C058-01
ICESat-2 melt depth retrievals: Revisiting surface melt on Amery Ice Shelf, East Antarctica

Tuesday, 15 December 2020: 11:30
Virtual
Helen Amanda Fricker1, Philipp A Arndt2, Kelly M Brunt3, Rajashree Datta4,5, Zachary Fair6, Michael F Jasinski7, Jonathan Kingslake8, Lori A Magruder9, Mahsa S Moussavi10, Julian Spergel8, Jeremy Stoll11 and Bert Wouters12, (1)Scripps Institution of Oceanography, La Jolla, CA, United States, (2)Scripps Institution of Oceanography, La Jolla, United States, (3)Goddard Earth Science Technology and Research, Greenbelt, MD, United States, (4)City College, City University of New York, New York, NY, United States, (5)NASA Goddard Space Flight Center, Cryospheric Sciences Laboratory, Code 615, Greenbelt, NY, United States, (6)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (7)NASA Goddard Space Flight Ctr, Greenbelt, MD, United States, (8)Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, United States, (9)University of Texas at Austin, Austin, TX, United States, (10)University of Colorado at Boulder, Boulder, CO, United States, (11)Science Systems and Applications, Inc., Lanham, MD, United States, (12)IMAU/TU Delft, Utrecht/Delft, Netherlands
Abstract:
The volume of surface melt produced on the Antarctic and Greenland ice sheets has been difficult to monitor accurately because depth estimates are challenging. NASA’S ICESat-2 brings a new capability to surface melt detection: 532 nm photons penetrate through water and are reflected from both the water and the underlying ice; the difference between the two provides a depth estimate. Several ICESat-2 tracks sampled Amery Ice Shelf during the January 2019 melt season. We initiated a pilot project with several investigators who contributed depth estimates for four melt features with depths ranging to 6m, using seven different algorithms based on ICESat-2, and compared them with estimates from two algorithms based on coincident Landsat MSS and Sentinel-2 images. We compared depth estimates with a manual dataset created as a baseline, in the absence of ground truth. We show that all algorithms are able to successfully identify the presence of surface water, at the same locations. The algorithms based on ICESat-2 data produce the most accurate depths, with each algorithm capturing different degrees of structural detail, and the image-based algorithms tending to underestimate depths. This implies that previous estimates of meltwater volumes have been too low, and that ICESat-2 depths can be used to tune image-based algorithms, moving us close to accurate estimates of meltwater volumes across Antarctica and Greenland.