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

• Main Authors: Mohajerani, Yara, Jeong, Seongsu, Scheuchl, Bernd, Velicogna, Isabella, Rignot, Eric, Milillo, Pietro
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2021
• Subjects: Antarc*; Antarctic; Antarctica; Getz Ice Shelf; Ice Sheet; Ice Shelf; West Antarctica
• Online Access: https://doi.org/10.7280/D1VD6G

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. The grounding lines for the entire Antarctic coastline for available Sentinel1-a/b tracks in 2018 are provided as Shapefiles for the 6-day and 12-day tracks separately, as "AllTracks_6d_GL.shp" and "AllTracks_12d_GL.shp" respectively. The corresponding uncertainty estimates are also provided, as described in the manuscript, which are labelled as "AllTracks_6d_uncertainty.shp" and "AllTracks_12d_uncertainty.shp". Each grounding line in the Shapefile contains 6 attribudes: ID: grounding line ID for each DInSAR scene Type: ...