## 5 Discussion  
Dielectric constant, which is a crucial indicator for soil moisture retrieval, has been estimated using the products from a GNSS-R instrument, specifically from the CYGNSS satellite constellation mission. In this study, sequential and seasonal variations of dielectric constant have been evaluated using the method, as described in [43]. The dielectric constant has been validated by the comparisons of the reference data to the SMAP satellite-derived soil moisture, and it has been statistically quantified by low values of confirmation metrics. A few subjects are addressed in this section in order to identify the parameters that influence the determination of dielectric constant from GNSS-R and soil moisture. Each topic is  
Figure 10: Seasonal scattering plots centered on the spring and fall equinoxes and summer and winter solstices using dielectric constant estimated from CYGNSS with the new technique and measured by SMAP at Little Washita, Oklahoma in U.S.  
required to be analyzed further in order to anticipate the quantitative soil moisture content determination using GNSS-R data.  
1. Vegetation type or land classification: the classification of land can be of concern due to the parameters that affect the calculation of dielectric constant when the reflectivity is transformed to the Fresnel reflection coefficient (Equation (3)). Therefore, an investigation, including vegetation type, is essential for improving the qualities of soil moisture or dielectric constant retrieval. Over the past few decades, there has been an attempt to classify land type by observing topography using satellite instruments, such as the International Geosphere-Biosphere Programme (IGBP) Land Classification. With comprehensive analysis regarding reflections over distinct land classifications, the results from GNSS-R will be similar to the reference instrument. NDVI or Freeze/thaw state will also be related to the dielectric constant, as referred to in [52]. Elaborated analysis for figuring out the characteristics of derived dielectric constant will be done in future research.
2. Spatial resolution: in this research, the EASE grid v2.0 with 36 km \(\times\) 36 km of resolution is applied to build two-dimensional images and to acquire information from assigned pixels according to specified locations. From the perspective of global images, the 36 km resolution is good enough to produce usable imagery. The land surface and other classifications, such as urban, cropland, or grassland, were included in the designated pixel. In other words, two specular points in the same pixel are possible for experiencing the reflection on different land classifications. Because the averaging of data over the same pixel has been implemented, the resulting dielectric constant estimates must contain an error. Increasing spatial resolution and investigating land classification is required to remove errors. However, the specific approach is demanding, because high spatial resolution can cause statistical errors for short periods of observations.
3. Soil moisture model: a soil moisture model was applied to convert soil moisture from SMAP to the dielectric constant. In this conversion, the semi-empirical model is used [29]; although, the semi-empirical model was developed based on the in-situ measurements of microwave frequency waves rather than a remote sensing technique. Because the semi-empirical soil moisture model is not specialized to GNSS-R, it consistently contains unexpected errors in conversion. Even though it was established to deal with remote sensing observation results, it still required particular soil moisture to produce coincident results while using GNSS-R.