Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning

Delineating the grounding line of marine-terminating glaciers—where ice
starts to become afloat in ocean waters—is crucial for measuring and
understanding ice sheet mass balance, glacier dynamics, and their
contributions to sea level rise. This task has been previously done using
time-consuming, mostly-manual digitizations of differential
interferometric synthetic-aperture radar interferograms by human experts.
This approach is no longer viable with a fast-growing set of satellite
observations and the need to establish time series over entire continents
with quantified uncertainties. We present a fully-convolutional neural
network with parallel atrous convolutional layers and asymmetric
encoder/decoder components that automatically delineates grounding lines
at a large scale, efficiently, and accompanied by uncertainty estimates.
Our procedure detects grounding lines within 232 m in 100-m
posting interferograms, which is comparable to the performance achieved by
human experts. We also find value in the machine learning approach in
situations that even challenge human experts. We use this approach to map
the tidal-induced variability in grounding line position around Antarctica
in 22,935 interferograms from year 2018. Along the Getz Ice Shelf, in West
Antarctica, we demonstrate that grounding zones are one order magnitude
(13.3 ± 3.9) wider than expected from hydrostatic
equilibrium, which justifies the need to map grounding lines repeatedly
and comprehensively to inform numerical models.