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.

Melt ponds play an important role in the seasonal evolution of Arctic sea ice. During the melt season, snow atop the sea ice begins to metamorphose and melt, forming ponds on the ice. These ponds reduce the albedo of the surface, allowing for increased solar energy absorption and thus further melting of snow and ice. Analyzing the spatial distribution and temporal evolution of melt ponds helps us understand the sea ice processes that occur during the summer melt season. It has been shown that the inclusion of melt pond parameters in sea ice models increases the skill of predicting the summer sea ice minimum extent. Previous studies have used remote sensing imagery to characterize surface features and calculate melt pond statistics. Here we use new observations of melt ponds obtained by the Digital Mapping System (DMS) flown onboard NASA Operation IceBridge (OIB) during two Arctic summer melt campaigns which surveyed thousands of kilometers of sea ice and resulted in more than 45,000 images. One campaign was conducted in the Beaufort Sea (July 2016), and one in the Lincoln Sea and the Arctic Ocean north of Greenland (July 2017). Using these data we expect to advance our understanding of the differences and similarities between melt pond features on young, thin sea ice seen in the Beaufort Sea versus those on multi-year ice. We have developed a pixel-based classification scheme by considering the different RGB spectral values associated with each surface type. We identify four sea ice surface types (level ice, rubbled ice, open water, and melt ponds). The classification scheme enables the calculation of parameters including melt pond fraction, ice concentration, melt pond area, and melt pond dimensions. We compare results with data from the Airborne Topographic Mapper (ATM), a laser altimeter also operated during these OIB missions. Given the extent over which the OIB data are available, regional information may be derived. Leveraging existing satellite data products, we examine whether the high-resolution airborne statistics are representative of the region and can be scaled up for comparison against satellite-derived parameters such as ice concentration and extent.

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.

The Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2 offers a new remote sensing capability to measure complex sea ice surface topography. We demonstrate the retrieval of six sea ice parameters from ICESat-2/ATLAS data: surface roughness, ridge height, ridge frequency, melt pond depth, floe size distribution and lead frequency. Our results establish that these properties can be observed in high fidelity, across broad geographic regions and ice conditions. We resolve features as narrow as 7 m, and achieve a vertical height precision of 0.01 m, representing a significant advance in resolution over previous satellite altimeters. ICESat-2 employs a year-round observation strategy spanning all seasons, across both the Arctic and Southern Oceans. Because of its higher resolution, coupled with the spatial and temporal extent of data acquisition, ICESat-2 observations may be used to investigate time-varying, dynamic and thermodynamic sea ice processes.

During the Arctic summer season, snow atop the sea ice melts and pools into low-lying areas on the surface. These melt ponds reduce surface albedo and increase solar absorption in the Arctic Ocean. Throughout the summer, melt ponds grow, drain, and connect, through a complex drainage system. Current melt pond schemes in sea ice models, such as the level-ice scheme in the Los Alamos Sea Ice Model (CICE), rely on a linear relationship between pond depth and fraction to predict the evolution of pond growth as the snow and sea ice melt. Although the inclusion of melt ponds in models has been shown to improve forecasts of end-of-summer sea ice extent, observations of melt pond depth and fraction guiding these models are from SHEBA, a spatially-limited field campaign which occurred over 20 years ago. Until recently, melt ponds characteristics have been difficult to resolve from spaceborne platforms due to their small size (10s - 100s m in diameter), and indistinguishable radiometric similarity to open water. Here we show that new, high-resolution laser altimetry measurements from ICESat-2 (IS2), combined with coincident high-resolution satellite imagery, provides a three-dimensional view of the melting sea ice cover. IS2, launched in September 2018, has now observed two summer melt seasons in the Arctic. IS2 operates at 532 nm, a wavelength that penetrates low turbidity water, and can therefore be used to capture the bathymetry of shallow water features. Building on previous work, we demonstrate IS2’s ability to detect and measure melt ponds on multiyear sea ice. We validate the existence of melt ponds with high resolution (10 m) visible imagery from the Sentinel-2 (S2) MultiSpectral Instrument. We apply the “density dimension algorithm – bifurcate” (DDA-bifurcate), an auto-adaptive algorithm utilizing data aggregation with the ability to track two surfaces, as well as a second algorithm that tracks melt pond surface and bottom, to derive melt pond depth for dozens of melt ponds in 2019 and 2020. Applying a sea ice surface classification algorithm to S2 imagery, we are able to determine melt pond fraction. We compare our findings of coincident melt pond fraction and depth with the melt pond parameterization used in the level-ice scheme in CICE. We discuss our results in the context of the existing literature on pond depth and volume.

ICESat-2 (IS2), launched in September 2018, has been providing high-resolution measurements of Arctic sea ice for two years. IS2’s main instrument, the Advanced Topographic Laser Altimeter System (ATLAS), is a photon-counting lidar with a pulse frequency of 10 kHz, translating to 0.7 m along-track sampling. We use high-resolution ATLAS measurements with a new algorithm, to derive sea ice surface elevations for all available IS2 reference ground tracks over sea ice for the 2019-2020 winter period. Sea ice surface elevations are used to calculate sea ice parameters including surface roughness, ridge sail height, and ridge frequency for the period October 2019 to April 2020. Near coincident Airborne Topographic Mapper (ATM) lidar data were collected along IS2 orbits during the Spring 2019 NASA Operation IceBridge flight campaign. Using ATM data as a validation tool, we show that, when compared to the existing ATL07 sea ice surface height product, our algorithm discerns pressure ridge frequency and height more accurately while maintaining measurement precision at under 2 cm. We show monthly variability in the probability distributions for these sea ice parameters, with respect to ice type. Following our previous studies, we show that pressure ridges have distinct characteristics depending on the ice type in which the ridge was formed. These results demonstrate the utility of photon-counting lidar for deriving new parameterizations of sea ice surface roughness, ridge sail height, and ridge frequency, that may be used to advance high-resolution sea ice modeling.

We investigate the characteristics and distribution of pressure ridges in Arctic sea ice using surface height profiles from the Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2. Applying a new algorithm to ATLAS measurements we derive the frequency and height of individual pressure ridges and map surface roughness and ridging intensity at the basin scale over three winters between 2019 and 2021. Comparisons with near-coincident airborne lidar data show that not only can we detect individual ridges 5.6 m wide, but also measure sail height more accurately than the existing ICESat-2 sea ice height product. We find regional variability in ridge morphology is large while annual variability is low. Ridge characteristics are not only related to their parent ice type but also their geographic location. High-resolution satellite altimetry data are valuable for characterizing sea ice deformation at short length-scales, providing observations that will advance ridge parameterizations in sea ice models.

Since its launch, in September 2018, ICESat-2’s Advanced Topographic Laser Altimeter System (ATLAS) has collected high-resolution measurements of Arctic sea ice by sampling the surface every 70 cm along-track. We utilize the high-resolution capabilities of ATLAS with a novel algorithm called the University of Maryland-Ridge Detection Algorithm (UMD-RDA) to investigate sea ice topography across a range of scales. Applying the UMD-RDA to the ATL03 Global Geolocated Photon product we measure surface roughness and derive the frequency, height, width, and angle of individual pressure ridge sails. Aggregating data from multiple orbit crossings per day we investigate ridge characteristics at length-scales varying from 1 km (individual floes) to the pan-Arctic scale (central Arctic Ocean). Here, we present an evaluation of pressure ridge characteristics during the winter seasons of 2018/19, 2019/20, and 2020/21, comparing results from distinct regions with varying ice conditions. Near-coincident, independent observations of pressure ridges with Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) lidar data, OIB near-coincident Continuous Airborne Mapping By Optical Translator (CAMBOT) high-resolution (~15 cm) optical imagery, and WorldView high-resolution (~30 cm) panchromatic satellite imagery are used to evaluate the accuracy of our ICESat-2 ridge detection scheme. There are many potential use-cases for a high-resolution sea ice topography data product within the community, ranging from navigational hazard mitigation to ecological studies of marine mammal habitats. We discuss plans for releasing these data products and discuss the improvements such data would make within high-resolution sea ice models.