Coincident satellite data refer to remote sensing observations from different platforms that capture the same geographic location within a short temporal window. Such data enable multi-modal and multi-view analysis—particularly for dynamic systems like the Arctic, where images captured just hours apart can reflect vastly different conditions (e.g., moving sea ice), complicating data integration. However, acquiring coincident data from different satellite platforms remains labor-intensive and computationally demanding. Finding coincident data is challenging due to separate query systems, inconsistent file formats, and the lack of built-in tools to compute cross-platform time differences. Researchers often settle for loosely aligned observations, compromising temporal alignment precision and analysis quality. To address this challenge, we introduce the Coincident Data Discovery Engine (CoDD), a platform designed to facilitate the discovery and access to spatially and temporally coincident remote sensing data across multiple platforms. CoDD provides data from seven widely used satellite missions spanning optical, SAR, and LiDAR modalities, with a maximum temporal gap of 72 hours and temporal granularity down to one second. The platform features an intuitive user interface that enables efficient querying, visualization, and download of coincident data. CoDD is already supporting diverse ongoing research such as Arctic change monitoring, satellite product validation, and ground truth generation for super-resolution models.

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 identified on the ICESat-2 standard sea-ice 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 0.7 m nominal along-track spacing 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.

Analysis of high-resolution imagery acquired by the Digital Mapping System during annual, late-winter NASA Operation IceBridge surveys of Arctic sea ice between 2010 and 2018 reveals that pressure ridge sail heights ( HS) vary regionally and interannually. We find distinct differences in HSdistributions between the central Arctic (CA) and the Beaufort/Chukchi Seas region. Our results show that differences with respect to ice type occur within the tails of the HSdistributions and that the 95th and 99th percentiles of HSare strong indicators of the predominant ice type in which the pressure ridge formed. During the first part of the study period HSincreased, with the largest sails observed in the winters of 2015 and 2016, after which HSdeclined, suggesting that the most heavily deformed sea ice may have drifted beyond the area surveyed and exited the CA. Our analysis of the interannual and regional variability in sea ice deformation in the western Arctic during the last decade provides an improved understanding of sail height that will help advance ridge parameterizations in sea ice models.

Only in the past eight years has the feasibility of using satellite-borne altimeters to estimate sea ice freeboard and thickness been demonstrated, and these estimates still have uncertainties primarily associated with limited knowledge of snow loading on sea ice. Because accurate estimates of Arctic-wide sea ice thickness and volume are fundamental inputs to global climate models, validation of satellite-derived thickness estimates using independent data is required. A detailed assessment of freeboard retrieved by the Geoscience Laser Altimeter System (GLAS) aboard the Ice, Cloud, and land Elevation Satellite has been carried out using high-resolution laser altimetry from the National Aeronautics and Space Administration's Airborne Topographic Mapper (ATM), the Delay-Doppler radar altimeter, and digital photography collected along a 300-km segment of sea ice in the Canada Basin. Exploiting the repeat coverage of the aircraft flight line, a correction was applied to GLAS footprint geolocations to adjust for sea ice drift that occurred during the time between satellite and aircraft acquisitions. Comparisons of GLAS and ATM measurements over sea ice show excellent agreement (about a 0.00-m mean) with no apparent bias between data sets. Freeboard estimates were examined using data from GLAS and ATM independently, employing measurements over refrozen leads to estimate local sea surface heights (SSHs). The results demonstrate the sensitivity of freeboard and thickness calculations to an accurate estimation of local SSH. Snow depth derived by differencing laser and radar data was combined with the freeboard estimates to yield a mean sea ice thickness of ~ 5.5 m over a 250-km subsection of the flight track.

The detection of leads, or cracks, in sea ice is critical for the derivation of sea-ice freeboard from altimetric measurements of sea-ice elevation. We present an approach for lead detection in sea ice using high-resolution visible imagery from airborne platforms. We develop a new algorithm, i.e., the sea-ice lead detection algorithm using minimal signal (SILDAMS), that detects clouds, extracts leads, and classifies ice types within leads from airborne visible imagery. Cloud detection is based on an assessment of local variances of pixel brightness across image scenes and where available coincident altimetric measurements are used to confirm suspected cloudy scenes. The lead extraction step computes affine time-frequency distributions (minimal signal) for the Red, Green, and Blue channels of each image. The transformed outputs are combined to take advantage of three channels simultaneously. Finally, lead pixel geolocations are extracted using a set of uniform thresholds for ice typing (including open water, thin ice, and gray ice) within leads along each flight line. SILDAMS was tested using data from the Digital Mapping System (DMS). DMS digital photographs represent the highest resolution ( ≈10 cm) visible imagery available over sea ice and were collected during NASA Operation IceBridge sea-ice flights in the Antarctic and the Arctic in 2009 and 2010, respectively. We demonstrate that SILDAMS has a high lead detection capability of 99%.

We present a first assessment of airborne laser and radar altimeter data over snow-covered sea ice, gathered during the National Aeronautics and Space Administration Operation IceBridge Mission. We describe a new technique designed to process radar echograms from the University of Kansas snow radar to estimate snow depth. We combine IceBridge laser altimetry with radar-derived snow depths to determine sea ice thickness. Results are validated through comparison with direct measurements of snow and ice thickness collected in situ at the Danish GreenArc 2009 sea ice camp located on fast ice north of Greenland. The IceBridge instrument suite provides accurate measurements of snow and ice thickness, particularly over level ice. Mean IceBridge snow and ice thickness agree with in situ measurements to within$\sim$0.01 and$\sim$0.05 m, respectively, while modal snow and ice thickness estimates agree to within 0.02 and 0.10 m, respectively. IceBridge snow depths were correlated with in situ measurements ($R = 0.7$, for an averaging length of 55 m). The uncertainty associated with the derived IceBridge sea ice thickness estimates is 0.40 m. The results demonstrate the retrieval of both first-year and multiyear ice thickness from IceBridge data. The airborne data were however compromised in heavily ridged ice where snow depth, and hence ice thickness, could not be measured. Techniques developed as part of this study will be used for routine processing of IceBridge retrievals over Arctic sea ice. The limitations of the GreenArc study are discussed, and recommendations for future validation of airborne measurements via field activities are provided.

Satellite and aircraft observations have revealed that remarkable changes in the Earth's polar ice cover have occurred in the last decade. The impacts of these changes, which include dramatic ice loss from ice sheets and rapid declines in Arctic sea ice, could be quite large in terms of sea level rise and global climate. NASA's Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), currently planned for launch in 2015, is specifically intended to quantify the amount of change in ice sheets and sea ice and provide key insights into their behavior. It will achieve these objectives through the use of precise laser measurements of surface elevation, building on the groundbreaking capabilities of its predecessor, the Ice Cloud and Land Elevation Satellite (ICESat). In particular, ICESat-2 will measure the temporal and spatial character of ice sheet elevation change to enable assessment of ice sheet mass balance and examination of the underlying mechanisms that control it. The precision of ICESat-2's elevation measurement will also allow for accurate measurements of sea ice freeboard height, from which sea ice thickness and its temporal changes can be estimated. ICESat-2 will provide important information on other components of the Earth System as well, most notably large-scale vegetation biomass estimates through the measurement of vegetation canopy height. When combined with the original ICESat observations, ICESat-2 will provide ice change measurements across more than a 15-year time span. Its significantly improved laser system will also provide observations with much greater spatial resolution, temporal resolution, and accuracy than has ever been possible before.