Correction method for scale bias in GNSS-IR sea level retrieval

Since the principles and methods of Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) sea level monitoring technology based on geodetic receivers were first proposed, the technology has undergone over a decade of development. Currently, this method typically utilizes the frequency of the SNR signal, converts it into the reflector height (RH), and further derives the sea level. Height variation error and tropospheric delay error are widely recognized as systematic errors. Both have corresponding mathematical models that can be used for error mitigation. Random errors and gross errors are generally handled or constrained through quality control or multi-GNSS combination methods. In 2024, a correction method based on the initial phase of the SNR arc was proposed, and related studies suggest it may exhibit superior performance in correcting errors. Beyond the aforementioned errors, many studies have observed a scale bias in inversion results that appears to be correlated with sea-level magnitude. This bias is generally believed to be related to tropospheric delay error, as the magnitude of the tropospheric delay is associated with RH magnitude. However, in many studies, the magnitude of the scale bias exceeds the magnitude of tropospheric delay model value. Through analysis, we infer that the scale bias in some studies is related to the assumption of uniform sea level changes within the time windows during multi-GNSS combination processing. This assumption introduces errors, leading to underestimation of tidal peaks and overestimation of tidal troughs. The primary objective of this paper is to correct scale biases in GNSS-IR sea level retrieval, including those exhibited by raw RHs and those observed in combined RHs. Considering that stations with larger tidal amplitudes tend to show more significant scale biases, GNSS data from two such stations, BRST and HKQT, were used. The results indicate that the phase-based error correction method is more effective than traditional correction method in reducing scale bias in raw RHs. Additionally, we propose a method to mitigate scale bias caused by the assumption of uniform changes. Results indicate that this method effectively corrects scale bias in the combined RHs.