Calibration and Validation of CYGNSS Reflectivity through Wetlands’ and Deserts’ Dielectric Permittivity

Abstract

The reflection of Global Navigation Satellite Systems (GNSS) signals, namely GNSS-Reflectometry (GNSS-R), has recently proven to be able to monitor land surface properties in the microwave spectrum, at a global scale, and with very low revisiting time. Moreover, this new technique has numerous additional advantages, including low cost, low power consumption, lightweight and small payloads, and near real-time massive data availability, as compared to conventional monostatic microwave remote sensing. However, the GNSS-R surface reflectivity values estimated through the bistatic radar equation, and the Fresnel coefficients have shown a lack of coincidence with real surface reflectivity data, mostly due to calibration issues. Previous studies have attempted to avoid this matter with direct regression methods between uncalibrated GNSS-R reflectivity data and external soil moisture content (SMC) products. However, calibration of GNSS-R reflectivity used in traditional inversion models is still a challenge, such as those to estimate SMC, freeze/thaw, or biomass. In this paper, a successful procedure for GNSS-R reflectivity calibration is established using data from the CYGNSS (Cyclone GNSS) constellation. The scale and bias parameters are estimated from the theoretical dielectric properties of water and dry sand, which are well-known and empirically validated values. We employ four calibration areas that provide maximum range limits of reflectivity, such as deserts and wetlands. The CYGNSS scale factor and the bias parameter resulted in a = 3.77 and b = 0.018, respectively. The derived scale and bias parameters are applied to the CYGNSS dataset, and the retrieved SMC values through the Fresnel reflection coefficients are in excellent agreement with the Soil Moisture Active Passive (SMAP) SMC product. Then, the SMAP SMC is used as a reference true value, and provides a standard linear regression with an R-square coefficient of 0.803, a root mean square error (RMSE) of 0.084, and a Pearson’s correlation coefficient of 0.896.

An Illustration of FY-3E GNOS-R for Global Soil Moisture Monitoring

“…The main difference between GNOS-R and SMAP-R is their polarization properties; the latter uses HR and VR polarization, which are very different from the present GNOS-R, CYGNSS, and other GNSS-R missions. However, the future GNOS-R payloads on FY-series satellites will have the potential of full polarization [41], which will benefit the vegetation and soil moisture monitoring. With the help of the fully physical scattering models designed oriented to GNOS-R, i.e., the LAGRS model, the scattering properties of different land cover types, such as desert, highly forested, or around water, can be analyzed in detail and the corresponding retrieval methods for these land types will be presented in our following work.…”

“…In terms of the incident angle, we limit our data to angles lower than 65 • , which is a range widely adopted in CYGNSS angle limitation. In terms of the signal-to-noise ratio (SNR), we pass over all the SNRs lower than −5 dB [41]. This part is often called quality control (QC).…”

Using Robust Regression to Retrieve Soil Moisture from CyGNSS Data

“…In recent decades, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated significant potential for retrieving geophysical parameters. Although the technique was originally designed to measure sea winds in tropical oceans [5], some recent studies and projects have demonstrated the sensitivity of reflected signals from the land surface to hydrological parameters, such as SM [6][7][8], mapping flood [9,10], wetlands [11,12], and vegetation [13,14]. Retrieving SM from spaceborne GNSS-R data has become a research hotspot due to its unique advantages (such as faster revisit time, all-weather operation, and lower payload costs).…”