Improving CyGNSS-Based Land Remote Sensing: Track-Wise Data Calibration Schemes

Cyclone Global Navigation Satellite System (CyGNSS) data have been used for generating several intermediate products, such as surface reflectivity (G), to facilitate a wide variety of land remote sensing applications. The accuracy of G relies on precise knowledge of the effective instantaneous radiative power (EIRP) of the transmitted GNSS signals in the direction of the specular reflection point, the precise knowledge of zenith antenna patterns which in turn affects estimates of EIRP, the good knowledge of receive antenna patterns etc. However, obtaining accurate estimates on these parameters completely is still a challenge. To solve this problem, in this paper, an effective method is proposed for calibrating the CyGNSS G product in a track-wise manner. Here, two different criteria for selecting data to calibrate and three reference options as targets of the calibrating data are examined. Accordingly, six calibration schemes corresponding to six different combinations are implemented and the resulting G products are assessed by (1) visual inspection and (2) evaluation of their associated soil moisture retrieval results. Both visual inspection and retrieval validation demonstrate the effectiveness of the proposed schemes, which are respectively demonstrated by the immediate removal/fix of track-wisely noisy data and obvious enhancement of retrieval accuracy with the calibrated G. Moreover, the schemes are tested using all the available CyGNSS level 1 version 3.0 data and the good results obtained from such a large volume of data further illustrate their robustness. This work provides an effective and robust way to calibrate the CyGNSS G result, which will further improve relevant remote sensing applications in the future.

Automated summary

This paper proposes an effective method for calibrating the CyGNSS G product, by considering different criteria and target data, implementing six calibration schemes, and evaluating the resulting products through visual inspection and soil moisture retrieval results. The effectiveness and robustness of the schemes are demonstrated, providing an efficient way to improve relevant remote sensing applications in the future.