This research applied a mathematical model that was developed for handling radiometer data and used to calculate the dielectric constant. This retrieval model is useful for estimating dielectric constant, because it only requires the reflectivity results from the instruments, though it is inevitable to contain errors in the dynamic range, because the model is built to implement radiometer data. On the contrary, [16] used the [PERSON] and Schmupge empirical model in computing the dielectric constant, which requires other parameters, such as frequency and temperature. Therefore, [16] represented the accurate dynamic range of the dielectric constant, even though this research produced significant results emphasizing a practical usage of GNSS Reflectometry instrument by only using the GNSS-R results and generating the dynamic range within the accurate dielectric constant range. In addition, the restriction of the incident angle in this analysis may affect the dielectric constant results, so needs to be further investigated. Yanco, TxSON, and Little Washita were used in the analysis, which are core validation sites for verifying SMAP soil moisture data using in-situ measurement data. These reference sites were selected for the characteristics of flat and grass field in order to ignore the effect of roughness in the reflected GNSS signals and concentrate on the computation process for dielectric constant.