Antarctic digital elevation models (DEMs) are essential
for fieldwork, ice motion tracking and the numerical modelling of the ice sheet.
In the past 30 years, several Antarctic DEMs derived from satellite data
have been published. However, these DEMs either have coarse spatial
resolution or aggregate observations spanning several years, which
limit their further scientific applications. In this study, the
new generation satellite laser altimeter Ice, Cloud, and Land Elevation
Satellite-2 (ICESat-2) is used to generate a new Antarctic DEM for both the
ice sheet and ice shelves. Approximately 4.69 × 109 ICESat-2
measurement points from November 2018 to November 2019 are used to estimate
surface elevations at resolutions of 500 m and 1 km based on a
spatiotemporal fitting method. Approximately 74 % of Antarctica is
observed and the remaining observation gaps are interpolated using the
normal kriging method. The DEM is formed from the estimated elevations in
500 m and 1 km grid cells, and is finally posted at the resolution of 500 m.
National Aeronautics and Space Administration (NASA) Operation IceBridge (OIB)
airborne data are used to evaluate the generated Antarctic DEM (hereafter
called the ICESat-2 DEM) in individual Antarctic regions and surface types.
Overall, a median bias of -0.19 m and a root-mean-square deviation of 10.83
m result from approximately 5.2 × 106 OIB measurement
points. The accuracy and uncertainty of the ICESat-2 DEM vary in relation to
the surface slope and roughness, and more reliable estimates are found in
the flat ice sheet interior. The ICESat-2 DEM is comparable to other DEMs
derived from altimetry, stereophotogrammetry and interferometry. Similar
results are found when comparing to elevation measurements from kinematic Global Navigation Satellite System
(GNSS) (GPS and the Russian GLONASS) transects. The elevations of high
accuracy and ability of annual updates make the ICESat-2 DEM an addition to
the existing Antarctic DEM groups, and it can be further used for other
scientific applications. The generated ICESat-2 DEM (including the map of
uncertainty) can be downloaded from National Tibetan Plateau Data Center,
Institute of Tibetan Plateau Research, Chinese Academy of Sciences at
https://data.tpdc.ac.cn/en/disallow/9427069c-117e-4ff8-96e0-4b18eb7782cb/ (last access: 27 June 2022)
(Shen et al., 2021a, 10.11888/Geogra.tpdc.271448).
Introduction
Knowledge of the detailed surface topography in Antarctica is essential for
fieldwork, ice motion tracking (Bamber et al., 2000) and the numerical
modelling of the ice sheet (Cornford et al., 2015). Digital elevation models
(DEMs) of Antarctica, for example, can be used for presenting the topography
of ice sheets and ice shelves and thus provide a crucial reference for ice
dynamics and glacier velocities (Wesche et al., 2007; Slater et al., 2018),
which is necessary for Antarctic mass balance monitoring and potential sea
level rise contribution estimation (Ritz et al., 2015; Mengel et al., 2018).
Due to the remoteness of Antarctica, most of the previously published
Antarctic DEMs were derived from satellite or airborne data, e.g. elevation
measurements from radar altimeters (Fricker et al., 2000; Helm et al., 2014;
Slater et al., 2018), laser altimeters (DiMarzio et al., 2007), a
combination of radar and laser altimeters (Bamber et al., 2009),
stereophotogrammetry (Korona et al., 2009; Cook et al., 2012; Howat et al.,
2019) and interferometry (Wessel et al., 2021). The currently available
continent-scale Antarctic DEMs include one DEM derived from ICESat
(hereafter called the ICESat DEM, DiMarzio et al., 2007), one based on the
combination of ICESat and ERS-1 elevation measurements (hereafter called the
ICESat/ERS-1 DEM, Bamber et al., 2009), two DEMs derived from CryoSat-2
(hereafter called the Helm CryoSat-2 DEM, Helm et al., 2014 and Slater
CryoSat-2 DEM, Slater et al., 2018), one DEM derived from
stereophotogrammetry using GeoEye-1 and WorldView-1/2/3 imageries
(hereafter called the Reference Elevation Model of Antarctica, REMA, DEM,
Howat et al., 2019), and one DEM derived from Interferometric Synthetic
Aperture Radar (InSAR) using TerraSAR-X and TanDEM-X data (hereafter called
the TanDEM-X PolarDEM, Wessel et al., 2021).
All these DEMs provide reasonable elevation estimates for Antarctica;
however, some flaws still cannot be totally avoided. The coverage of ICESat
is limited in ice sheet margins due to its coarse across-track resolution,
hence for ICESat DEM most of the elevations in ice sheet margins were
interpolated based on the neighbouring data. Although the ICESat/ERS-1 DEM
improves the data coverage by combining the measurements from ICESat and
ERS-1 elevations, this DEM aggregates observations spanning several
years due to the different time spans (1994–1995 for ERS-1 and 2003–2008 for
ICESat) of these two satellite altimeter datasets. This issue also exists
with the REMA DEM and TanDEM-X PolarDEM, where multiyear satellite imageries
were used. Different from the abovementioned DEMs, the Slater CryoSat-2 DEM
was derived based on a model fitting method by using 7-year CryoSat-2
data (from July 2010 to July 2016). This method can quantify the measured
elevation fluctuations due to interannual variations, and can provide a DEM
for each month during the time span of applied data; however, although the
radar penetration depth of the CryoSat-2 Ku-band into snowpack can be
corrected either empirically or theoretically using a waveform fitting
approach (Davis, 1996, 1997), the spatial and temporal variations of
radar penetration depth are still difficult to account. As multitemporal
and large-scale satellite radar altimeter data are usually used, the
accuracy of estimated elevations is reduced. A similar problem also exists
with the Helm CryoSat-2 DEM and TanDEM-X PolarDEM (the penetration depth of
the X-band into snow may be several metres, Fischer et al., 2020; Dehecq et
al., 2016).
The new generation satellite laser altimeter Ice, Cloud, And Land Elevation
Satellite-2 (ICESat-2) of the National Aeronautics and Space Administration
(NASA), which was launched on 15 September 2018, provides near-global (up to
88∘ S) and dense land ice elevation measurements in an accurate
repeated cycle of 91 d by using a multibeam (6 beams in 3 pairs
that work at 532 nm) laser altimeter (i.e. Advanced Topographic Laser
Altimeter System, ATLAS, Neumann et al., 2019). The narrow footprint
(approximately 17 m with a spatial interval of 0.7 m) and 3 pairs of
beams (2 beams in 1 pair can determine the local slope) enable a
fine-scale measurement of Antarctic surface heights even in steep regions.
Hence, ICESat-2 can be expected to provide a new Antarctic DEM on a fine
scale.
Here, we use a 1-year time series (from November 2018 to November 2019) of
ICESat-2 elevation measurements to generate a new Antarctic DEM that covers
both the ice sheet and ice shelves (hereafter called the ICESat-2 DEM). The
applied data, DEM generation method and quality control criteria are
presented in Sect. 2. Furthermore, we present the map of the ICESat-2 DEM
and construct an accuracy evaluation by comparing it to the elevation
measurements from the NASA Operation IceBridge (OIB) airborne mission and
kinematic GPS and the Russian Global Navigation Satellite System (GLONASS) (GNSS) transects in Sect. 3. The
performances of the ICESat-2 DEM and six currently available Antarctic DEMs
are compared in Sect. 4, Sect. 5 provides the data availability and
Sect. 6 summarizes the conclusions of this study.
Data and methodsICESat-2 data
The ICESat-2 ATL06 land ice elevation product (version 3, Smith et al.,
2019) from November 2018 to November 2019 is used. This product provides
land ice elevation measurements at a spatial resolution of 20 m after
correcting instrument-specific biases (i.e. corrections for transmit-pulse
shape and first-photon bias, Neumann et al., 2019); here, only ATL06 data
with good quality (according to the surface signal confidence metric from
ATL06 data, i.e. those for which atl06_quality_summary equals zero) are used to generate the DEM.
For the data collected over Antarctic ice shelves, corrections for ocean
tide and inverse barometer effects are also applied (Egbert et al., 1994;
Egbert and Erofeeva, 2002; Padman et al., 2002). Elevation measurements from
all six beams are used to produce the densest surface height coverage.
Although the signal energies of strong and weak beams are different, all six
beams provide centimetre-scale elevation measurements, and the biases of two
beams in one pair are less than 2 cm for flat regions (Brunt et al., 2019)
and 5 cm for steep surfaces (Shen et al., 2021b). Thus, the effect of
elevations estimated from weak beams is negligible.
NASA OIB airborne data and kinematic GNSS data
Elevation measurements from the OIB airborne mission in Antarctica are used
here to evaluate the accuracy of the ICESat-2 DEM on a continental scale,
including in the stable ice sheet interior and active marginal ice shelves.
Surface heights from OIB airborne missions are measured by the Airborne
Topographic Mapper (ATM), a conically scanning laser altimeter (at 532 nm)
with a swath width of 140 m and footprint size of 1–3 m. The elevation
measurement accuracy of the ATM is approximately 10 cm or better (Kurtz et al.,
2013). Here, the IceBridge ATM L2 Icessn elevation, slope and roughness
(V002) product (Studinger, 2014) is used, and a data filter (Young et
al., 2008; Kwok et al., 2012; Studinger, 2014) is applied to remove
abnormal values due to geolocation errors or cloud cover. To reduce the
effect of interannual changes of surface elevations on DEM evaluation, the
time difference between applied OIB airborne data and ICESat-2 DEM should be
less than 1 year. Thus, OIB airborne data in October and November 2018 and
October and November 2019 in Antarctica (Fig. 1) are chosen to evaluate the
accuracy of the ICESat-2 DEM. In order to provide a comprehensive and more
robust evaluation of the ICESat-2 DEM, OIB data in areas of low elevation
change (i.e. ice sheet interior) from 2009 to 2017 are also used
additionally (Fig. 1). The CryoSay-2 low rate mode (LRM) mask in Antarctica
(which was designed for flat ice sheet interior measurements) is used to
extract the regions of low elevation change. The CryoSat Geographical Mode Mask
(V4.0, updated in 19–26 August 2019) at
https://earth.esa.int/eogateway/news/cryosat-geographical-mode-mask-4-0-released (last access: 27 June 2022)
is used. The averaged elevation change rate in the OIB data locations used
is -0.0074 ± 0.0821 m yr-1 from 2003 to 2019, according to
elevation change rate estimates from Smith et al. (2020). Hence, we assume
that in these areas the effect of the elevation change on the DEM evaluation
can be ignored. Besides, common OIB data in these areas from 2009 to 2019
(Fig. 1) are used to provide a robust and reasonable comparison between
ICESat-2 DEM and previously published DEMs (see Sect. 2.3).
In addition, elevation records from kinematic GNSS observations
(Schröder et al., 2017) in areas of low elevation change are also used
for an additional DEM elevation comparison (Fig. 1). These GNSS profiles
were measured in the region from the Vostok Station (106.8∘ E,
78.5∘ S) to the East Antarctic coast from 2001 to 2015 and an
averaged offset of 4.9 cm was found compared to OIB airborne data in
November 2013. A detailed introduction to the data collection, data
processing method and accuracy evaluation can be found in Schröder et
al. (2017).
Although OIB and GNSS data in the low elevation change areas and OIB data
with a small time difference (< 1 year) compared to ICESat-2 DEM
are used for DEM evaluation, the effect of the time difference between the DEM
and evaluation data still needs to be considered. Here, we adjust the
changes of ICESat-2 DEM elevation values which occur during the time
difference between these two sets of data, the trend values are derived from Smith
et al. (2020) and we assume the constant elevation change rates, the
corresponding adjustments are calculated and applied for the DEM values in
the locations of OIB and GNSS measurements before comparisons.
Maps of the OIB airborne data in October and November 2018 and October and November 2019 (red), from 2009 to 2019 in ice sheet
interior (blue). Map of the GNSS transects from 2001 to 2015 in Antarctica
(yellow). The dashed lines show the boundary of the region where we assume there is low elevation change, which is the mode mask boundary of CryoSat-2 LRM
data in Antarctica.
Detailed introduction to six previously published Antarctic DEMs,
including the source data, time span of the source data, spatial
posting or resolution.
DEMSource dataTime span of applied source dataSpatial posting or resolutionICESat DEMICESatFebruary 2003–June 2005500 mICESat/ERS-1 DEMICESat, ERS-11994–1995, 2003–20081 kmSlater CryoSat-2 DEMCryoSat-2July 2010–July 20161 kmHelm CryoSat-2 DEMCryoSat-2A full 369 d-long cycle starting January 20121 kmREMA DEMGeoEye-1, WorldView-1/2/32009–2017, with most collected in 2015 and 2016Variable resolutions, 2 and 8 mTanDEM-X PolarDEMTerraSAR-X, TanDEM-XApril–November 2013,April–October 2014, mid-2014, July 2016–September 201790 mPreviously published Antarctic DEMs
The six previously published Antarctic DEM products are compared to the ICESat-2
DEM, i.e. ICESat DEM (DiMarzio et al., 2007), ICESat/ERS-1 DEM (Bamber et
al., 2009), Helm CryoSat-2 DEM (Helm et al., 2014), Slater CryoSat-2 DEM
(Slater et al., 2018), REMA DEM (Howat et al., 2019) and TanDEM-X PolarDEM
(Wessel et al., 2021), as shown in Sect. 4. Detailed information
concerning these DEMs is provided in Table 1, and all DEMs have been
referenced to the WGS84 ellipsoid.
ICESat-2 DEM generation methodSurface elevation and uncertainty estimation
To separate the various contributions (i.e. local surface terrain and
elevation change), following Slater et al. (2018) a model fitting method is
applied here. The elevation is estimated using a quadratic function based on
the local surface terrain and a time term (Eq. 1). This function is fitted
in each grid (at resolutions of 500 m and 1 km, see following
subsection) by using an iterative least-squares fit to all the included
elevation measurements. By considering the surface elevation fluctuations
and sub-annual changes, this method tends to obtain more accurate elevation
estimates (Flament and Rémy, 2012; McMillan et al., 2014).
E(x,y,t)=E‾+a0x+a1y+a2x2+a3y2+a4xy+a5(t-tMay 2019),
where E is the surface elevation derived from ICESat-2 measurement points,
x and y are the local surface terrain respectively (i.e. the geographical locations), t is the time term, and
E‾ is the DEM value in May 2019.
This method suits the ICESat-2 orbit cycle, which samples dense ground tracks
comparing to previous satellite altimeters, more measurement points are
included in the grid cell and the estimated elevations are more robust. It
is possible for a quadratic form to model the topography at the resolutions
of 500 m and 1 km and smaller elevation residuals can be found than using a
simple linear fit (Flament and Remy, 2012). In addition, the model fitting
method can provide the estimation of elevation change rate (a5), and the
estimate agrees well with accurate elevation change estimations from the
crossover points method (Moholdt et al., 2010), which provides an addition
reference for the research of ice dynamics and mass balance.
To reduce the effect of any poor fit the quality control criteria listed in
Table 2 are performed, which includes the number of ICESat-2 measurement
points used, the time span of the data used, the root-mean-square deviation
(RMSD) of the residuals of fitted elevations, the elevation rate of change
and the uncertainty. These criteria are constructed for all grid cells, and
thus, there are some elevation gaps in the initial DEM. The remaining gaps
are filled by using ordinary kriging interpolation (semi-variogram model:
spherical, nugget: 0, sill: 1 652 285.953, radius: 10 km), which is widely
used for generating previous DEMs (Helm et al., 2014; Slater et al., 2018).
During the interpolation process, a search radius of 10 km is applied to
obtain neighbouring measurement points. Similar estimation models have also
been used in previous studies (Moholdt et al., 2010; Flament and Remy, 2012;
McMillan et al., 2014; Konrad et al., 2017; Slater et al., 2018), and the
evaluation in Sect. 3.2 also demonstrates its validity.
Quality control criteria applied to remove the unrealistic
elevations due to the poor fitting performances in each grid cell.
ParametersRulesNumber of ICESat-2 measurement points≤10Time span≤2 monthsRMSD of the residuals of fitted elevations≥10 mElevation change rate≥10 m yr-1Uncertainty of elevation change rate≥10 m yr-1
The performance of this surface fit method is also affected by the spatial
distribution and number of ICESat-2 measurement points. After quality
control, 4.69 × 109 ICESat-2 measurement points from November
2018 to November 2019 that cover all of Antarctica are used. An adequate
number of ICESat-2 measurement points in one grid cell is required to
generate valid elevation estimates. Figure 2 shows the distribution of the
numbers of ICESat-2 measurement points used in individual grid cells (at the
resolution of 500 m), which indicates a latitude-dependent pattern. Each
grid cell contains approximately 118 ICESat-2 measurement points on average.
In the ice sheet interior, the large coverage of ICESat-2 measurement points
provides a complete surface height observation. In the low latitude region,
the numbers of ICESat-2 measurement points are relatively small, the
proportion of observed grid cells is reduced, and the representativeness is
also reduced. Additionally, the performance of the surface fit method also
depends on the time span of the input data, i.e. it should be
noted that whether 1 year of ICESat-2 data can be used to obtain a
satisfied fitting performance. Here we find that the elevation change rate
map based on 1-year ICESat-2 data (i.e. a5 in Eq. 1) has a similar
pattern to that from Smith et al. (2020), which estimated the
elevation change rate from 2003 to 2019 based on ICESat and ICESat-2 data,
indicating that 1 year of data can also provide the reasonable elevation
change rates and thus the surface fit method used here is reliable.
Map of the numbers of valid ICESat-2 measurement points
in each 500 m grid cell. The numbers of ICESat-2 measurement points in 1 km
grid cells are resampled to the resolution of 500 m.
Uncertainties in DEM are calculated based on the approach described in Helm et al. (2014). The OIB elevation data are used as the reference and the elevation
differences due to the time difference between OIB data and DEM are
corrected based on the elevation change rates from Smith et al. (2020). The
DEM uncertainty is then calculated from surface slope, roughness, number of
data points used(N) and the elevation standard deviation (SD). Due to the
difference in methods we calculate the DEM uncertainty for observed and
interpolated grid cells. The surface slope and roughness are
directly derived from the ICESat-2 DEM, the slope in one grid cell is
derived as the maximum rate of change in elevation from that cell to its
eight neighbours, the roughness is derived from the elevation difference
between DEM and the smoothed DEM (by applying a 3 × 3 median filter). For
observed grid cells, N is the number of data points in each grid cell
used for elevation estimation; for interpolated grid cells, N is derived by
counting all data points within the search radius of 10 km, which is the
radius used for elevation interpolation and SD is the standard deviation of
elevations of these data points. The differences between DEM and OIB
elevations are calculated and firstly binned with reference to surface slope. The slope
is divided into 200 bins with an interval of 0.01∘ (from 0 to
2∘), the median and standard deviation are calculated for each
bin. This processing method is also applied for the other 3 parameters, an
interval of 0.05 m for surface roughness, 250/500 (observed/interpolated
grid cells) for N and 0.25 m for SD. For each distribution a 2-order polynomial
is fitted by using the standard deviations of the elevation
differences for each bin. The corresponding coefficients are listed in Table 3. This kind of polynomial order ensures a good and robust fitting
performance, including for the small elevation differences in flat regions.
Finally, the DEM uncertainty is calculated as follows:
2u=∑i=14wiui3wi=1si∑i=141si4si=σi∑i=14σi5ui=bi1x2+bi2x+bi3,
where u is the DEM uncertainty, wi is the weighting factor, ui is
the uncertainty for each uncertainty source, si is the scaling factor, σ is the standard deviation of the difference between the data and the
polynomial fit and bi0–3 are the coefficients for each polynomial fit
(as listed in Table 3). When deriving the ICESat-2 DEM uncertainty
estimation, the uncertainty from ICESat-2 measurements is not considered
because the effect of ICESat-2 measurement bias is limited (< 5 cm,
Brunt et al., 2019; < 14 cm, Shen et al., 2021b).
Fitting coefficients and weights used for the DEM
uncertainty estimation.
Maps of the observed grid cells of DEMs at the spatial
resolution of 250 m (a), 500 m (b) and 1 km (c). The observed grid cells are
coloured in blue and the overall coverage of each DEM in Antarctica is also
included.
(a) Spatial coverage of observed grid cells in the five
latitude ranges when three spatial resolutions, i.e. 250 m (black), 500 m (blue) and 1 km (red), are applied. (b) Map of the elevation difference of
DEMs at the resolutions of 1 km and 500 m. (c) Histograms of the elevation
difference of DEMs at the resolutions of 1 km and 500 m, the average and
standard deviation values are also included.
(a) A new DEM of Antarctica at a posting of 500 m derived
from ICESat-2, which covers both the ice sheet and ice shelves with the
southern limit of 88∘ S. (b) Map of the ICESat-2 DEM elevation
uncertainty.
(a) Map of the surface slope of Antarctica derived from the
ICESat-2 DEM. (b) Map of the ICESat-2 DEM surface slope uncertainty. The
uncertainty is estimated based on the propagation of elevation uncertainty.
Choice of DEM resolution
The selection criterion for DEM resolution is to present the detailed pattern
of elevations and ensure enough spatial coverage of observed elevations (a
smaller resolution tends to cause more observed elevation gaps). Although a
much finer scale (e.g. 250 m) can reveal a more detailed elevation pattern,
this contributes to more gaps among observed elevations. The overall spatial
coverage of observed elevations when applying 250 m, 500 m and 1 km
resolutions (which are usually applied in the Antarctic DEM) are 26 %,
46 % and 72 %, respectively. High latitude areas always have higher
observed elevation coverages; in lower latitudes there are still some 250 m grid cells with estimated elevations (Fig. 3); however, 250 m DEM only has
26 % coverage. The detailed variations in the spatial coverages of
observed grid cells at different latitudes at variable spatial resolutions
(250 m, 500 m and 1 km, which are usually applied in the Antarctic DEM) are
shown in Fig. 4a. A reliable grid size is 500 m, which provides denser spatial
coverage of the observed elevations but a single resolution cannot obtain
ideal spatial coverage, especially in low latitude areas. To increase the
coverages of observed elevations as much as possible, referring to Slater et
al. (2018), two spatial resolutions are used to estimate the surface
elevations from ICESat-2, i.e. elevations are estimated at resolutions
of 500 m and 1 km. The observation gaps in the 500 m DEM are filled by the
resampled 1 km DEMs (resampled to the 500 m DEM). The addition of DEMs at 1
km greatly increases the observation coverage, the overall spatial coverage
is approximately 74 %, and the remaining gaps are filled using ordinary
kriging interpolation. Although two resolutions are applied, 1 km and
interpolated elevations are both resampled to the posting of 500 m to
provide a consistent DEM dataset; hence, the final ICESat-2 DEM is posted at
a resolution of 500 m.
The application of two resolutions may include additional effects, i.e.
different grid cell resolutions tend to present different elevation
estimates. Here, we compare the elevation difference at the overlapped areas
in Antarctica at different spatial resolutions (Fig. 4b). The elevation
values become lower when a larger spatial resolution is applied, which acts
as a running mean. Although applying different spatial resolutions affects
the elevation values, an averaged elevation difference of 0.04 ± 2.93 m can be found (Fig. 4c), which is relatively small compared to the estimated
elevations. In addition, this method can increase the coverage of observed
elevations, and observed elevations tend to be more reliable than
interpolated elevations (as shown in Sect. 3.2).
Finally, in order to remove additional elevation outliers in the generated
DEM, a 3 standard deviation filter (3 × 3) is firstly applied. Visual
inspection indicates that only a small number of anomalous elevations remain
and these are further removed by using a 3 × 3 median filter. These quality
assurance filters ensure the elevation pattern of the final DEM is smoothed
and reasonable.
DEM evaluation method
The ICESat-2 DEM and previously published DEMs are resampled to the OIB and GNSS
data locations and the differences for evaluation are calculated to reduce the
effect of resolution differences between various DEMs. Four indexes are used
to evaluate the DEM performance, including median deviation (MeD), median
absolute deviation (MeAD), standard deviation (SD) and RMSD. The
corresponding calculation equations are listed as follows:
6MeD=median(δi=1,2…,n)7MeAD=median(δi=1,2…,n)8SD=∑i=1n(δi-MD)2n-19RMSD=∑i=1nδi2n-1,
where δi is the bias of ICESat-2 DEM and OIB or GNSS elevation, MD
is the mean deviation and n is the number of the matched grid cells.
ResultsGeneral attributes of ICESat-2 DEM
The effective time stamp of the ICESat-2 DEM is May 2019, which is halfway
between November 2018 and November 2019. The ICESat-2 DEM provides a
complete surface elevation reference for Antarctica, which illustrates
higher elevations in the ice sheet interior and lower values in marginal ice
shelves (Fig. 5). Negative elevations can be found in the ice shelves,
especially in the Ross Ice Shelf. The local slope shows a pattern similar to
the DEM, and undulated slopes are found in areas with rugged terrain, such
as the Antarctic Peninsula and Transantarctic Mountains (Fig. 6). Both
elevation and slope uncertainties show topography-dependent patterns, and
larger values tend to be found at rugged areas, which may be related to the
local surface conditions (i.e. slope and roughness). Larger elevation
uncertainties can be found for interpolated grid cells than for observed ones, which is due to the difference in methods when deriving the surface elevations.
According to the shaded relief map of Antarctica derived from the ICESat-2
DEM (Fig. 7), obvious topographical patterns and flat terrain can be found
in the mountain environments and ice sheet interior, respectively. On the
Antarctic Peninsula, the ice shelf limit is visually identified from the
shaded relief map (Fig. 7b). Other large-scale terrain features, e.g.
subglacial lakes and floating ice shelves, can also be visually detected
(Fig. 7c and d).
(a) Shaded relief map of Antarctica derived from the ICESat-2 DEM.
The detailed maps of the Larsen C ice shelf, Lake Vostok and Amery ice shelf
are shown in (b), (c) and (d), respectively, and their locations are also
shown in (a) by red rectangular boxes.
Two spatial resolutions are used in the ICESat-2 DEM, and the distributions
of three kinds of grid cells (interpolated and observed at resolutions of 500 m and 1 km) show obvious latitude-dependent patterns. Regardless of
whether at the basin scale or regional scale, more elevations at higher
resolutions tend to be located in high-altitude areas, while elevations at
lower or interpolated resolutions are mostly located in low-altitude regions
(Fig. 8).
(a) Coverage of observed grid cells at 500 m and 1 km
and interpolated grid cells in 27 drainage basins of ice sheets (Zwally et
al., 2012) and ice shelves. The boundaries and basin index (ID) of 27 ice
sheet drainage basins (numbers 1–27) and ice shelves (number 28) are
shown in (b). The coverages of observed (at two spatial resolutions) and
interpolated grid cells in the Antarctic Peninsula, West Antarctica, East
Antarctica and ice shelves are also shown in (b). Map of the selected
grid cell resolution for deriving the ICESat-2 DEM in all grid cells at a
spatial resolution of 500 m (c). Elevation values derived from 1 km and
interpolation (i.e. 1 km) are resampled to a resolution of 500 m.
Comparisons between the ICESat-2 DEM and OIB airborne elevation
measurements (including data in areas of low elevation change from 2009 to
2017 and data in the Antarctica from 2018 to 2019) in observed and
interpolated areas for individual regions (i.e. the ice sheet and ice
shelves). MeD: median deviation, MeAD: median absolute deviation, SD:
standard deviation, RMSD: root-mean-square deviation.
RegionMeDMeADSDRMSDNumber of used(m)(m)(m)(m)OIB measurement pointsObservedIce sheet-0.171.219.259.263 589 087Ice shelves0.592.5314.0714.09191 754Total-0.151.269.569.573 780 841InterpolatedIce sheet-0.522.6313.3013.361 237 416Ice shelves0.443.0015.1615.21185 613Total-0.412.6713.5813.621 423 029OverallIce sheet-0.221.4710.4410.474 826 503Ice shelves0.532.7514.6214.65377 367Total-0.191.5410.8110.835 203 870Evaluation of ICESat-2 DEM by comparing to OIB airborne data
In total, approximately 5.2 × 106 OIB measurement points
that cover both the steep and flat regions (Fig. 1) are chosen to evaluate
the ICESat-2 DEM. Generally, a MeAD of 1.54 m and an RMSD of 10.83 m are
found for ICESat-2 DEM compared to OIB surface heights (Table 4). Ice sheet
elevations are more accurate than those estimated for ice shelves, which may be due to a higher percentage of high-slope areas in ice shelves observed by
OIB data than in ice sheets.
We also evaluate the elevation performance for observed and interpolated
grid cells (Table 4). Generally, the bias of observed elevations is smaller
than that of interpolated elevations in both ice sheets and ice shelves,
which indicates that the observed elevations tend to be more accurate than
those estimated from interpolation. Larger biases will be included in the
ICESat-2 DEM if the coverage of interpolated elevations is high, hence the
elevation gaps in the 500 m DEM are firstly filled by the resampled 1 km DEM
to reduce the coverage of interpolated elevations. The accuracy of the
ICESat-2 DEM has an obvious relationship with local terrain conditions, and
the bias increases when the slope or roughness becomes larger, which is visible
for three surface types (Table 5) and different surface slope conditions
(Fig. 9). The bias in rocks is obviously larger than those for snow or firn and
blue ice areas (BIAs), which is mainly due to the local terrain condition,
as they are mostly located in the Transantarctic Mountains and the Antarctic
Peninsula, while snow, firn and BIAs tend to have flat surface terrain;
hence, they have a smaller bias. While in the low-slope regions, the
ICESat-2 DEM shows good agreement with both the OIB and GNSS data, in the
large-slope areas, larger biases occur (Fig. 9).
Comparison between the ICESat-2 DEM and OIB airborne elevation
measurements (including data in areas of low elevation change from 2009 to
2017 and data in the Antarctica from 2018 to 2019) with respect to three
surface types, i.e. snow or firn, blue ice areas (BIAs) and rocks. The surface
type data are obtained from Hui et al. (2017).
MeDMeADSDRMSDNumber of used(m)(m)(m)(m)OIB measurement pointsSnow or firn-0.181.479.609.625 046 581BIA-1.718.8224.5524.65151 111Rock-0.8524.0375.1178.896178Total-0.191.5410.8110.835 203 870
Scatter plots of ICESat-2 DEM elevation and OIB airborne
elevation (a) and GNSS elevation (b). The surface slopes are distinguished in different colours, as shown in the figure legend.
Although OIB airborne data provide an independent evaluation of the
generated DEM, they still cannot present a comprehensive comparison. Most of
the OIB airborne data were obtained in ice sheet margins or mountain
environments, with high slopes and low elevations. Approximately 78 % of
the OIB elevations used were less than 1500 m, and 76 % of the observed surface
slopes from the OIB mission were less than 1∘, while the
corresponding percentages from the ICESat-2 DEM are 36 % and 91 %,
respectively. The OIB airborne data applied cannot completely represent the
slope and elevation distributions of the Antarctic DEM; hence, the real accuracy
of the ICESat-2 DEM is biased and may be higher.
In order to evaluate the DEM performance in more detail, the elevations
along two OIB tracks in the flat ice sheet interior and rough ice sheet margins
are shown in Fig. 10. In the ice sheet interior where surface slopes are
small (Fig. 10a), elevation differences of approximately 5 m can be found
(the median elevation difference for ICESat-2 DEM is -0.13 ± 0.19 m).
The elevation differences are further reduced when the surface slopes become
smaller. While at the Pine Island Glacier where surface slopes are large
(Fig. 10b), elevation differences of approximately 20 m can be found in the
undulating terrains (the median elevation difference is -0.01 ± 4.58 m). Overall, ICESat-2 DEM has better performances in the flat regions than
steep areas. Regions of low surface slope represent the majority of
Antarctic ice sheet, hence most elevations from ICESat-2 DEM have smaller
elevation biases.
Differences between the ICESat-2 DEM and OIB elevations
along two OIB flight paths in (a) ice sheet interior and (b) Pine Island
Glacier. ICESat-2 DEM elevations are in red, OIB elevations are in black,
and the elevation differences between ICESat-2 DEM and OIB elevations are in
blue. Locations of the two OIB flight paths are shown in red in the inserted
figures of Antarctica.
Elevation differences between the ICESat-2 DEM and six
previously published DEMs, i.e. ICESat DEM (a), ICESat/ERS-1 DEM (b), Helm
CryoSat-2 DEM (c), Slater CryoSat-2 DEM (d), REMA DEM (e) and TanDEM
PolarDEM (f).
Additionally, by comparing to the OIB or GNSS elevation data (see Sect. 4), we can estimate the actual ICESat-2 DEM uncertainty as the SD of the
differences to OIB or GNSS elevation data. In the estimated uncertainty map
(Fig. 5b), a median value of 5.84 ± 5.29 m can be found. The SD of
differences to OIB data shows a value of
10.44 m (Table 4, including plenty of measurements in ice sheet margins),
while in the ice sheet interior a value of 3.26 m is found (Table 6).
Considering the data coverage and surface slope difference, the estimated
uncertainty values can represent the SDs from what is given as OIB, which
means that the provided uncertainty estimates are reliable. A small SD value
of 1.59 m can be found when comparing to the GNSS data (Table 7), which were
obtained in the regions of low slope and may be due to the resolution and
measurement accuracy differences between airborne and GNSS data; hence the
ICESat-2 DEM uncertainty map may be slightly overestimated and can be
assumed as the upper limit.
Comparisons between the ICESat-2 DEM, ICESat DEM, ICESat/ERS-1 DEM,
Helm CryoSat-2 DEM, Slater CryoSat-2 DEM, REMA DEM, TanDEM PolarDEM and OIB
airborne elevation measurements in areas of low elevation change from 2009
to 2019.
Median differences between seven DEMs and OIB airborne elevation
measurements in areas of low elevation change from 2009 to 2019 with respect
to surface slope (a) and roughness (b). The upper and lower lines in each box
indicate the 25th and 75th percentiles, the whiskers indicate the 5th and
95th percentiles, and the central horizontal line indicates the median
difference.
Comparisons with previously published Antarctic DEMs
When compared to the altimeter-derived DEMs, the elevation difference rises
when the surface slope becomes larger, especially in mountainous
environments (e.g. Transantarctic Mountains and Antarctic Peninsula, Fig. 11). This may be due to their differences in spatial resolution and
measurement accuracy; this effect is considerably reduced when the local
terrain is flatter (e.g. ice sheet interior). Compared to the REMA DEM and
TanDEM PolarDEM, smaller elevation differences can be found in both the flat
ice sheet interior and steep mountains and marginal ice sheets. REMA DEM and
TanDEM PolarDEM have much smaller spatial resolutions and better
performances and similar elevations indicate the reliability of ICESat-2 DEM in
mountain environments. In particular, the ICESat-2 DEM shows a generally
higher surface height than the TanDEM PolarDEM, which is assumed to be
caused by the penetration depth of the X-band (TerraSAR-X and TanDEM-X) into
snowpack (Dehecq et al., 2016; Fischer et al., 2020). Here it should be
noted that the time differences between these DEMs can still cause the
uncertainties in the DEM comparison.
To indicate a general comparison between the ICESat-2 DEM and other DEMs,
OIB airborne data in areas of low elevation change from 2009 to 2019 are
used to evaluate individual DEMs and the same evaluation method applied for the
ICESat-2 DEM is used (as described in Sect. 2.2). The evaluation result
shows that the ICESat-2 DEM has a reliable performance and is comparable to
other DEMs derived from altimetry, stereo-photogrammetry and interferometry
(Table 6).
The median differences in surface slope and roughness for these seven DEMs
illustrate that all the elevation biases become more uncertain with
increasing slope and roughness (Fig. 12). The REMA DEM always has more
stable performances than the ICESat-2 DEM, as stereo-photogrammetry can
generate more consistent elevation estimations at the regional scale than
altimetry. A similar situation occurs for the TanDEM PolarDEM when slopes are > 1.5∘. Nevertheless, the ICESat-2 DEM is comparable
to both the REMA DEM and TanDEM PolarDEM when slopes are less than
1∘, which occupies 91 % of Antarctica north of 88∘ S.
Here, kinematic GNSS data from 2001 to 2015 in the ice sheet interior are used
to construct an additional DEM comparison. The comparison method and
adjustment for time difference are the same as for OIB data. As surfaces in
the interior of East Antarctica are flat, better performances for all DEM
except TanDEM PolarDEM are found than those based on OIB airborne data.
Similarly, ICESat-2 DEM has a reliable performance and is comparable to
other DEMs (Table 7). Additionally, the accuracy of the ICESat-2 DEM may be
related to the surface slope (Fig. 9b); however, as the terrain conditions
of GNSS measurement points are relatively flat, this relationship is not
obvious. It should be noted that the spatiotemporal coverages of the OIB
and GNSS data used are limited here and they cannot provide an unbiased
evaluation for ICESat-2 DEM and other DEMs. Hence the comparisons above only
give a general reference for the performances and cannot be used as the
quantitative accuracy evaluation.
Comparisons between the ICESat-2 DEM, ICESat DEM, ICESat/ERS-1 DEM,
Helm CryoSat-2 DEM, Slater CryoSat-2 DEM, REMA DEM, TanDEM PolarDEM and GNSS
elevation data in areas of low elevation change from 2001 to 2015.
Compared to other DEMs, the elevation change rate can be obtained when deriving
the ICESat-2 DEM, which provides an additional reference for ice topography
and mass balance estimation. Additionally, in previous studies several years
of altimeter data are needed to derive the DEM in Antarctica. Due to the
high-density measurements of ICESat-2, 13 months of ICESat-2 data can be
used to generate a DEM for Antarctica and the performance is comparable to
other DEMs, indicating that the ICESat-2 DEM can be updated annually. This
study demonstrates the feasibility and reliability of using 1-year
ICESat-2 data to derive the Antarctic DEM and provides a reference for the
processing scheme of DEM (e.g. in higher resolution, regularly updated)
based on ICESat-2 in future.
Data availability
The generated ICESat-2 DEM (including the map of uncertainty) can be
downloaded from the National Tibetan Plateau Data Center, Institute of Tibetan
Plateau Research, Chinese Academy of Sciences at
https://data.tpdc.ac.cn/en/disallow/9427069c-117e-4ff8-96e0-4b18eb7782cb/ (last access: 27 June 2022)
(Shen et al., 2021a, 10.11888/Geogra.tpdc.271448).
Conclusions
A new DEM for Antarctica with a posting of 500 m is presented based on the
surface height measurements from ICESat-2 by using a model fitting method.
This DEM has an elevation measurement that accounts for 74 % of
Antarctica, and the remaining 26 % is estimated based on the ordinary
kriging method. The accuracy of the ICESat-2 DEM is evaluated by comparing
it to the independent airborne data from the OIB mission. Overall, the
ICESat-2 DEM shows a median bias of -0.19 m and an RMSD of 10.83 m, and
these accuracies are compromises for DEM values from surface fits and
interpolation. A median bias of -0.15 m and an RMSD of 9.57 m are found for
areas where elevations are derived from ICESat-2 measurements, and they
increase to -0.41 and 13.62 m, respectively, for interpolated elevations. The accuracy
decreases when the surface slope or roughness increases; thus, larger biases
occur for steep rocks, and flat snow or firn and blue ice areas have smaller
elevation differences.
Compared to DEMs derived from satellite altimeters (i.e. the ICESat DEM,
ICESat/ERS-1 DEM, Helm CryoSat-2 DEM, and Slater CryoSat-2 DEM), larger
differences are found in regions with high slopes, which is due to their
resolution difference, while smaller elevation differences compared to the
REMA DEM and TanDEM PolarDEM support the reliability of the ICESat-2 DEM.
Based on the OIB airborne data and kinematic GNSS transects, the ICESat-2
DEM shows reliable performance and is comparable to other DEMs, which
further demonstrates the reliability of the ICESat-2 DEM.
Here 13 months of ICESat-2 data are used to generate the Antarctic DEM
and the evaluation results show that the corresponding DEM is reasonable and
valid. This means that the ICESat-2 DEM can be provided in a sustainable
way, i.e. this DEM can be updated annually and thus accumulated on an
annual basis. Additionally, reasonable elevation change rates can also be
obtained when deriving the DEM. The combination of the derived DEMs and
elevation change rates can be further used for the references of fieldwork
planning, ice motion tracking, numerical modelling of ice sheets and the mass
balance estimation. More importantly, these data can be provided on an annual
basis, which has a large application potential for Antarctic research
especially under climate warming.
Author contributions
XS and YF developed the related algorithm, generated and
evaluated the ICESat-2 DEM; LD constructed the comparison to
previously published DEM products; CQK supervised this work.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank all the data providers for their
data. We thank the editors and reviewers for their constructive comments
which helped to improve this manuscript.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 41976212, 41830105) and the Natural Science Foundation of Jiangsu Province (grant no. BK20210193).
Review statement
This paper was edited by Baptiste Vandecrux and reviewed by Veit Helm and two anonymous referees.
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