Land Elevation Satellite Research Articles

Comparisons of Satellite and Airborne Altimetry With Ground‐Based Data From the Interior of the Antarctic Ice Sheet

AbstractA series of traverses has been conducted for validation of the National Aeronautics and Space Administration Ice, Cloud, and land Elevation Satellite 2 (ICESat‐2) on the flat interior of the Antarctic ice sheet. Global Navigation Satellite System data collected on three separate 88S Traverses intersect 20% of the ICESat‐2 reference ground tracks and have precisions of better than ±7 cm and biases of less than ∼4 cm. Data from these traverses were used to assess heights from ICESat‐2, CryoSat‐2, and Airborne Topographic Mapper (ATM). ICESat‐2 heights have better than ±3.3 cm bias and better than ±7.2 cm precision. ATM heights have better than 9.3 cm bias and better than ±9.6 cm precision. CryoSat‐2 heights have −38.9 cm of bias and ±47.3 cm precision. These best case results are from the flat ice‐sheet interior but provide a characterization of the quality of satellite and airborne altimetry.

Subglacial discharge weakens the stability of the Ross Ice Shelf around the grounding line

In this paper, we examine potential impact of discharge in Subglacial Lake Engelhardt, West Antarctica, on the stability of the Ross Ice Shelf around the grounding line by combining satellite altimetry and remote sensing images. According to satellite altimetry data from the Ice, Cloud and Land Elevation Satellite (ICESat; 2003–06), Subglacial Lake Engelhardt (SLE) discharged ca. 1.91 ± 0.04 km3 of water into the downstream region. The ice-surface record derived from ICESat (2006–09) and CryoSat-2 (2011–17) data shows that the lake gained ca. 2.09 ± 0.05 km3 of water during the refilling event following the drainage event, taking three times as much time to reach the previous water level before the discharge; the calculation demonstrates that water input from an upstream lake is unable to sustain water increase in SLE, indicating that the subglacial, hydrologic system and groundwater flow could have contributed to water increase in SLE via hydrologic networks. Satellite images captured surface depressions and crevasses at the drainage outlet point of hydrologic networks around the grounding line; satellite altimetry data show that the ice surface there is still depressing even though the subglacial discharge has finished, potentially reflecting the long-term impact of subglacial discharge on the stability of the immediate Ross Ice Shelf around the grounding line.

Refraction correction and coordinate displacement compensation in nearshore bathymetry using ICESat-2 lidar data and remote-sensing images.

In nearshore bathymetry based on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), water refraction causes a position displacement of the seafloor signal photon, decreasing the bathymetric accuracy. A novel refraction-correction method is proposed for the ICESat-2. Based on the experimental results and statistical analysis, it is illustrated that the displacement in elevation direction reaches several meters at the relative depth. The displacement in the along- and cross-track directions is very small, ranging from millimeters to centimeters. Finally, the relationship of refraction displacements with various incident angles and water depths is analyzed in detail, revealing the nearshore bathymetry characteristics of ICESat-2.

Characterization of ice shelf fracture features using ICESat-2 – A case study over the Amery Ice Shelf

Fractures are important structural features that affect the stress condition and stability of ice shelves. Previous studies have mainly focused on the measurement of fractures in the horizontal dimension. However, the vertical morphology of fractures could also be potentially important in determining their evolution and role in ice shelf stability. In this regard, the dense and high-resolution surface elevation measurements collected by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provide an excellent opportunity for studying fractures in the vertical dimension over a regional scale. Here we developed an object-oriented algorithm to automatically detect and characterize fracture features (including rifts, surface fractures, and the surface expressions of basal fractures) from ICESat-2 data. We successfully applied the algorithm to ICESat-2 L3A Land Ice Height (ATL06) data over the Amery Ice Shelf in East Antarctica, and retrieved the vertical structural information of two major fracture fields. The detected fracture features match well with the visible fracture lines in Landsat-8 satellite imagery, and the edges of fracture features are well-captured from the ATL06 data. We analyzed the fracture patterns in terms of surface depth, width, vertical shape, length, and orientation. We found that the fracture feature depth is a key factor determining the formation of new fractures and the spatial pattern of depth is closely related to rift formation. We anticipate that further application of the object-oriented method over other ice shelves will produce important insights regarding fracture formation and ice shelf vulnerability by combining ICESat-2-derived vertical information with the horizontal structure retrieved from satellite imagery.

Multiscale Fusion Signal Extraction for Spaceborne Photon-Counting Laser Altimeter in Complex and Low Signal-to-Noise Ratio Scenarios

Extracting signal photons from noisy raw data is one of the critical processes for the new generation of spaceborne photon-counting laser altimeter. Affected by vast noise photon-counting events, the extraction of weak signal events still faces challenges in complex scenarios with low signal-to-noise ratio (SNR). Aiming to improve the extraction ability of signal photon events in these scenarios, a multiscale fusion signal extraction method was proposed, characterized by combining global spatial correlation constraint with optimized local spatial correlation constraint. The local constraint is implemented based on a density-based spatial clustering of applications with noise (DBSCAN) clustering method with adaptive parameter estimation, which is used to extract possible signal photons. A subsequent global constraint based on the spatial correlation of the terrain profiles is designed to remove the pseudo-signal photons clustered in the local constraints’ step. The global constraint is implemented based on a cost function, which is used to quantify different candidate paths. Our method was verified based on the actual Ice, Cloud, and land Elevation satellite-(ICESat2) data containing vegetation, mountains, and residential areas. The experimental results show that compared with the ICESat-2 extraction method, our method can significantly improve the precision and recall rate of signal photon events from the low SNR photon-counting data.

An active-passive fusion strategy and accuracy evaluation for shallow water bathymetry based on ICESat-2 ATLAS laser point cloud and satellite remote sensing imagery

ABSTRACT The Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) uses a 532 nm band photon-counting LiDAR (Light Detection and Ranging), which has certain penetrability to water bodies, and the measured data show that the bathymetric ability reaches nearly one Secchi depth. ATLAS has a limited number of beams and a fixed ground track, and only collects section elevation along the track direction. The fusion of active laser point cloud and passive optical remote sensing satellite image can fill the gap of shallow water depth data in a large range. This paper takes ATLAS as the research object, with the aim of exploring the effective algorithm flow of spaceborne active laser and passive optical fusion processing, and systematically evaluating the bathymetric accuracy of the fusion algorithm. An adaptive Gauss filtering technology based on the density of point cloud was firstly improved to achieve accurate denoising under the condition of uneven surface/underwater density. Subsequently, the depth of underwater points was calculated automatically through steps of water surface modelling, refraction correction, etc. Finally, the control points and check points were randomly extracted to solve the parameters of multispectral inversion model and verify the internal accuracy of the model. In this paper, the accuracy of ATLAS bathymetry was verified by Airborne LiDAR Bathymetry (ALB) data in Oahu Island, Hawaii, and the results indicate that the vertical root mean square error (RMSE) ranges from 0.56 m to 1.11 m. In Yongle islands and Qilianyu area of the South China Sea, WorldView-2 (WV2) 4 bands multispectral images and ATLAS data were used to carry out the active-passive fusion bathymetry, and the ALB and sonar data were used to evaluate the accuracy. Experimental results show that the internal compliance accuracy of the fusion model is better than 1.25 m (RMSE), and the real bathymetry accuracy is better than 1.42 m (RMSE). The above results reveal the great potential of active-passive fusion bathymetry based on ICESat-2 and other high-resolution remote sensing satellites, which can provide strong technical support for filling the blank of shallow water depth information.

Satellite Remote Sensing Image Stereoscopic Positioning Accuracy Promotion Based on Joint Block Adjustment With ICESat-2 Laser Altimetry Data

The next generation Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) was launched in 2018 to provide large amounts of spaceborne laser altimetry data. The joint block adjustment with laser altimetry data and satellite remote sensing images can promote limited stereoscopic positioning accuracy without ground control points (GCPs). However, there are two problems in the joint block adjustment, that is, the reliable ATLAS laser altimetry points (LAPs) selection and the discrepancies of image-object points. To solve the above problems, a surface point cloud extraction algorithm based on wavelet reconstruction was proposed. Then, the LAPs selection constraint model was established, so the terrain influence was resolved. Finally, a block joint adjustment method has been proposed with remote sensing images and the reliable LAPs considering error of their plane coordinates, which could achieve the accurate corresponding image points of the reliable LAPs, remarkably decrease the discrepancies of image-object points. Fourteen pairs of ATLAS data, seven ZY3-02 stereo images and eleven Mapping Satellite-1 stereo images in Zhengzhou, China were collected to validate the method performance. Experiment results have shown that the consistency of the image-object points and the positioning accuracy of stereoscopic images without GCPs have been improved. Compared with the free net block adjustment, the height, east and north positioning accuracy of ZY3-02 and Mapping Satellite-1 stereo images have increased by 61%, 56%, 60% and 56%, 38%, 37%, respectively.

Deriving Highly Accurate Shallow Water Bathymetry From Sentinel-2 and ICESat-2 Datasets by a Multitemporal Stacking Method

Empirical models have been widely used to retrieve shallow water bathymetry from multispectral/hyperspectral satellite imagery. In traditional studies on deriving the topography and monitoring its temporal changes, a single date satellite image without clouds corresponded to a bathymetric map and multidate images corresponded to multiple bathymetric maps. The satellite image noise caused by various environmental conditions and satellite sensors can inevitably introduce errors or gaps in deriving bathymetric maps. Also, empirical models are limited in some remote areas due to the lack of prior bathymetric points. In this article, using only satellite data, including multitemporal Sentinel-2 images and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data, a multitemporal stacking method was developed to derive highly accurate and cloud free shallow water bathymetry with accuracy of approximately 1 m and the depth range exceeding 22 m. The proposed method was tested and validated by an airborne bathymetric lidar. To be specific, our method using multitemporal Sentinel-2 images can achieve a mean root mean square error (RMSE) of 1.08 m (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.94) by comparing with in-situ airborne lidar data around Ganquan Island, which is better than the result (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.92, RMSE = 1.46 m) derived from single date image based methods.Also, the gaps in a bathymetric map due to clouds or other noise can be avoidable benefitting from the stacking of multiple date satellite images. In the future, this satellite data driven method can be further extended to the globe to produce highly accurate and cloud free bathymetry around clear shallow water benefited from prior ICESat-2 bathymetric data.

Morphological Profiles of Sand Dunes from ICESat-2 Geolocated Photons

Aeolian process leads to the transportation and accumulation of sand particles that result in sand dune landforms. The structure and shape of the sand dunes are driven by the parameters of interacting wind force and the material composition of sand within. Cross-section profiles over the sand dunes will essay the geomorphological parameters through which the steady state and rate of sand transport can be computed. National Aeronautics and Space Administration’s novel satellite namely Ice, Cloud and land Elevation Satellite-2 (ICESat-2) hosts a solo sensor namely Advanced Topographic Laser Altimeter System (ATLAS) which is a photon counting instrument that measures the round-trip time of the light pulse being emitted and reflected back from the surface determines the true height of the topographic feature on the Earth. In this article, cross-section profiles generated from the beams of ICESat-2 ground-tracks acquired over sand dunes of the Thar Desert region were analysed for detecting the geomorphological parameters. Observations from the cross-section profiles have resulted in giving unprecedented details about the shapes and morphological settings of various types of sand dunes like barchanoids, parabolic, longitudinal, and transverse dunes. Morphological parameters of sand dunes like the length of the stoss slope, crest height, slip face details, inter-arms spacing, height of the trailing arms, length of the depositional lobes, and sinuosity of the recurring crest lines were retrieved with ease from the Level-2A data product namely ATL03 of ICESat-2/ATLAS.

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched on September 15, 2018. It is the first photon-counting laser altimeter satellite, which is of great significance for the research into laser altimetry. ICESat-2 is, however, highly sensitive and susceptible to environmental influences. In addition to surface returns, a lot of nonsurface photons are found in the data. It is, therefore, necessary to study an effective method to separate the surface signal from background information. In this article, we review the existing surface detection methods for photon point cloud data and select seven methods for comparison. Four sources of photon-counting data were considered in the experiments: The Multiple Altimeter Beam Experimental Lidar (MABEL), the Chinese Multibeam LiDAR, The Advanced Topographic Laser Altimeter System (ICESat-2&#x002F;ATLAS), and MATLAS (using MABEL data to simulate the expected ATLAS photon point cloud). Four scenarios of land, land ice, sea ice, and ocean were also considered. Each surface detection method was tested in 12 experiments, and the different methods were finally compared by qualitative and quantitative measures. We were, thus, able to establish the advantages and disadvantages of each method, which will be of great significance for scholars studying surface detection methods.

Annual Maps of Forests in Australia from Analyses of Microwave and Optical Images with FAO Forest Definition

The Australian governmental agencies reported a total of 149 million ha forest in the Food and Agriculture Organization of the United Nations (FAO) in 2010, ranking sixth in the world, which is based on a forest definition with tree height &gt; 2 meters . Here, we report a new forest cover data product that used the FAO forest definition ( tree cover &gt; 10 % and tree height &gt; 5 meters at observation time or mature) and was derived from microwave (Phased Array type L-band Synthetic Aperture Radar, PALSAR) and optical (Moderate Resolution Imaging Spectroradiometer, MODIS) images and validated with very high spatial resolution images, Light Detection and Ranging (LiDAR) data from the Ice, Cloud, and land Elevation Satellite (ICESat), and in situ field survey sites. The new PALSAR/MODIS forest map estimates 32 million ha of forest in 2010 over Australia. PALSAR/MODIS forest map has an overall accuracy of ~95% based on the reference data derived from visual interpretation of very high spatial resolution images for forest and nonforest cover types. Compared with the canopy height and canopy coverage data derived from ICESat LiDAR strips, PALSAR/MODIS forest map has 73% of forest pixels meeting the FAO forest definition, much higher than the other four widely used forest maps (ranging from 36% to 52%). PALSAR/MODIS forest map also has a reasonable spatial consistency with the forest map from the National Vegetation Information System. This new annual map of forests in Australia could support cross-country comparison when using data from the FAO Forest Resource Assessment Reports.

Evaluation of Ice, Cloud, And Land Elevation Satellite-2 (ICESat-2) land ice surface heights using Airborne Topographic Mapper (ATM) data in Antarctica

ABSTRACT The land ice surface heights derived from Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) were assessed by comparing to the near-coincident measurements from the Airborne Topographic Mapper (ATM) Light Detection and Ranging (LiDAR) system during the 2018 Operation IceBridge mission in Antarctica. Over the marginal and interior Antarctic Ice Sheet, the ICESat-2 ice surface height was accurate to less than 14 cm, and remarkable correlations (R 2 = 1) were found through the comparison to the ATM measurements. Centimetre-scale accuracy was seen for all ICESat-2 beams, ranging from −13 cm to −3 cm. The ICESat-2 land ice surface height performance was affected by the terrain conditions (i.e., surface roughness and slope); larger height differences could be seen when the surface roughness or slope values were higher as it becomes difficult for photons to accurately capture height distributions in areas with complex measurement geometry. No evident performance difference was found for the ICESat-2 beams; weak beams can still be used for height measurement. The fine spatial resolution and centimetre-scale accuracy of the ICESat-2 land ice elevation product make it one of the most suitable data sources for ice surface height measurements.

Accuracy Assessment of the ICESat-2/ATL06 Product in the Qilian Mountains Based on CORS and UAV Data

Ice, Cloud and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System 06 (ICESat-2/ATL06) data can be widely used for various applications, such as monitoring changes in glacier thickness. In this article, we use field surveys based on continuously operating reference system (CORS) and unmanned aerial vehicles (UAVs) to verify the elevation accuracy of the ATL06 product. A total of 208 ATL06 footprints were selected for an accuracy assessment by obtaining 348 CORS global positioning system (GPS) measurements and deriving a digital elevation model (DEMs) from 2683 UAV images in four experimental areas with different terrain and landform types in the Qilian Mountains, China. The experiments show that ATL06 data have very high vertical accuracy and horizontal positioning accuracy; the average root-mean-square error (RMSE) yielded by CORS is 0.0846 m, and the corresponding RMSE yielded by the UAV data is 0.1517 m. Furthermore, the positioning accuracy was also affected by the terrain slope.

Surface heights and crevasse morphologies of surging and fast-moving glaciers from ICESat-2 laser altimeter data - Application of the density-dimension algorithm (DDA-ice) and evaluation using airborne altimeter and Planet SkySat data

NASA’s Ice, Cloud and land Elevation Satellite ICESat-2, launched September 15, 2018, carries the first space-borne multi-beam micro-pulse photon-counting laser altimeter system, the Advanced Topographic Laser Altimeter System (ATLAS). Observations from ATLAS are acquired in three pairs of weak and strong beams with 0.7 ​m nominal along-track spacing (under clear-sky conditions). The recording of the observations as a photon point cloud, which includes signal and background/noise events, requires a dedicated algorithm for identification of signal photons and determination of surface heights. The objectives of this paper are to demonstrate that measurements from ICESat-2 allow determination of heights over heavily crevassed ice surfaces and yield elevation profiles that present morphological characteristics that are typical of fast-moving and accelerating glaciers. Surface-height determination from the photon point cloud is facilitated by the density-dimension algorithm for ice surfaces, the DDA-ice. The DDA-ice returns surface heights at the 0.7 ​m sensor resolution for strong and weak beams, it utilizes a radial basis function for data aggregation and automatically adapts to changing environmental conditions and background characteristics, including time of day and apparent surface reflectance. In contrast, the official Land-Ice Along-Track Height Product, ATL06, provides surface heights at 40 ​m resolution with 20 ​m postings. The DDA-ice signal classification consistently identifies photons from complex reflectors in both the strong and weak ATLAS beams and hence constitutes a significant advance over the signal classification on the ATL03 Global Geolocated Photons Product. Results are evaluated using (1) airborne laser altimeter data collected during our ICESat-2 validation campaign over Negribreen, Svalbard, during surge, and (2) high-resolution (0.72 ​m or 0.86 ​m) satellite image data from Planet SkySat acquired over Ilulissat Ice Stream (Jakobshavn Isbræ), Greenland. Using DDA-ice analysis, ICESat-2 data allow discrimination of ice-surface types from surging glaciers (Negribreen) and continuously fast-moving and accelerating glaciers (Jakobshavn Isbræ) based on morphological characteristics.

Correction of Time-Varying Baseline Errors Based on Multibaseline Airborne Interferometric Data Without High-Precision DEMs

The time-varying baseline error (TVBE) caused by the residual motion errors is one of the most significant error sources in the airborne interferometric synthetic aperture radar (InSAR). Although global digital elevation model (DEM) products can be used to extract the TVBE for areas without high-precision DEMs, they cause remarkable height differences (HDs) relative to the actual InSAR-measured topography. In addition, due to the low flight height of airborne SAR platforms, the range-dependent phases resulted from the HDs have significant impacts on the TVBE estimation. Through the mutual restraint of multibaseline observations, this research establishes a multibaseline parameterized model jointly considering HDs and TVBEs in each range direction for the absolute phase calibration. A weighted truncated singular value decomposition (TSVD) algorithm is used to solve the ill-posed problem in joint parameter estimation. Note that, to improve the TVBE extraction, a two-step strategy is adopted. In step one, TVBEs and HDs are preliminarily estimated by the multibaseline parameterized model. After that, bare areas with flat topography, where the truths of HDs can be assumed to be 0, are selected to correct the bias of the estimated HDs. If the flat and bare areas are absent, global ecosystem dynamics investigation (GEDI) or Ice, Cloud, and land Elevation Satellite (ICESAT) points can be used instead. In step two, the multibaseline parameterized model is updated by the calibrated DEM and then used to reestimate the TVBEs. The proposed method was validated by using a simulated data set and two real data sets (i.e., the P- and L-band data sets acquired by the airborne E-SAR system during the BioSAR2008 campaign). The results show that, with respect to the reference values, the root-mean-square error (RMSE) of the calibrated DEMs is 0.48 m for the simulated data set, 2.6 m for the P-band data set, and 3.6 m for the L-band data set, suggesting that the high-precision DEMs are well reconstructed and the TVBE-induced phases can be precisely extracted. After correcting the TVBE-induced phases, the accuracy of the DEMs generated by every linked phase (LP) is improved by at least 38.2% for P-band and by at least 51.0% for L-band. To assess the effectiveness and practicability of the proposed model, we further investigated the influence of the limited multibaseline number, and the baseline combination, and the precision of external DEMs on the TVBE correction. In conclusion, without high-precision DEMs, the multibaseline parameterized method can work well in accurately extracting the TVBE, even for the limited airborne InSAR data set.

Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden

Much attention is paid to the estimation of forest biomass-related variables (stem volume and above-ground biomass) with synthetic aperture radar (SAR) backscatter images because of the increasing number of sensors in space providing global and repeated coverage and the sensitivity of the backscattered intensity to forest properties. One of the most popular models used to estimate a biomass-related variable from SAR backscatter observations is the Water Cloud Model (WCM) because of its simplicity allowing for a straightforward retrieval. Nonetheless, a common feature of these estimates is the tendency to over- or underestimate specific ranges due to simplifying assumptions in the model. In this study, the WCM has been revisited by exploring pathways for a physically-based, Light Detection and Ranging (LiDAR)-aided, model parameterization at larger scale with the overall aim to reduce systematic retrieval errors associated with empirical assumptions in the model. The study was undertaken in Sweden where repeated observations of backscatter by the Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) were available. The integration was prototyped in Sweden thanks to detailed allometries relating forest variables in the WCM. These were derived from spatially dense estimates of canopy density and vegetation height from observations by the Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) and measurements of height and stem volume from the Swedish National Forest Inventory (NFI). The SAR backscatter predicted by the revisited WCM was in strong agreement with the observations. When evaluated against stem volumes estimated from the NFI data, the SAR-based stem volumes presented strong dispersion at the pixel level. Average stem volume at the level of five or more pixels, i.e., for an area larger than 0.3 ha, were instead unbiased and similar to the average values obtained from the NFI data (relative root mean square error: 21.4%, estimation bias: 0.9 m3/ha and coefficient of determination: 0.67). This study demonstrates that the integration of allometries in the WCM effectively reduces estimation errors. The method here prototyped in Sweden qualifies to provide large-scale estimates of biomass-related variables using multiple observations of L-band backscatter with potential application worldwide.

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth’s surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period.

Using ICESat-2 and Operation IceBridge altimetry for supraglacial lake depth retrievals

Abstract. Supraglacial lakes and melt ponds occur in the ablation zones of Antarctica and Greenland during the summer months. Detection of lake extent, depth, and temporal evolution is important for understanding glacier dynamics. Previous remote sensing observations of lake depth are limited to estimates from passive satellite imagery, which has inherent uncertainties, and there is little ground truth available. In this study, we use laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) over the Antarctic and Greenland ablation zones and the Airborne Topographic Mapper (ATM) for Hiawatha Glacier (Greenland) to demonstrate retrievals of supraglacial lake depth. Using an algorithm to separate lake surfaces and beds, we present case studies for 12 supraglacial lakes with the ATM lidar and 12 lakes with ICESat-2. Both lidars reliably detect bottom returns for lake beds as deep as 7 m. Lake bed uncertainties for these retrievals are 0.05–0.20 m for ATM and 0.12–0.80 m for ICESat-2, with the highest uncertainties observed for lakes deeper than 4 m. The bimodal nature of lake returns means that high-confidence photons are often insufficient to fully profile lakes, so lower confidence and buffer photons are required to view the lake bed. Despite challenges in automation, the altimeter results are promising, and we expect them to serve as a benchmark for future studies of surface meltwater depths.

Differences Between ICESat and CryoSat‐2 Sea Ice Thicknesses Over the Arctic: Consequences for Analyzing the Ice Volume Trend

AbstractTwo sources of readily available and non‐overlapping space‐borne data on sea ice thickness over the Arctic (i.e., Ice, Cloud, and land Elevation Satellite [ICESat] and CryoSat‐2 [CS2]) were compared using overlapping thermal microwave radiometer data to examine their respective differences. As a bridge connecting these two data sets, data on the vertically polarized emissivity difference between 10.65 and 18.7 GHz derived from the Advanced Microwave Sounding Radiometer were related to ICESat and CS2 thickness values via regression. The results indicate that there are substantial and systematic differences in the ice thickness between these two data sets over the Arctic; ICESat ice thickness was systematically lower than that of CS2 by ~50 cm, compared to ice thickness from Operation IceBridge and upper looking sonar data. The CS2 thickness observed later than that of ICESat was found to be thicker, contradicting the thinning expected under global warming conditions. Correcting for the 50‐cm difference between ICESat or CS2 data revealed trends in ice volume that are consistent with the expected and modeled declines in the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System data, further corroborating the 50 cm of relative bias observed between the two data sets.

Early ICESat-2 on-orbit Geolocation Validation Using Ground-Based Corner Cube Retro-Reflectors

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), an Earth-observing laser altimetry mission, is currently providing global elevation measurements. Geolocation validation confirms the altimeter’s ability to accurately position the measurement on the surface of the Earth and provides insight into the fidelity of the geolocation determination process. Surfaces well characterized by independent methods are well suited to provide a measure of the ICESat-2 geolocation accuracy through statistical comparison. This study compares airborne lidar data with the ICESat-2 along-track geolocated photon data product to determine the horizontal geolocation accuracy by minimizing the vertical residuals between datasets. At the same location arrays of corner cube retro-reflectors (CCRs) provide unique signal signatures back to the satellite from their known positions to give a deterministic solution of the laser footprint diameter and the geolocation accuracy for those cases where two or more CCRs were illuminated within one ICESat-2 transect. This passive method for diameter recovery and geolocation accuracy assessment is implemented at two locations: White Sands Missile Range (WSMR) in New Mexico and along the 88°S latitude line in Antarctica. This early on-orbit study provides results as a proof of concept for this passive validation technique. For the cases studied the diameter value ranged from 10.6 to 12 m. The variability is attributed to the statistical nature of photon-counting lidar technology and potentially, variations in the atmospheric conditions that impact signal transmission. The geolocation accuracy results from the CCR technique and airborne lidar comparisons are within the mission requirement of 6.5 m.