Herzfeld_et.TGRS.ms_icesat2.seaice.bif.TGRS.format.20220930.v6.sm1.pdf (7.05 MB)
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Automated Detection and Depth Determination of Melt Ponds on Sea Ice in ICESat-2 ATLAS Data — The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-bifurcate-seaice)

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posted on 2022-10-10, 15:26 authored by Ute HerzfeldUte Herzfeld, Thomas Trantow, Huilin Han, Ellen Buckley, Sinead Farrell, Matthew Lawson

As climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover is imminent, understanding melt ponding is central to understanding changes in the new Arctic. NASA's Ice, Cloud and land Elevation Satellite (ICESat-2) has the capacity to provide measurements  and monitoring of the onset of melt in the Arctic and on melt progression. Yet ponds are currently not reported on the ICESat-2 standard sea-ice products because of the low resolution of the products, in which only a single surface is determined.

The objective of this paper is to introduce a mathematical algorithm that facilitates automated detection of melt ponds in ICESat-2 ATLAS data, retrieval of two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds. With the Advanced Topographic Laser Altimeter System (ATLAS), ICESat-2 carries the first space-borne multi-beam micro-pulse photon-counting laser altimeter system, operating at 532~nm frequency. ATLAS data are recorded as clouds of discrete photon points. The Density-Dimension Algorithm for bifurcating sea-ice reflectors (DDA-bifurcate-seaice) is an auto-adaptive algorithm that solves the problem of pond detection near  the 0.7m nominal alongtrack resolution of ATLAS data, utilizing the radial basis function for calculation of a density field and a threshold function that automatically adapts to changes in background, apparent surface reflectance and some instrument effects. The DDA-bifurcate-seaice is applied to large ICESat-2 data sets from the 2019 and 2020 melt seasons in the multi-year Arctic sea-ice region. Results are evaluated by comparison to those from a manually forced algorithm.


NASA Earth Sciences 80NSSC20K097

NASA Earth Sciences 80NSSC22K1155

NASA Earth Sciences 80NSSC18K1439

NASA Earth Sciences NNX17AG75G

NASA Earth Sciences 80NSSC20K0966

NASA Earth Sciences 80NSSC17K0006


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University of Colorado Boulder

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  • United States of America