GNSS-IR Sea Level Retrieval Combining Quality Control with Inter-Frequency Bias Correction

Objectives: Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has been proved to be able to monitor sea level. Improving accuracy is the key to GNSS-IR sea level retrieval based on signal-to-noise ratio (SNR) data. Method: This paper proposes a new quality control method considering the sharpness of spectrum peak on the basis of the common SNR spectrum quality control methods, which those methods jointly control the quality of the initial retrievals. Then a second-order dynamic sea surface correction model considering inter-frequency bias is established by combining the new quality control method used for weighting with inter-frequency bias correction, which is realizes multi-frequency multi-system data fusion and error correction of initial retrievals. Results: The GNSS data collected from SC02 in USA and HKQT in China's Hong Kong was processed in the experiment. The accuracy of initial retrievals is generally improved by more than 1 cm after using the new quality control method. The second-order dynamic sea surface correction model considering inter-frequency bias is applied to initial retrievals and improves accuracy by more than 3 cm. When the observation environment and data quality are favorable, the accuracy of GNSS-IR sea level retrieval reach centimeter level, but the retrievals are poor when the wind speed is more than 20 m/s. Under the circumstance of larger time window, the model proposed in this paper has better dynamic sea surface correction effect than the firstorder model, and the retrieval accuracy is significantly improved than the model without taking into account the inter-frequency bias. Conclusion: The new quality control method can effectively control the occurrence of gross errors. The second-order dynamic sea surface correction model considering inter-frequency bias has better correction effect than the conventional first-order model and the model without considering the inter-frequency bias.