Quantifying GNSS-R Delay Sea State Bias and Predicting Its Variation Based on Ship-Borne Observations in China’s Seas

Global navigation satellite system reflectometry (GNSS-R) is able to achieve intensive and high-frequency measurements of global sea levels. The sea state bias (SSB) makes the measurement uncertain. Based on ship-borne GNSS-R observations in China’s Seas, we obtained reflected signal delay. We constructed a new mean sea surface (MSS) reflection surface model to calculate the modeled reflection delay. By combining the observation and modeled reflection delay, we separated and quantified the SSB. The SSB was approximately 1.3 m for spring and summer alternate seasons in China’s Seas, with obvious differences in the spatial distribution of sea area, latitude, and distance from shore. Based on the quantified SSB and sea state parameters, we introduced reflection incident angle parameter and constructed a new parameter model of SSB suitable for GNSS-R. We applied the parameter model to predict variations in delay SSB. The parameter model had a variance explanation rate of approximately 70%, it can reliably predict large-scale SSB variations, and it performed better in higher elevation scenarios. This letter proposes a regional approach to quantify GNSS-R delay SSB and to predict its variations, and it is expected to be applied to GNSS-R satellite altimetry to support SSB correction.