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.

Observations reveal end of summer Arctic sea ice extent is declining at an accelerating rate. Model projections underestimate this decline and continue to have a broad spread in forecasted September sea ice extent. This suggests some important summer processes, such as melt pond formation and evolution, may not be properly represented in current models. Melt ponds form on the sea ice surface as snow melts, and pools in low lying areas on the sea ice surface. The evolution of the ponds depends on snow depth, ice thickness, and surface conditions. Melt water may spread across a level surface, or be confined to depressions between sea ice ridges. Ponds decrease the albedo of the surface and enhance the positive ice albedo feedback, accelerating further melt. Until recently, Arctic-wide observations of individual melt ponds were not available. ICESat-2, a photon counting laser altimeter launched in 2018, provides high resolution detail of sea ice and snow topography due to its unique combination of a small footprint (~12 m) and high-resolution along-track sampling (0.7 m). The green laser (532 nm) is able to penetrate water, enabling melt pond depth measurements. We have developed methods to track the melt pond surface and bathymetry in ICESat-2 data to determine melt pond depth. We also track melt pond evolution through application of a sea ice classification algorithm to 10 m resolution Sentinel-2 imagery. The combination of these two datasets allows for an evolving, three-dimensional view of the melting sea ice surface. We focus on the evolution of summer melt on multiyear ice in the Central Arctic north of Greenland and Canada in 2020. Our findings are put in context of existing literature on melt pond depth, volume, and evolution. We also discuss our results in relation to the melt pond fraction north of the Fram Strait, where we expect different ice conditions in the vicinity of the 2020 MOSAiC field studies. Observational data products comprising melt pond fraction and pond depth are being developed for public distribution. These products may be of interest to those studying under-ice light and biology, as well as modelers who are interested in understanding the evolution of melt pond parameters for model initialization and validation.

Glacial acceleration is the largest source of uncertainty in sea-level-rise assessment, according to the Intergovernmental Panel on Climate Change. Of the different types of glacial acceleration, surging is the least understood. In this paper, we demonstrate how a combination of automated algorithms dedicated to analysis of two entirely different observation types - satellite altimetry from NASA’s ICESat-2 and satellite imagery from Planet SkySat - can aid in advancing glaciology, utilizing state-of-the art remote sensing /Earth observation technology. NASA’s Ice, Cloud and land Elevation Satellite ICESat-2, launched 15~September~2018, carries the first space-borne multi-beam micro-pulse photon counting laser altimeter system, the Advanced Topographic Laser Altimeter System (ATLAS). ATLAS observations are collected in three pairs of weak and strong beams with 0.7m nominal along-track spacing (under clear-sky conditions). The recording of the observations as a photon-point cloud requires a dedicated algorithm for identification of signal photons and determination of surface heights. As a solution, we developed the density-dimension algorithm for ice surfaces, the DDA-ice. ATLAS data analyzed with the DDA-ice allow determination of heights over heavily crevassed ice surfaces, which are characteristics of accelerating glaciers. The study presented here builds on a special multi-component data set, obtained through synoptic observations of an Arctic glacier system during surge (Negribreen, Svalbard): Airborne altimeter and image data collected during our ICESat-2 validation campaign, and SkySat image data from a special acquisition collected as part of NASA’s Commercial Smallsat Data Acquisitions Pilot program. These are complemented by WorldView (Maxar) and ESA Sentinel-1 data. With a spatial resolution of 0.7-0.86m, SkySat data and WorldView lend themselves to automated classification of crevasse types. Altogether, we obtain a characterization in 3 dimensions that allows discrimination of ice-surface types from surging glaciers (Negribreen) and continuously fast-moving and accelerating glaciers (Jakobshavn Isbrae) based on morphological characteristics.