This article presents an assessment of the horizontal accuracy and precision of the laser altimetry observations collected by NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission. We selected the terrain-matching method to determine the position of laser altimeter profiles within a precisely known surface, represented by a digital elevation model (DEM). We took this classical approach a step further, approximated the DEM by planar surfaces, and calculated the optimal position of the laser profile by minimizing the square sum of the elevation differences between reference DEMs and ICESat-2 profiles. We found the highly accurate DEMs of the McMurdo Dry Valleys (DV), Antarctica, ideal for this research because of their stable landscape and rugged topography. We computed the 3-D shift parameters of 379 different laser altimeter profiles along two reference ground tracks collected within the first two years of the mission. Analyzing these results revealed a total geolocation error (mean +1$\sigma $) of 4.93 m for version 3 and 4.66 m for version 4 data. These numbers are the averages of the six beams, expressed as mean +1$\sigma $and lie well within the mission requirement of 6.5 m.

Abstract There is no consensus on how quickly the earth's ice sheets are melting due to global warming, nor on the ramifications to sea level rise. Due to its potential effects on coastal populations and global economies, sea level rise is a grave concern, making ice melt rates an important area of study. The ice‐sheet science community consists of two groups that perform related but distinct kinds of research: a data community, and a model building community. The data community characterizes past and current states of the ice sheets by assembling data from field and satellite observations. The modeling community forecasts the rate of ice‐sheet decline with computational models validated against observations. Although observational data and models depend on one another, these two groups are not well integrated. Better coordination between data collection efforts and modeling efforts is imperative if we are to improve our understanding of ice sheet loss rates. We present a new science gateway, GHub, a collaboration space for ice sheet scientists. This web‐accessible gateway will host datasets and modeling workflows, and provide access to codes that enable tool building by the ice sheet science community. Using GHub, we will collect and centralize existing datasets, creating data products that more completely catalog the ice sheets of Greenland and Antarctica. We will build workflows for model validation and uncertainty quantification, extending existing ice sheet models. Finally, we will host existing community codes, enabling scientists to build new tools utilizing them. With this new cyberinfrastructure, ice sheet scientists will gain integrated tools to quantify the rate and extent of sea level rise, benefitting human societies around the globe.

Modeling time series (TS) is a research focus in cryospheric sciences because of the complexity and multiscale nature of events of interest. Highly nonuniform sampling of measurements from different sensors with different levels of accuracy, as is typical for measurements of ice sheet elevations, makes the problem even more challenging. In this article, we propose a spline-based approximation framework (ALPS—Approximation by Localized Penalized Splines) for modeling TS of land ice changes. The localized support of the B-spline basis functions enable robustness to nonuniform sampling, a considerable improvement over other global and piecewise local models. With features like discrete-coordinate-difference-based penalization and two-level outlier detection, ALPS further guarantees the stability and quality of approximations. ALPS incorporates rigorous model uncertainty estimates with all approximations. As demonstrated by examples, ALPS performs well for a variety of data sets, including TS of ice sheet thickness, elevation, velocity, and terminus locations. The robust estimation of TS and their derivatives facilitates new applications, such as the reconstruction of high-resolution elevation change records by fusing sparsely sampled TS of ice sheet thickness changes with modeled firn thickness changes, and the analysis of the relationship between different outlet glacier observations to gain new insights into processes and forcing.

Observations from satellite lidar instruments have provided evidence in the remarkable changes in polar ice sheets on a global scale. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled for launch by NASA in 2017 and will monitor the elevation changes of polar ice sheets and vegetation canopy. To validate ICESat-2's approach of photon-counting laser altimetry, measurements obtained from the Multiple Altimeter Beam Experimental Lidar (MABEL) instrument are critical. In support of the ICESat-2 mission, this paper derives an algorithm for the detection of ground and vegetation canopy in photon-counting laser altimeter data. This approach uses a density-based clustering model and modifies the shape of search area. Based on results from MABEL observations, the proposed approach is seen to be robust in detecting ground and vegetation canopy as well as background noise reduction. In addition, this approach can be quickly implemented and adaptive to photon-counting lidar data sets with different point cloud densities.

Lidar sensing has been found to be a useful method of monitoring the dynamics and mass balance of glaciers, ice caps, and ice sheets. However, it is also known that ice surfaces can have complex 3-dimensional structure, which can challenge their accurate retrieval with lidar sensing. In support of future lidar sensing satellite missions, such as the upcoming ICESat-2, a joint research project was recently initiated between the Rochester Institute of Technology (RIT) and the University at Buffalo to study lidar sensing of complex ice surfaces. This effort is supported by NASA's Remote Sensing Theory program and is aimed at advancing the science of lidar sensing. The general approach is to 1) define realistic complex ice surfaces, 2) render lidar image simulations, and 3) compare the resulting data to the known surfaces to gain insight into the phenomenology of lidar sensing of snow and ice. The project will build on existing scientific understanding of light scattering from snow and ice as well as lidar sensor system modeling with a systems engineering end-to-end perspective. Initial results show the simulations capturing realistic scattering of photons in snow volumes and the resulting point clouds measured by a model spaceborne lidar system.

The integrated program Surface Elevation Reconstruction And Change detection (SERAC) was specifically designed and developed for detecting surface elevation and elevation changes from the Ice Cloud and land Elevation Satellite (ICESat). ICESat carried geoscience laser altimeter system (GLAS) with the primary goal of measuring elevation changes of the polar ice sheets to sufficient accuracy to assess their impact on global sea level. GLAS had three lasers that operated sequentially, with two to three campaigns per year. The footprint size was about 70 m and the point-to-point spacing between neighboring laser points reached 170 m. SERAC copes with different scenarios. Originally developed for calculating surface elevation changes of crossover areas, it was extended to along-track areas and the inclusion of non-ICESat laser data, such as Airborne Topographic Mapper (ATM), an airborne laser scanning system developed by NASA Wallops Flight Facility. The adjustment system of SERAC simultaneously computes the shape of surface patches containing laser points of the same time epoch, estimates surface elevation changes, and approximates the time series of elevation changes by a polynomial after removing the seasonal cycle. Results shown in the second part of the paper demonstrate the potential of SERAC for calculating detailed ice sheet elevation and volume change histories. Greenland Ice Sheet volume changes, calculated from a combined ICESat/ATM data set, show good agreement with previously published results and provide improved sampling in the rapidly thinning coastal regions of southern Greenland. Moreover, the polynomial approximation of the time series of surface elevation changes is taken to advantage in the last example of Northwest Greenland, illuminating the intricate thinning/thickening patterns that often vary considerably over short spatial scales.

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.

We present a technique for automatically labeling segmented hyperspectral imagery with semantically meaningful material labels. The technique compares the mean signatures of each image segment to a spectral library of known materials, and material labels are assigned to image segments according to the most similar library entry. The similarity between spectral signatures is evaluated using our recently proposedCICRdsimilarity measure designed specifically for hyperspectral imagery. This measure considers both the continuum-intact reflectance spectrum and its continuum-removed representation. We provide a thorough assessment of this measure by comparison to several commonly used similarity measures on a well-studied low-altitude Airborne Visible/Infrared Imaging Spectrometer image of an urban area. We evaluate our results using both information-theoretic techniques and visual validation of the resulting spectral matches.

High resolution declassified Corona satellite photographs from the 1960s are used to establish baseline velocities for a fast flowing outlet glacier in Greenland. The mathematical model of the camera was developed and position and attitude parameters were estimated using GCPs. To illustrate the procedure, ice velocities were derived from Corona and Landsat-7 images over the Kangerdlugssuaq glacier in SE Greenland. Time series of velocity changes indicate significant increase in ice flow velocity during the last decade, propagating upstream from the calving front.