Altimetry Waveform Classification and Retracking Strategy for Improved Coastal Altimetry Products
- Indian Institute of Technology Kanpur, Kanpur, India (shubhka@iitk.ac.in)
Coastal zones exhibit unique altimetry signal characteristics, primarily influenced by the presence of land artifacts. The shape of the altimetry echo serves as a distinctive marker, representing the physical parameters of the surface it originates from. Open ocean reflections for SAR (Synthetic Aperture Radar) mode yield signals with a steep leading edge and a trailing edge modeled by a negative exponential function. In contrast, land areas in coastal zones typically produce specular and quasi-specular waveforms. The presence of specific waveform classes is further influenced by seasonality and changes in land use and patterns such as coastal erosion.
This study aims to classify altimetry waveforms in coastal zones at various global sites and subsequently retrack the identified waveform classes using an optimal retracking strategy. Site selection is based on the availability of in-situ tide gauge data. Waveform classification is achieved using a Long Short-Term Memory (LSTM) auto-encoder, capturing the temporal nature of waveforms and providing an 8-dimensional feature representation. In addition, the LSTM-autoencoder provides de-noised waveforms, which are used for subsequent retracking processes.
Different waveform shapes necessitate specific retracking strategies. While an Ocean retracker suffices for SAR waveforms over open oceans, it is inadequate for retracking specular, quasi-specular, and multi-peak waveforms. Advanced retracking algorithms such as OCOG, Threshold, ALES, Beta-5, and Beta-9 are employed based on the waveform class.
To validate the proposed strategy, the performance of the altimetry product, sea level anomalies, and retracking outcomes are compared with established coastal altimetry products like XTRACK, in-situ tide gauge data, and popular retracking algorithms like OCOG, Ocean retracker, Threshold, Beta-5 and Beta-9. Sea level anomalies are derived from sensor geophysical data records (SGDR) of altimetry missions and compared with existing coastal altimetry products and in-situ tide gauge records. Evaluation metrics such as Pearson's correlation coefficient and root mean square error assess the agreement in seasonal and yearly trends, as well as the accuracy of measurements.
This comprehensive analysis aims to validate the effectiveness of the proposed coastal waveform post-processing strategy, showcasing its ability to quantify long-term sea level trends and explore regional variations.
How to cite: Kant, S. and Devaraju, B.: Altimetry Waveform Classification and Retracking Strategy for Improved Coastal Altimetry Products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-770, https://doi.org/10.5194/egusphere-egu24-770, 2024.
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