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)
Journal Article
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.
