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