Computation Approach for Quantitative Dielectric Constant from Time Sequential Data Observed by CYGNSS Satellites  
[PERSON]  
1 Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada; [EMAIL_ADDRESS] (S.B. ); [EMAIL_ADDRESS] (R.S.K.L. );  
[EMAIL_ADDRESS] (S.K.K.) [PERSON]  
1 Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada; [EMAIL_ADDRESS] (S.B. ); [EMAIL_ADDRESS] (R.S.K.L. );  
[EMAIL_ADDRESS] (S.K.K.) [PERSON]  
1 Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada; [EMAIL_ADDRESS] (S.B. ); [EMAIL_ADDRESS] (R.S.K.L. );  
For instance, inferring ocean roughness and ocean winds were examined in [9]. Over the last two decades, [10], and other similar research has examined sea ice coverage [11], soil moisture [12; 13; 14; 15; 16; 17; 18], and potentially mean sea slope and topography [19; 20; 21]. In addition, the compact size, low mass, and power consumption are major advantages of a GNSS-R receiver when compared with other remote sensing instruments, such as Synthetic Aperture Radar (SAR). Diverse research has implemented GNSS-R receivers on multiple platforms, from ground to airborne missions, and has estimated or inferred various geophysical characteristics of the Earth's surface [22; 23]. The NASA Cyclone Global Navigation Satellite System (CYGNSS) is the recent satellite mission that has popularized GNSS-R instruments, which is designed to measure wind speed over Earth's oceans and predict hurricanes using the GPS signals received by the Delay Doppler Map Instrument (DDMI) aboard each of the eight nanosatellites [24]. Soil moisture retrieval by GNSS-R remains a challenging problem in formulating the mathematical model and understanding other surface source effects on GNSS reflected signal changes. There has been significant research contributing to obtaining qualitative comparisons between the soil moisture content from reference data and GNSS-R reflectivity, which is the ratio of direct signal power from the zenith direction antenna and reflected signal power measured from the nadir direction antenna. For example, [12] demonstrated the positive linear correlation between soil moisture data from the SMAP satellite and reflectivity from CYGNSS satellites [12], and [25] evaluated the CYGNSS-derived relative (zenith versus nadir antenna) signal-to-noise ratio (SNR) to estimate soil moisture over a moderately vegetated region [26]. In addition, they attempted to retrieve soil moisture using UK's TechdemoSat-1 (TDS-1) and CYGNSS based on a back-propagation artificial neural network (BP-ANN) [27]. The referred approaches offer the valuable perspective of a direct relationship between soil moisture and GNSS-R relative SNR; however, understanding the physical relationship between dielectric constant and soil moisture constitutes an important part of soil related research. Amongst the parameters that are known to be related to soil moisture, such as surface soil temperature, composition of soil, and salinity, it is known that the relative dielectric constant presents a strong dependence on soil moisture contents, especially at microwave frequency ranges. [PERSON] presented an empirical polynomial model for dielectric constant as a function of volumetric water, clay, and sand contents based on five soil types, and a wide range of moisture conditions from 1.4 to 18 GHz; this is the range of microwave frequencies [28]. The Dobson model, which is also called the semi-empirical model [29], was developed on the basis of the same dielectric measurements as the [PERSON] model, where the observation frequency was extended down to 0.3 GHz. The more recent Mironov model is based on the refractive dielectric mixing model.