Sea-Level Estimation from GNSS-IR under Loose Constraints Based on Local Mean Decomposition

Area of Science:

• Geodesy
• Oceanography
• Remote Sensing

Background:

• Coastal sea-level monitoring is crucial for understanding climate change impacts.
• Traditional Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) methods face limitations in accuracy and flexibility due to reflector height and satellite-elevation ranges.
• Existing quadratic fitting models for GNSS-IR sea-level estimation are constrained by retrieval ranges, impacting their real-world applicability.

Purpose of the Study:

• To develop and evaluate a novel GNSS-IR sea-level estimation model combining Local Mean Decomposition (LMD) and Lomb-Scargle Periodogram (LSP).
• To improve the accuracy and stability of coastal sea-level monitoring using GNSS-IR.
• To assess the model's performance under varying constraints and its long-term monitoring capabilities.

Main Methods:

• Decomposition of signal-to-noise ratio (SNR) arcs using Local Mean Decomposition (LMD) to isolate sea surface information.
• Construction of an oscillation term from selected signal components.
• Frequency extraction of the oscillation term using Lomb-Scargle Periodogram (LSP).
• Evaluation using observational data from SC02 sites in the United States, analyzing performance across different reflector height (RH) and satellite-elevation ranges.

Main Results:

• The LMD-based model yields an oscillation term with a lower noise level compared to other signal separation methods.
• The new model demonstrates improved accuracy and avoids abnormal values in sea-level retrieval.
• The model maintains good performance even under relaxed constraints (wide RH range, high-elevation range).
• Annual sea-level retrieval results show significant improvements over quadratic fitting, with average reductions in root mean square error (RMSE) and mean absolute error (MAE) of 8.34% and 8.87%, respectively.

Conclusions:

• The combined LMD-LSP model offers a substantial advancement for GNSS-IR coastal sea-level estimation.
• This novel approach enhances accuracy, stability, and flexibility, overcoming limitations of traditional quadratic fitting methods.
• The model proves effective for long-term, high-precision coastal sea-level monitoring.