Optimization of Automated Sea Ice Melt Pond Depth Determination in ICESat-2 Laser Altimeter Data with the DDA-bifurcate-seaice Algorithm Using Airborne Campaign Data

Thomas Trantow, Ute C. Herzfeld, Mia Vanderwilt, Kutalmis Saylam, Nathan Kurtz, Huilin Han, Rachel Tilling

Melt ponding on Arctic sea ice is a key indicator of the transition from a predominantly perennial to a seasonal sea ice cover, yet quantitative data on pond depth remain limited. Here, we present the first analysis of melt-pond depth using ICESat-2’s Advanced Topographic Lidar Altimeter System (ATLAS). The Density-Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-bifurcate-seaice) automatically detects multiple surface returns in ICESat-2 photon data and estimates corresponding surface heights, enabling melt-pond-depth retrievals under varied noise conditions.

Airborne lidar and imagery collected during the NASA ICESat-2 Project Arctic Summer Sea Ice Campaign (July 2022) provide near-coincident observations used to evaluate and optimize the algorithm’s melt-pond detection. Evaluation of the melt-pond-depth quantile using Chiroptera data shows the uniform value used in the ATL07 release 7 data product is near-optimal. We demonstrate DDA-bifurcate-seaice’s capability to detect a wide range of melt feature morphologies, including smooth or rough bottoms, ridge-adjacent ponds, partial drainage, and seawater intrusion. To further improve depth determination, we propose a depth-quantile function that reduces bias and mean-squared-error by a factor of 2.75 and 2.2 respectively. This work improves melt-pond-depth estimation using the DDA-seaice-bifurcate, supporting Arctic- and Antarctic-wide mapping in the upcoming (release 7) ICESat-2/ATLAS experimental sea-ice melt-pond data product.