STAR: Spatio-Temporal Altimeter Waveform Retracking using Sparse Representation and Conditional Random Fields

TL;DR

The paper introduces STAR, a novel spatio-temporal retracking method for satellite radar altimetry that improves sea surface height estimation accuracy over open ocean and coastal regions by integrating neighboring waveform information and advanced detection techniques.

Contribution

STAR combines sparse representation, conditional random fields, and graph algorithms to enhance waveform retracking, especially in distorted coastal and inland water waveforms, outperforming existing methods.

Findings

STAR achieves comparable or better accuracy than existing methods.

It provides more reliable data over larger temporal spans.

Sea surface heights are less affected by outliers.

Abstract

Satellite radar altimetry is one of the most powerful techniques for measuring sea surface height variations, with applications ranging from operational oceanography to climate research. Over open oceans, altimeter return waveforms generally correspond to the Brown model, and by inversion, estimated shape parameters provide mean surface height and wind speed. However, in coastal areas or over inland waters, the waveform shape is often distorted by land influence, resulting in peaks or fast decaying trailing edges. As a result, derived sea surface heights are then less accurate and waveforms need to be reprocessed by sophisticated algorithms. To this end, this work suggests a novel Spatio-Temporal Altimetry Retracking (STAR) technique. We show that STAR enables the derivation of sea surface heights over the open ocean as well as over coastal regions of at least the same quality as compared to existing retracking methods, but for a larger number of cycles and thus retaining more useful data. Novel elements of our method are (a) integrating information from spatially and temporally neighboring waveforms through a conditional random field approach, (b) sub-waveform detection, where relevant sub-waveforms are separated from corrupted or non-relevant parts through a sparse representation approach, and (c) identifying the final best set of sea surfaces heights from multiple likely heights using Dijkstra's algorithm. We apply STAR to data from the Jason-1, Jason-2 and Envisat missions for study sites in the Gulf of Trieste, Italy and in the coastal region of the Ganges-Brahmaputra-Meghna estuary, Bangladesh. We compare to several established and recent retracking methods, as well as to tide gauge data. Our experiments suggest that the obtained sea surface heights are significantly less affected by outliers when compared to results obtained by other approaches.