TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-11-1041-2017A revised calibration of the interferometric mode of the CryoSat-2 radar
altimeter improves ice height and height change measurements in western
GreenlandGrayLaurencelaurence.gray@sympatico.caBurgessDavidCoplandLukeDunseThorbenhttps://orcid.org/0000-0002-4362-4265LangleyKirstyMoholdtGeirDepartment of Geography, Environment and Geomatics, University of
Ottawa, Ottawa, ON K1N 6N5, CanadaGeological Survey of Canada, Natural Resources Canada, Ottawa, ON K1A 0E8, CanadaDepartment of Geosciences, University of Oslo, 0316 Oslo, NorwayAsiaq, Greenland Survey, 3900 Nuuk, GreenlandNorwegian Polar Institute, 9296 Tromso, NorwayLaurence Gray (laurence.gray@sympatico.ca)4May20171131041105830November201621December20167March20171April2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/11/1041/2017/tc-11-1041-2017.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/11/1041/2017/tc-11-1041-2017.pdf
We compare geocoded heights derived from the interferometric mode (SARIn) of CryoSat to surface heights from
calibration–validation sites on Devon Ice Cap and western Greenland.
Comparisons are included for both the heights derived from the first return
(the “point-of-closest-approach” or POCA) and heights derived from delayed
waveform returns (“swath” processing). While swath-processed heights are
normally less precise than edited POCA heights, e.g. standard deviations of
∼ 3 and ∼ 1.5 m respectively for the western Greenland site, the
increased coverage possible with swath data complements the POCA data and
provides useful information for both system calibration and improving digital
elevation models (DEMs). We show that the pre-launch interferometric baseline
coupled with an additional roll correction
(∼ 0.0075∘± 0.0025∘), or equivalent phase
correction (∼ 0.0435 ± 0.0145 radians), provides an improved
calibration of the interferometric SARIn mode.
We extend the potential use of SARIn data by showing the influence of
surface conditions, especially melt, on the return waveforms and that it is
possible to detect and measure the height of summer supraglacial lakes in
western Greenland. A supraglacial lake can provide a strong radar target in the
waveform, stronger than the initial POCA return, if viewed at near-normal
incidence. This provides an ideal situation for swath processing and we
demonstrate a height precision of ∼ 0.5 m for two lake sites,
one in the accumulation zone and one in the ablation zone, which were
measured every year from 2010 or 2011 to 2016. Each year the lake in the
ablation zone was viewed in June by ascending passes and then 5.5 days later
by descending passes, which allows an approximate estimate of the filling
rate. The results suggest that CryoSat waveform data and measurements of
supraglacial lake height change could complement the use of optical
satellite imagery and be helpful as proxy indicators for surface melt around
Greenland.
Introduction
Temporal change in ice sheet surface elevation derived from satellite
altimeters has been used in mass balance estimates and the associated
contribution to sea level rise (e.g. Davis and Ferguson, 2004; Rémy and
Parouty, 2009; Shepherd et al., 2012; Hurkmans et al., 2014; Zwally et al.,
2015). Satellite radar altimeters have traditionally operated at Ku band
(∼ 13 GHz) and used parabolic transmit–receive dish antennas
with a diameter of ∼ 1 m so that the main beam illuminates an
area beneath the satellite with a diameter of ∼ 15 km and area
of ∼ 180 km2. With a typical bandwidth of ∼ 300 MHz the range resolution is ∼ 50 cm and, as delay time
increases beyond the point at which the first surface returns are received,
an increasing area contributes to the received signal. These returns are
termed “pulse limited”, with the initial signal originating from the area
within the main beam closest to the satellite, often referred to as the
“point-of-closest-approach” (POCA). With these parameters, the diameter of
the initially sampled POCA area over the ocean is ∼ 1.2–1.5 km, but this is not necessarily the case over glacial ice. The initial area
contributing to the leading edge of the waveform (the delay time variation
in received power) over an ice cap or ice sheet depends on the topography.
All we know is that it must originate from somewhere within the area
illuminated by the main antenna beam and that part of the POCA surface area
must be orthogonal to the incident wave. Considering the large variability
in ice cap topography and surface conditions, it is not unexpected that the
waveforms from glacial ice will vary significantly in shape and power. The
fact that the geographic position of the POCA is, a priori, unknown is one
of the major problems in traditional radar altimetry and methods to get
around this limitation have been studied extensively (Brenner et al., 1983;
Bamber, 1994; Brenner et al., 2007; Hurkmans et al., 2012; Levinsen et al.,
2016).
The European Space Agency (ESA) launched CryoSat as the first in their
Earth Explorer series of satellites, which are designed to explore and
demonstrate new techniques and methods in Earth observation. As such,
CryoSat was designed to include a new mode of operation to address some of
the limitations of traditional radar altimetry when used over sea ice, ice
caps, and ice sheet margins. The new approach uses bursts of pulses in which
the frequency of the pulses within each burst is high enough that coherent
Doppler processing can be used to focus the energy in the along-track
direction and ultimately create a footprint for which the along-track
position is known, but the footprint centre can still be displaced from the
sub-satellite track dependent on the cross-track slope. The along-track
processing approach is referred to as “delay-Doppler” and was pioneered by
Raney (1998). The suggestion that cross-track interferometry could solve the
cross-track footprint position problem in radar altimetry is due to Jensen (1999). For glacial terrain the new SARIn mode of operation provides a
relatively small geocoded footprint which allows, for the first time, a
systematic comparison of satellite radar altimeter elevations with surface
heights from surface and airborne campaigns.
The first CryoSat satellite equipped with the Synthetic Aperture
Interferometric Radar Altimeter (SIRAL) was launched in 2005 but failed to
enter orbit. A replacement satellite was launched in 2010 and, as of March
2017, is still operating satisfactorily, almost 4 years beyond its design
life. CryoSat operates in three modes: a conventional low-resolution mode
(LRM) which is used over oceans and the interior of Antarctica and
Greenland, a synthetic aperture mode (SAR) for use over sea ice, and the
interferometric SARIn mode over all the other glacial ice areas on Earth. A
comprehensive description of CryoSat is given by Wingham et al. (2006). Here
we are concerned primarily with SARIn mode calibration and with
demonstrating some unique capabilities of this new mode of satellite radar
altimetry. These depend primarily on the ability to geocode the position of
the relatively small footprint.
After the initial commissioning phase of the satellite in spring and summer
2010, intermediate and final products were available from ESA. For glacial
ice the ESA level 2 (L2) product contains the position and height of the
geocoded POCA positions. An additional L2i product is available, which
contains the same geocoded height solution as the L2 product as well as
information on the waveform which can be used to help eliminate poor data
and solutions. An intermediate product (L1b) has also been made available
which includes the waveform power, phase, coherence, satellite position and
velocity, etc., and all the corrections and timing information necessary to
calculate the position and height of the POCA footprint. This has been
useful to those users wishing to study processing techniques; for example,
by having access to the intermediate L1b product it has been possible to
demonstrate that the returns which are time delayed beyond the initial POCA
position can be used in areas with suitable cross-track slopes to create
“swath-processed” elevations (Gray et al., 2013). Initially, airborne data
had been used to demonstrate the possibility of swath-mode processing of
delay-Doppler data (Hawley et al., 2009). The L1b products have also been
used in several studies of change in Antarctica and Greenland (e.g. Helm et
al., 2014; Nilsson et al., 2016; Christie et al., 2016; Smith et al., 2017),
smaller Arctic ice caps (Gray et al., 2015; Foresta et al., 2016), and lake
height (Kleinherenbrink et al., 2014). In these studies, the authors claim
improvements in the results over the standard level 2 product due to the
specialized processing.
Three versions of the various CryoSat products have been distributed by ESA
since commissioning; these are the so-called baseline A, B, and C products.
Details of the improvements can be found through the ESA Earth Online website devoted to the CryoSat mission (https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/cryosat).
Here we have used only the latest baseline C products, particularly because
the waveforms in these products span a range window distance of
∼ 240 m, twice the distance available in the baseline B
products. Some comparisons are also made between results derived from the
baseline C L1b files and those provided in the L2 products.
In this study we use CryoSat and surface height data from two well-studied
sites in the Canadian Arctic and Greenland to improve the calibration of the
SARIn mode. Further, we show that the waveforms do change significantly with
surface melt and that it is possible to detect the formation of supraglacial
lakes. By using a modified swath processing scheme, we also show that it is
possible to measure lake height and height change.
Methods
Our processing methods were described in Gray et al. (2013, 2015). The current Matlab processing provides both POCA and swath-mode
results, and here we note any changes since the earlier work. The method to
generate POCA heights is comparable to those described in Helm et al. (2014), Nilsson et al. (2016), and Smith et al. (2017) and were motivated by
similar concerns, particularly the performance of the L2 “retracker”: this
is the algorithm designed to find the position of the POCA return in each
waveform.
The delay-Doppler processing (Raney, 1998) for the SARIn mode of CryoSat is
described in Wingham et al. (2006) and Kleinherenbrink et al. (2014). In
this method 64 pulses are used in each transmitted burst and fast Fourier
transform processing is used to create 64 unfocussed beams so that, with
appropriate superposition of results from a sequence of bursts, multiple
“looks” can be averaged for each ground footprint. In practice there are
less than 64 looks contributing to each waveform in the L1b file, normally
∼ 57. In the along-track direction the footprints are
separated by ∼ 280–300 m and the resolution is
∼ 380 m (Bouzinac, 2012). In the cross-track direction the
footprint size is dictated by the cross-track slopes and by any smoothing of
the waveform in the processing. The position of the POCA footprint derived
from each waveform will be in the plane, including the satellite position,
and the lines defined by the cross-track and nadir directions. The POCA area
will be centred on the closest point in the intersection of this plane with
the terrain surface so that when ascending and descending orbits cross the
two POCA footprints will not be the same when there is a cross-track slope.
Consequently, it is not appropriate to compare results from the interpolated
orbital cross-over point. The L1b files contain two echo-scaling parameters
for each waveform, which allow a calibration of the waveform power to watts.
The logarithmic (dB) values used in the results then represent logarithmic ratios scaled with respect to
1 W.
Selecting the POCA position from the SARIn waveform
If the altimeter response from terrain were “predictable” it would be
beneficial to use the complete waveform in the estimation of the position in
delay time of the surface, and this is the basis of the ESA L2 processing.
However, our experience with the L1b SARIn waveforms over glacial ice shows
that the shape and magnitude of the waveform can vary significantly, even in
one area at one time (see examples in Sect. 4). The average return power
as a function of delay time from the first surface sample will vary with the
illuminated surface area, the reflectivity of the surface, and any near-surface layering on the ice cap. The cross-track slope and fixed sampling in
delay time (3.125 ns) defines the basic cross-track footprint size so that
the waveform shape beyond the POCA depends primarily on the variation in
topography in the cross-track direction. This is essentially independent of
the position of the POCA, resulting in our decision to estimate the POCA position
based on the first significant leading edge in the waveform. Our approach
(Gray et al., 2015) uses the point of inflexion (maximum slope) on the first
significant waveform increase and is similar to that adopted by Nilsson et al. (2016) and Smith et al. (2017). Helm et al. (2014) used a threshold
level of the first significant leading edge for their work in Greenland and
Antarctica, following the work of Davis (1997), who advocated a threshold
retracker to minimize the dependency on varying microwave penetration into,
and backscattering from, various snow–firn–ice layers. The importance of the
cross-track footprint size in dictating the shape of the waveform has been
demonstrated by the success of the straightforward waveform simulation based
primarily on topography shown in Gray et al. (2013).
Although the L1b waveforms already represent averaged values, some
additional smoothing has been done on the complex waveform data. The
low-pass filter uses a 3 dB width of ∼ 4 samples and is
designed to avoid introducing any bias in the waveform phase. Smoothing the
SARIn waveform data is performed only in the range direction with a
relatively small impact on the cross-track footprint size (Gray et al., 2015) and none on the along-track resolution. The resulting reduction in
phase noise improves the POCA footprint geocoding, as the phase provides the
cross-track look angle. It is not appropriate to average any of the L1b
waveform data in the azimuth direction because there can be jumps in the
delay time to the first waveform sample. The processing steps to generate
geocoded heights are described in Gray et al. (2015) using the results of
the calibration described in Sect. 3 below. Solutions are derived for the
phase at the estimated POCA position in the waveform and for this phase are
+2π and -2π. Comparison with the height of the reference digital elevation model (DEM) is
used to select the most likely of the three solutions. Some waveforms are
not used for POCA generation. This can occur for various reasons: the
coherence at the POCA point is less than 0.7, the power for the average of
the first five waveform values is too high (> -150 dB, for baseline
C), the ratio of the maximum waveform power to the average of the first five values is too low (< 6 dB), or there is not a clear leading edge in
the waveform. These criteria are rather arbitrary and may be changed for
different sites, depending on the results. For example, we found that using
a more stringent POCA coherence requirement improved the overall results for
the western Greenland site.
Swath-mode processing
The techniques used to process the returns delayed beyond the POCA position
are essentially as described in Gray et al. (2013). In that work the bias
errors associated with the uncertainty in the baseline roll angle (Galin et
al., 2012) were reduced by comparing the derived east–west slope on the western
flank of Devon Ice Cap with the reference data slope and changing the
baseline roll angle to minimize this error. This step has not been
undertaken here as it presumes a good-quality reference DEM which is not
necessarily available.
Waveform smoothing can lead to a situation in which results may be
oversampled in the cross-track direction. The swath-processed results from
any one waveform will form a straight line in the cross-track direction and
the final samples in cross-track are generated by binning and averaging the
results in segments of the cross-track line. The separation between
ground-range cross-track samples is nominally ∼ 100 m.
Criteria for minimum values of the filtered coherence and returned power are
set and are usually ∼ 0.84 and -150 dB respectively for
baseline C data. The phase unwrapping and ambiguity checking method is
similar to that described by Smith et al. (2017).
The swath processing of the summer CryoSat data for supraglacial lake height
(Sect. 4.2) omitted the cross-track binning stage and produced an
elevation for each sample in the waveform. Only heights derived from
waveform samples with phase values equivalent to small look angles
(< ∼ 0.2∘), high power (> ∼-140 dB), and high coherence (> 0.95) were used.
These minimum values virtually guarantee that there will be a small
contribution from the range ambiguous zone and that phase unwrapping or
ambiguity checking is unnecessary. The resulting geographic positions were
compared to the best-available visible imagery, usually Landsat 8 images,
and north, south, east, and west boundaries around the lake feature were set.
The resulting height estimate was then obtained by averaging all estimates
within the lake boundary.
Measuring the height difference between the reference surface
and CryoSat heights
We used two methods to compare the derived CryoSat heights with the surface
reference data. For Devon Ice Cap the reference data included
intercalibrated snowmobile-based differential GPS transects and airborne
scanning laser altimeter data from both the NASA Airborne Terrain Mapper
(ATM; Krabill et al., 2002; Krabill, 2014) and the TUD ALS (https://earth.esa.int/documents/10174/134665/ESA-CryoVEx-ASIRAS-2014-report) systems.
For the Greenland site, we have relied on the ATM data collected on NASA
IceBridge flights. The first method stepped through all the CryoSat results
and searched for reference heights within 400 m of the centre of the CryoSat
footprint. The height differences between the CryoSat and reference heights
were corrected for the slope between the centres of the two footprints using
interpolation with the reference DEM. If there were many reference values,
as can be the case for the western Greenland site, then a second simpler method
was used: a search was made for reference points within 50 m and the height
differences were tabulated and averaged without the slope correction stage.
Virtually all the reference height data for both sites were obtained under
cold conditions in April or early May and we assumed that any accumulation
or change in the backscatter conditions between January and mid-May would
lead to a relatively small change in the CryoSat height. This provided the
rationale for comparing all the CryoSat results from the January to May
passes with the April or May reference height data.
Estimating height errors in the CryoSat data
Ku band radar waves can penetrate the surface and the CryoSat-to-surface
height bias will vary depending on the conditions of the surface and near
surface (Gray et al., 2015; Nilsson et al., 2015). Consequently, we use the
standard deviation of the height differences about the mean height
difference as the primary measure of the quality of the CryoSat
measurements. The relatively small error in the ATM or ALS laser surface
heights (∼ 20 cm; Krabill et al., 2002) is ignored, and any
impact due to the difference in the footprint size is not considered.
Information on the conditions and results of the analysis of the CryoSat
data for the two lake features L1 (70.275∘ N, 48.56∘ W) and L2 (70.178∘ N,
48.55∘ W) shown in Figs. 13 and 14. The “Min dB” column reflects the lower
limit of the sample power used in the averaging of the height estimates
contained within the window around the surface depression.
When estimating the height errors for the supraglacial lakes it is not
appropriate to quote the standard error (standard deviation divided by the
square root of the number of samples averaged), because the samples will not
be independent and there is the possibility of small bias error in the
result. The errors were therefore estimated on a case-by-case basis by
looking at any cross-track slope across a lake feature, using the standard
deviation itself, and checking independent estimates from ascending and
descending passes over the same feature. The standard deviation about the
mean was typically ∼ 0.5 m, and the mean difference between
the ascending and descending passes over the same accumulation zone lake
feature in August was ∼ 0.25 m. Table 1 includes the error
estimates from two lakes and shows that relatively good precision can be
achieved for these strong targets, better than the potential error for
individual POCA estimates.
Results: SARIn mode calibration
The key parameters for SARIn mode geocoding are the range to the surface
and the satellite look angle between the normal to the WGS84 ellipsoid and
the footprint centre in the cross-track plane. The former involves
consideration of timing and the retracker algorithm for the POCA results,
but it is the latter which requires careful calibration for both POCA and
swath-mode results.
The satellite look angle, α, is related to two other angles through
α=β-δ,
where β is the interferometric angle defined below and δ is
the roll angle of the interferometric baseline, all defined in the
cross-track plane containing the line normal to the WGS84 ellipsoid. The
angle β is related to the interferometric phase through (Galin et
al., 2012)
β=-asin(χ/kB),
where χ is the phase provided in the L1b file, k is the wavenumber,
B is the length of the interferometric baseline, and the CryoSat altimeter
transmits through the left antenna and receives from both. The sense of the
look and interferometric angle is as follows. For zero roll an observer
siting on the CryoSat satellite facing in the direction of motion with their
feet pointing towards the Earth will “see” a footprint to the right of the
sub-satellite track when the look angle α is positive. The roll
angle δ is also provided in L1b files. For the same observer
configuration, a positive roll angle corresponds to the left antenna being
higher than the right-hand one.
Any bias in the look angle, Δα, can then be related to biases
in the baseline (ΔB), phase (Δχ), and roll angle (Δδ) through
α+Δα=-asinχ+ΔχkB+ΔB-δ+Δδ.
Using the approximations that sinx=x for small x and
B≫ΔB leads to an expression for the bias in roll angle as
Δα=-ΔχkB+χkBΔBB-Δδ.
The CryoSat satellite and processing chain contains careful controls, which
should minimize any extraneous inter-channel phase shift Δχ on
the satellite (Bouzinac, 2012). Even if a residual phase bias exists, due
perhaps to an uncompensated path length difference between the two
receivers, it can be expressed in the same form as the roll-angle correction
Δδ and the two can be considered together. The second term in
Eq. (4) reflects the possibility of a bias between the actual and
pre-launch measurement of the interferometric baseline – the distance between
the two antenna phase centres. This was part of the post-launch SARIn mode
calibration carried out by Galin et al. (2012). This work used results from
satellite roll manoeuvres over mid-latitude ocean tracks to show that the
interferometric angle should be scaled by a factor of 0.973 ± 0.002,
which is equivalent to scaling the baseline by a factor of 1.0277. The third
term in Eq. (4), the uncertainty in the baseline roll angle Δδ, is important because the baseline roll angle is derived from one of three
star trackers mounted on a support bench on the satellite. Galin et al. (2012) identified a problem with the reported roll angle and suggested that
this was due to bending of the support bench under a changing thermal
environment. However, recent work by ESA (Scagliola et al., 2017) showed
that the roll-angle problem arose, at least partly, because of an error in
processing the star-tracker data. Consequently, there is currently an
unknown bias in the reported value of the baseline roll angle which can vary
pass to pass. In the following sections, we use SARIn data over
well-documented glacial ice to investigate any residual bias in the roll
angle provided in the L1b files and to study the influence of changing the
baseline length in processing L1b files.
Calibration test sites
We used data from two sites, the western flank of Devon Ice Cap (Fig. 1) and
an area in western Greenland including the Jakobshavn Glacier (Fig. 2), as
both have excellent reference surface height data. Our calibration approach
depends on the presence of a predominantly east–west slope, which is why the
test area in Fig. 1 is limited in the north–south direction. By using
terrain with an east–west slope we obviate the necessity for roll tilting
the satellite. Figure 3 illustrates the difference in the slopes for the two
test sites. The significant increase in slope variation in the western
Greenland site represents a more challenging situation for satellite radar
altimetry than the more modest slope variation on the western flank of Devon
Ice Cap, and this is the reason we have concentrated on comparing the
results from these two test sites.
Location of the test area on the western slopes of the
Devon Ice Cap (black rectangle). The sub-satellite positions of the spring
2011 ascending and descending passes crossing the test area are shown by the
red and black lines respectively. The positions of the reference surface
height data are shown in blue, the elevation profile in Fig. 3c is a black
line, and the sub-satellite track for the waveform power in Fig. 6a is
labelled. The insert shows the position of Devon Ice Cap (circled) in the
Canadian Arctic Archipelago.
The positions of the reference ATM surface elevations
flown by NASA IceBridge missions over the western Greenland site in spring 2011
are shown in green. Sub-satellite CryoSat tracks for the period 20 January to
16 May 2011 are shown by red (ascending) and blue (descending) lines. The
inset map shows the position of the test area in Greenland and the
background image is a black–white representation of the GIMP reference DEM
(Howat et al., 2014). The position of the height profile in Fig. 3a is shown
by the black line.
Calibration based on data from Devon Ice Cap
The western portion of Devon Ice Cap has suitable cross-track slopes for
swath-mode height estimation for both ascending and descending passes, and
this area was used in the demonstration of swath-mode processing (Gray et al., 2013). While the possible range of average cross-track slopes can be
∼ 0.5 to ∼ 2∘, here we
have restricted the use of results to east–west slopes of ∼ 0.7–1.5∘ over a distance of > 5 km as this
range generally provides a better suppression of the ambiguous range
contribution. Figure 1 shows the positions of the spring 2011 surface height
reference data obtained from NASA and ESA supported overflights and from
surface snowmobile dGPS transects, all superimposed on a colour
representation of the reference DEM. The sub-satellite tracks of 15 CryoSat
passes are also shown. Results from all the passes in this time period were
compared to the reference surface heights as conditions on Devon Ice Cap
change little between January and May, and we assume that any change in
surface height or change in the bias between the surface and CryoSat height
was small with respect to the error in the CryoSat heights.
Illustration of the difference in slopes for a typical
western Greenland transect (70.37∘ N, -47.73∘ W to
69.35∘ N, -48.42∘ W; black line in Fig. 2) derived from
an ATM flight line from 6 April 2011 (a; elevation and b; slope) and
the east–west transect (black line in Fig. 1) from western Devon Ice Cap (c;
elevation and d slope, Fig. 1).
The histogram of the difference between the reference and CryoSat swath-mode
heights obtained with the pre-launch baseline estimate (1.1676 m; Bouzinac,
2012) showed a bimodal distribution and the average bias changed between
ascending and descending passes, ∼-0.5 and ∼ 2.5 m respectively. As we could find no reasonable geophysical explanation
for this difference, the possibility of a roll-angle bias was investigated.
If there were a roll-angle bias on an ascending pass the swath-processed
height estimates would be displaced either up- or down-slope depending on
the sense of the bias. However, with a descending pass and the same
roll-angle bias, the results will be displaced in the opposite direction and
the height bias will have the opposite sign from that obtained with the
ascending pass. To investigate this effect further, all the data in this
time period were reprocessed with an additional roll-angle bias added to the
value provided in the L1b file. Figure 4 illustrates the results of an
experiment in which the 15 2011 passes (seven ascending and eight descending) were
each reprocessed nine times with an additional roll correction varying from
-0.02 to +0.02∘. The results were then compared to
the reference height data collected in early May 2011. As expected, the
sense of the height difference changes between ascending and descending
passes but the curves do not overlap well. While the results from the 8 2011
descending passes do cluster nicely, this was not the case for the 2012 data
(Gray et al., 2016), and neither year shows consistent results for the
ascending pass results.
Illustration of the changing bias between the reference
and CryoSat (CS) swath-mode heights for the Devon test site as an additional
roll bias is subtracted from the roll figure given in the L1b file. Results
for seven ascending and eight descending passes in the winter–spring of 2011 are
shown in red and black respectively. The reference–POCA height variation
with the added roll-angle bias is shown with the dashed lines.
Consequently, it appears that the roll angle provided in the L1b file has a
time variable bias, apparently due to a problem in processing the
star-tracker data (Scagliola et al., 2017). The uncertainty in the roll
angle in this example appears to be of the order of 0.006∘ or
∼ 100 µ radians, not inconsistent with the observations
in Galin (2012). While there will be a contribution from the range ambiguous
zone in swath-mode processing, which could introduce a small bias, this does
not appear to be the primary source of these differences. The roll-angle
uncertainty, and resulting unknown bias in the baseline roll angle, appears
to be a limitation to the use of swath-mode heights. Note that in Fig. 4
there is essentially no slope to the plots of the height difference versus
roll-angle bias for the POCA height estimates. This is a direct consequence of
the fact that while the POCA estimates are mapped incorrectly when there is
a roll-angle error, the derived height can still be appropriate for the
wrong position because the incident wave may still be essentially
perpendicular to the surface (Gray et al., 2013).
Histograms of the reference minus CryoSat swath heights
for Devon Ice Cap: (a) pre-launch baseline and a roll-angle offset of
0.0075∘; (b) modified baseline with zero roll
offset; (c) pre-launch baseline with zero roll offset; and (d) modified baseline with a
roll offset of 0.0075∘.
The variable east–west cross-track slope also provides a suitable test area to
check the phase to cross-track angle conversion dictated by the baseline
(Eq. 2 above). Figure 5 illustrates the results of an experiment in which
the results obtained with a phase-to-angle conversion based on the
pre-launch baseline are compared to the calibration given by Galin et al. (2012). The two histograms on the left used the pre-launch baseline while
the histograms on the right used the angle scaling from Galin et al. (2012).
Figure 5c shows the bimodal distribution referred to earlier, and Fig. 5a
shows the improved results with a significantly narrower error distribution
when a bias of 0.0075∘ is subtracted from the roll angle provided
in the L1b file. The uncertainty in this additional roll bias has been
estimated as ±0.0025∘. When the phase-to-angle conversion is scaled
by 0.973 (Fig. 5b and d), the results show a broader distribution and poorer
results.
Calibration based on data from western Greenland
We use IceBridge data from an area in central western Greenland (Fig. 2,
insert), including the Jakobshavn Glacier, that has shown
significant surface height loss in recent years due to change in both output
flux and surface mass balance (Joughin et al., 2008; Qi and Braun, 2013)
and has excellent reference surface height data (Krabill et al., 2002;
Krabill, 2014).
Figure 2 illustrates the positions of the reference surface height data
obtained from the four NASA IceBridge flights flown on 31 March and 6,
7, and 23 April 2011 superimposed on a black and white representation of the GIMP
DEM (Howat et al., 2014). This DEM was used as the reference DEM for all the
CryoSat processing in this area. Data from the ATM L2 files have been used
for this work and compared with height results from all the CryoSat passes
between 16 February and 23 April.
It is important to recognize the differences in this test site in relation
to that on Devon Ice Cap. The two profiles in Fig. 3 show that even in the
accumulation area of this part of western Greenland the slope variation is much
larger than on the east–west profile interpolated from the airborne laser altimeter
flown over of Devon Ice Cap. The difference is also very apparent in the
CryoSat results: Fig. 6 compares two image representations of the waveform
power for 22 km segments of the 7 February 2011 ascending pass over Devon Ice
Cap and the 21 April 2011 descending pass over the western Greenland test site.
For the ascending pass over Devon Ice Cap the POCA position will be on the
left, close to the beginning of the 240 m range window, as indicated by the
stronger return signals in red. However, for the western Greenland site the
peak return is often in the middle of the waveform. The difference in the
signals may be influenced by the different conditions but it is clear that
the dominant reason for the differences in waveforms are the differences
in the cross-track slopes. The larger slope variation in western Greenland
clearly influence the CryoSat returns, and the waveform shape is now much
more variable than those from the Devon test site. This situation favours a
retracker which looks for the first significant leading edge, rather than
one that assumes a particular model for the waveform and then fits the
waveform to that model, as is the case for the ESA L2 SARIn product. Some
details of the retrackers used in the baseline C L2 products are given in
Buffard (2015).
Waveform power for 22 km segments of (a) the 7 February 2011
ascending pass over Devon Ice Cap (Fig. 1) and (b) the 21 April 2011
descending pass (Fig. 12a) over the western Greenland test site. The return
power in dB is represented in colour and the individual waveforms have been
shifted in the x direction depending on the time delay to the first sample
and the satellite elevation above the WGS84 ellipsoid.
Figure 7 compares the results obtained with our geocoding and that obtained
with CryoSat L2 retracker. Our processor picks out the POCA position
satisfactorily (black dots on Fig. 7a) and leads to the mapping solution
shown in Fig. 7b. The positions of the CryoSat L2 solutions are shown in
Fig. 7b as purple dots and are often different by many kilometres. The
solutions are close only when the waveforms show a clear maximum close to
start of the waveform (e.g. at ∼ 70.05∘ N). Using the position
of the L2 solution, the off-nadir look angle and equivalent phase can be
calculated. Then the position in the waveform with that phase is identified
and marked as purple dots in Fig. 7a. This shows that the L2 retracker
normally does not identify the point-of-closest-approach correctly,
primarily because of the strong peaks in middle of the waveform.
(a) Waveform power without any x axis shifts using the
same dB colour scale as in Fig. 6b. The detected POCA positions are shown in (a) for
each waveform with black dots, and they clearly correspond to the
leading edge of the waveforms. The purple dots are the estimated positions
in the waveforms of the L2 POCA solution. (b) Geographic positions of the
geocoded footprints (black dots) are compared to the positions of the ESA L2
solutions (red dots). The solid black line is the sub-satellite track.
In comparing our CryoSat POCA height results with the ATM surface height
results we found that the results here were not as precise as those obtained
over the Devon test site. However, when slightly more stringent editing was
used, in particular by increasing the minimum POCA coherence requirement to
0.8 from 0.7, then the results were improved. The histograms of the ATM
minus CryoSat heights for the 2011 spring data are shown in Fig. 8. Again
the poor results from the baseline C CryoSat L2 files are apparent (Fig. 8a),
particularly the much larger number of height errors greater than 20 m.
Results from exactly the same waveforms have been used in this comparison,
as the L2 results were removed for those waveforms already removed through
the L1b editing. While it is unfair to compare results from an operational
algorithm which must work everywhere to one which can be tuned for different
areas and includes editing based on the coherence and the return power, it
is fair to say that the current L2 retracker is inherently unsuitable for
the western Greenland site. The L2 results are better in other areas, such as
the ridges on Austfonna, ice rises and ice shelves in Antarctica, and parts
of the Devon Ice Cap. In these areas the waveforms show a more consistent
shape and the dominant return is close to the start of the waveform.
Comparisons of the ATM–CryoSat POCA height difference
histograms for the western Greenland test site. (a) The ESA L2
solution. (b) Results from the current maximum slope leading edge retracker. The mean and
standard deviation in (a) have been calculated after removal of the 39 blunders.
CryoSat data from all the passes between 16 February and 23 April 2011
have been used in this comparison, and results from the same waveforms used
in both cases.
The comparison between results obtained with the angle scaling factor from
the Galin et al. (2012) calibration (Fig. 9a and c) and without (Fig. 9b
and d) mirrors the results discussed in the previous section for Devon Ice
Cap. The results imply that the pre-launch baseline coupled with an
additional roll-angle offset (or equivalent phase shift) improves the
results for both western Greenland and Devon Ice Cap.
Comparisons of the western Greenland ATM–CryoSat height
difference histograms for the solution using the pre-launch baseline coupled
with an additional roll offset (a; POCA, c; swath-mode solutions). (b)
and (d) use the Galin et al. (2012) calibration for the POCA and swath
solutions respectively. Results from the CryoSat passes for the period 20 January to 16 May 2011 have been used.
There is an important difference in the results for this test site in
relation to Devon. For Devon, the ATM–POCA height difference was
essentially independent of the roll-angle offset between -0.02
and 0.02∘ (Fig. 4), but this was not the case for the western
Greenland site. A comparison of the average ATM–POCA height difference
over 16 passes as a function of the additional roll-angle bias (Fig. 10a)
shows that the CryoSat POCA height is not independent of the roll-angle bias
but increases for both positive and negative bias errors from a value of
∼ 0.0075∘± 0.0025∘. As the CryoSat
results are mapped incorrectly in the cross-track direction, the larger
cross-track slopes imply that the distance in the cross-track direction
which is essentially orthogonal to the incident wave is smaller in western
Greenland than for the relatively smooth surface of western Devon Ice Cap.
Consequently, this will lead to a CryoSat POCA height error as the mapping
process takes the centre of the footprint outside the region which is
orthogonal to the incident wave. Figure 10b shows the variation in the
standard deviation of the swath-mode ATM–CryoSat heights for each pass
(dotted lines) and the average over all 16 passes (black line). The offset
in the position of the minimum from zero roll-angle bias also supports the
contention that on average there is a difference between the actual baseline
roll angle and the value reported in the L1b file based on one of the three star
trackers or that there is an equivalent phase shift. For batch processing,
we have used the L1b roll angle minus 0.0075∘, but this may
change with more experience with the bias.
Illustration of the average of the ATM–CryoSat height
differences for (a) 16 passes plotted against the additional roll-angle
bias used in the processing. The error bars are ±1 standard deviation
about the mean. (b) Variation in the standard deviation (SD) of the ATM–CryoSat height difference for the individual passes (blue dotted lines) and
the average over all the passes (solid black line).
There is another discrepancy in these results that warrants explanation.
From Fig. 9a we see that the average ATM–POCA height difference is
-0.16 m, but with the same waveform data the height difference from swath-mode
processing is +0.91 m (Fig. 9c), so that the two processing methods are
giving average heights different by 1.07 m. With the Galin et al. (2012)
calibration the discrepancy is even worse: 2.52 m. Further, there is an
apparent discrepancy with the results from Devon Ice Cap where previously
(Gray et al., 2015), and now, we see the CryoSat height as being somewhat
below the physical surface. The explanation for the anomalous average ATM–POCA result for western Greenland, where the average CryoSat POCA height is
slightly above the surface, appears to be related to the results in Fig. 10a. If there is an error in the roll angle this will lead to an increase in
detected height irrespective of the sign of the roll-angle error. This will
lead to an asymmetric distribution and the mean height will be biased high.
Note that the distribution in Fig. 9a is somewhat asymmetric, more so than
that in Fig. 9c for the swath-processed data where the sign of any roll-angle
error would dictate the sign of the height error. For areas like the western
Greenland test site this implies that the roll-angle bias error will tend to
bias the average POCA height high with respect to the surface.
Unique capabilities of the SARIn mode
In this section we use our methodology and revised calibration to
demonstrate some unique capabilities of the SARIn mode, first by
illustrating signature change with surface conditions in western Greenland and
secondly by showing that it is possible to detect supraglacial lakes in the
waveform data and estimate the surface height and height change with
relatively good precision.
The effect of surface melt on SARIn waveforms
The influence of melt on SARIn signatures should be considered when
presenting temporal height change for any region which may have undergone
surface melt (Nilsson et al., 2015; Gray et al., 2015). Figure 11
illustrates one example of the influence of melt on the strength of the
SARIn waveform data. The position of this 14 July 2011 descending pass is
shown in Fig. 12a and begins at ∼ 2200 m elevation and crosses
the Jakobshavn Glacier at ∼ 1000 m; then the elevation
increases slightly before ending at ∼ 1100 m. At high
elevations, the returns are comparable to those obtained under cold
winter–spring conditions, but at lower elevations, ∼ 1700–1900 m, there is a decrease of ∼ 15–20 dB in average
waveform power. It is well known that the introduction of even a small
amount of liquid water in snow dramatically alters the emissivity and
backscatter (Ulaby et al., 1986). For example, a significant drop in
QuikSCAT 13.3 GHz backscatter was shown to be linked to melting from weather
station data (Nghiem et al., 2001). The presence of water droplets in snow
increases absorption and reduces the penetration depth, which in turn leads to
an increase in brightness temperature and decrease in radar backscatter
(Wang et al., 2016). Consequently, we associate the relatively low
reflectivity at these elevations to a damp snow layer. At lower elevations
(< 1600 m) not only is the average return larger but also the
waveform-to-waveform variability is much higher, indicative of occasional
specular reflection from a wet surface facing the radar. Also, the strongest
returns in most of the waveforms in this area are not from the leading edge
but vary in position across the waveform so that a retracker that uses all
of the waveform will not accurately measure the position and height of the
POCA.
The background image illustrates the swath waveform power
in colour with a dB scale for the 14 July 2011 descending pass over the
western
Greenland test area (Fig. 12a). The waveforms making up this pseudo-image
have been shifted in the x direction to account for the changing delay time
to the first sample and the varying satellite height above the WGS84
ellipsoid. The insert shows the sub-satellite terrain elevation and the
waveform average power both plotted against latitude.
Figure 12 illustrates the average waveform power plotted against elevation
for five descending passes (Fig. 12a) acquired during the summer of 2011. At
elevations up to ∼ 1300 m the 18 June pass (Fig. 12d) shows the high
waveform-to-waveform variability that we suggest is due to occasional
specular reflection, but this was not observed in the earlier passes in April
(Fig. 12b) and May (Fig. 12c). By 14 July (Fig. 12e) the region with strong
and variable power includes elevations up to ∼ 1600 m and
the August pass (Fig. 12f) shows some strong waveform returns at even higher
elevations. Comparable results were obtained from the five repeat passes
369 days later in 2012, but the descending pass on 20 July 2013 showed the
wet snow signature at lower elevations (∼ 1500 m) without any
indication of occasional specular reflections. This is consistent with the
relatively colder conditions at that time in 2013 with respect to both 2011
and 2012 (see e.g. Fettweis, 2016, http://climato.be/melt-2016). The
Supplement includes figures equivalent to Fig. 12 for all the years from 2012
to 2016.
Plots of the average waveform power for the five 2011
descending passes shown in (a). The five plots are from descending passes on (b) 21 April, (c) 20 May,
(d) 18 June, (e) 14 July, and (f) 12 August and
illustrate average waveform power as a function of elevation.
Supraglacial lakes
During summer melt around the periphery of the Greenland Ice Sheet water
pools in surface depressions as supraglacial lakes (Echelmeyer et al., 1991),
forming first at lower elevations and then to higher elevations as melt
progresses. With increasing positive air temperatures, surface meltwater will
infiltrate to lower elevations so that the snow at the edges of the
depression will tend to become saturated and melt before the snow in the
centre of the depression. In many cases, small supraglacial streams form,
which will add energy to melt snow or ice where they enter the surface
depression. Optical satellite imagery and DEM data have been used to study
the distribution, extent, depth, and drainage of these features when there is
an open water surface (Box and Ski, 2007; McMillan et al., 2007; Sneed and
Hamilton, 2007; Liang et al., 2012; Fitzpatrick et al., 2014; Leeson et al.,
2015; Pope et al., 2016; Ignéczi et al., 2016). While Landsat and MODIS
imagery has been used to estimate total lake volume of relatively large areas
(e.g. Pope et al., 2016), the limitations due to clouds and atmospheric
conditions hamper routine use for quantitative melt estimates. Here we
demonstrate that CryoSat SARIn data can provide complementary information to
that available from visible satellites by showing that measurements of
surface height and height change can be derived from SARIn data over
individual supraglacial lakes. SARIn data can be obtained reliably day or
night and in all weather conditions but are very limited in surface coverage.
If CryoSat passes directly over a typical unfrozen supraglacial lake one
would expect a strong specular reflection which would not be at the leading
edge of the waveform, as it must be surrounded by ice at higher elevations.
Even if the lake has some snow cover or a partially unfrozen surface, the
flat surface will still enhance the return and could lead to a strong peak
in the waveform. Figure 13 illustrates some strong signals in the middle of
the waveforms of a 50 km section of the 7 August 2011 ascending pass over
the test area in western Greenland. These may originate from extended surfaces
orthogonal, or nearly orthogonal, to the incident wave. We have selected one
such strong signal, labelled as “L1” in Fig. 13, which is detected in
results from ascending and descending passes from all the summers from 2010
to 2016. The Supplement contains a sequence of 14 summer MODIS
images from 2012 to 2016 which show that the L1 and L2 features are above
the snow line for all 5 years and that the surface of these depressions did
not become totally ice free. Figure 14 shows the positions of the
sub-satellite tracks superimposed on a summer 2016 Landsat 8 image and that
there were dark regions, presumably wet snow, at the positions of the
topographic lows marked as L1 and L2. The relative strength of the CryoSat
return signals for the seven ascending passes for both features are shown in
Fig. 15 and the year-to-year derived height in Fig. 16 with details provided
in Table 1. The sequence of dates for the repeat ascending passes are 4 August 2010, 7 August 2011, 9 August 2012, 12 August 2013, 16 August 2014, 19 August
2015, and 21 August 2016 reflecting the 369.25-day repeat orbit cycle. The
repeat descending passes are 5.5 days after the ascending passes.
Illustration of part of the waveform power from an
ascending pass over western Greenland on 7 August 2011 (Fig. 14). The bright
returns labelled as L1 and L2 are at elevations ∼ 1609 m and
1573 m respectively and represent topographic lows where water could
collect.
Ascending and descending sub-satellite repeat tracks
over, or close to, the L1 and L2 features for all the years from 2010 to
2016 superimposed on part of the Landsat 8 image of 9 August 2016 (inset
image).
“Images” of part of the CryoSat waveforms for the areas
including “L1” (a) and “L2” (b) in western Greenland for the August dates in each year from 2010 to 2016. The x and y axes of each “image” are
increasing range and increasing along-track position (north up).
Surface elevation of L1 (a) and L2 (b) between the
summers of 2010 and 2016.
Our interpretation of the strengths of the lake signatures and the surface
elevation is as follows: considering the low surface velocity
(∼ 3.5 m yr-1; Joughin et al., 2010, 2016) and elevation
(∼ 1600 m) at this position, it is unlikely that either of these
depressions drained in the manner of the lakes in the ablation zone in any of
the summers. The increase in height from the summer 2010 to 2012 (Fig. 16)
may reflect the melt at these positions, which was particularly strong in
2012 (see e.g. Fettweis, 2016, http://climato.be/melt-2016). However,
the decrease in elevation in subsequent years is then a problem. The
discovery that water can persist for years in firn aquifers (Koenig et al.,
2014; Forster et al., 2014) suggests that the decrease in elevation after
2012 may reflect a slow percolation of the meltwater into the firn. Clearly,
the specific causes of the decrease in elevation of L1 and L2 after 2012, and
the difference between the L1 and L2 height change, are not known.
Information on the conditions and results of the analysis of the CryoSat
data for the lake shown in Fig. 17 and overflown by CryoSat on the dates
shown.
The Landsat 8 image from 6 July 2016 (Fig. 17) includes one 2.4×1 km lake
at 70.37∘ N, 49.79∘ W, and ∼ 1020 m in elevation, which was
detected in the CryoSat waveforms from all the ascending and descending
repeat passes listed on Fig. 17 between 2011 and 2016. By the time of the
Landsat 8 image in 2016 most of the snow had melted and we surmise that melt
had been on-going during June and early July for the years 2011–2016 at
this position and at the times of the CryoSat overpasses (Fig. 17 and
Table 2). Figure 18 illustrates the lake height for all passes except for
the 2013 descending pass, which was too far to the west of the lake for
reliable results. In contrast to the high elevation, low melt “lake”
described above, now there is a clear height increase in the 5.5 days
between the ascending and descending passes over the lake. This allows an
estimate of the filling rate at the time of the two passes. If we assume a
lake area of 2 ± 0.5 km2 this implies a filling rate of
∼ 0.2. 106–2.106 m3 meltwater added per
day. This lake does drain sometime after the start of July (see the MODIS
sequence in the Supplement) but appears not to have drained at
the times of any of the CryoSat overpasses.
Landsat 8 image from 6 July 2016 of an area in the
ablation zone of the western Greenland test site which includes a lake viewed by CryoSat on all the repeat ascending and descending passes
listed on the image. The insert image shows the magnified position in the full
Landsat 8 frame.
Discussion
In this section we discuss the two SARIn processing approaches and the
limitations and successes of the current CryoSat SARIn products for glacial
ice.
(a) Surface height of the lake in Fig. 17 at the times of
the overpasses; (b) height increase during the 5.5 days between the
ascending and descending passes. The dates listed in the lower plot are at
the middle of the 5.5-day period between the ascending and descending
passes.
There are two important advantages with swath processing: firstly, there is
no need for a retracker and, secondly, the swath data are obtained
predominantly from the region directly beneath the satellite and the look
angles for the swath footprints can be less than those for the POCA (for
those areas with cross-track slopes appropriate for swath processing). With
the small look angles, the footprint illumination cross-track is essentially
uniform. Consequently, assuming a small contribution from the range
ambiguous area, the phase should represent the geometric centre of the
footprint so that the range, satellite state vectors, and the various angles
lead to reliable heights. Unfortunately, the roll-angle problem
discussed earlier compromises the swath-mode results as the resulting
cross-track mis-mapping will normally lead to a height error (Gray et al.,
2013).
POCA processing requires a retracker and the look angle can extend into the
range in which the illumination cross-track is affected by the antenna
pattern variation so that the phase may not reflect the geometric centre of
the footprint; instead, it may be displaced towards the sub-satellite track.
With interferometric swath processing, precise knowledge of the baseline and
baseline angles is important (Rosen et al., 2000), and with the CryoSat
roll-angle problem individual POCA heights are normally more precise than
swath-mode heights. For height change estimates, however, both POCA and
swath-mode results can be combined as long as any bias is accounted for.
Foresta et al. (2016) used primarily swath-mode results in a study of
elevation change of Icelandic ice caps, showing the improved surface
coverage of swath mode and that height change information could be derived
from these results. Also, Smith et al. (2017) combined swath and POCA data
to document surface height change on the Thwaites Glacier. However, in this case
“metre-scale biases”, correlated over tens of kilometres but independent
orbit-to-orbit, were partially corrected by combining with the POCA data.
The known problem in processing the star-tracker data (Scagliola et al.,
2017), and the resulting varying error in the reported value of the baseline
roll angle, can have an impact on the precision of the CryoSat height
results. Any roll-angle error translates directly into a cross-track mapping
error so that the resulting height error then depends on the angle between
the incident wave and the tangent to the cross-track surface. If this angle
is 90∘ and the surface slope changes slowly over a few hundred
metres, then the error is small as the geocoding algorithm produces the
correct elevation for the mis-mapped footprint. Although we show that the
roll-angle problem had essentially no impact on the Devon POCA results, it
did have an impact on the POCA results from the western Greenland test site. In
this case the cross-track slopes varied more rapidly than for Devon and lead
to the situation where an incorrect roll angle could lead to an increase in
the CryoSat height with respect to the surface irrespective of the sense of
the roll-angle error. This we suggest is the origin of the unrealistic
result that the average POCA height was slightly above the physical surface
for the western Greenland site.
POCA heights originate from ridges and peaks and, when the cross-track slope
is appropriate for swath processing, the swath-mode results will normally
originate from the area beneath the satellite so the two approaches are
complementary in surface coverage. As discussed above, there can be a bias
between POCA and swath heights which needs to be considered if the results
are merged. The potential height error for individual estimates is normally
less for POCA data than for swath-mode heights but the exception is the
precision with which one can estimate the height of relatively large
supraglacial lakes when the lake is beneath the satellite and viewed at
close to normal incidence. In this case, we have a very strong signal in the
middle of the waveform, any range ambiguous contribution should be small,
and no retracker is required for the geocoding solution. Further, with this
viewing geometry the problem of an incorrect roll angle leads to a small
error in the lake surface height and a precision of ∼ 0.5 m is
possible for the surface height of a large lake. Work is underway to better
evaluate the extent to which CryoSat data can help in quantifying the time
and extent of melt around Greenland.
The ability to geocode the relatively small footprint possible with the
SARIn mode over glacial ice creates a huge advantage for this mode over the
traditional low-resolution radar altimetry. Future radar altimeters
employing coherent along-track processing, either fully focussed or
delay-Doppler, coupled with cross-track interferometry could play a very
important role in monitoring change on many ice caps and glaciers.
Conclusions
Here we list the specific conclusions arising from our analysis of the SARIn
data over Devon Ice Cap and western Greenland.
A more consistent fit can be obtained between CryoSat and surface heights
using the pre-launch baseline coupled with an additional roll-angle bias of
∼ 0.0075∘± 0.0025∘. Although the additional bias may
originate with the angle measurement, it could equally well be an
equivalent additional phase correction of ∼ 0.0435 ± 0.0145 radians to
the value of 0.612 radians currently used in the baseline C product
(Bouzinac, 2012).
A retracker which uses the first significant leading edge of the waveform
normally leads to more reliable elevations than a retracker that uses the
whole waveform; this appears to be particularly true for areas like western
Greenland in which the shape of the waveform is very variable and the peak
signal is often in the middle of the waveform.
Swath-mode results complement the POCA results but are normally less
precise. The exception is the precision with which the heights of
supraglacial lakes can be obtained when the satellite flies almost directly
over the lake.
The uncertainty in the CryoSat baseline roll angle affects primarily swath-mode results but can also impact the precision of POCA results when the
surface topography is comparable to that in the western Greenland test site.
While more work is required to establish to what extent CryoSat SARIn
waveforms and heights can improve our knowledge of melt in the ablation zone
of the Greenland Ice Sheet, these initial results indicate that
CryoSat SARIn data can help provide useful information on the variation of
year-to-year melt.
Both L1b
and L2 Cryosat-2 data are publically available (ESA, 2017) and can be
downloaded using the CryoSat User Tool software available from
https://earth.esa.int/web/guest/-/cryosat-user-tool-7386. The
laser-scanned surface height reference data flown by NASA (Krabill, 2016)
under the IceBridge programme are available from
https://nsidc.org/icebridge/portal/map. Further information on the
custom software used to process the CryoSat L1b data to geocoded height data
can be obtained from the first author. Specific results, e.g. the heights
derived from the western Greenland site for comparison with the spring 2011
reference surface height data, can also be obtained from the first author.
The Supplement related to this article is available online at doi:10.5194/tc-11-1041-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by the European Space Agency through the provision
of CryoSat-2 data and the support for the CRYOVEX airborne field campaigns
in both the Canadian Arctic and Greenland. NASA supported the IceBridge
flights over the Canadian Arctic and Greenland, while NSIDC facilitated
provision of the airborne laser data. The Technical University of Denmark
(TUD) managed the ESA supported flights over Devon. The IceBridge and TUD
teams are gratefully acknowledged for the acquisition and provision of the
airborne data used in this work. The Polar Continental Shelf Project
(Natural Resources Canada) provided logistic support for field work in the
Canadian Arctic, and the Nunavut Research Institute and the community of
Resolute Bay gave permission to conduct research on the Devon Ice Cap.
Support for D. Burgess was provided through the Climate Change Geoscience
Program, Earth Sciences Sector, Natural Resources Canada and the GRIP
programme of the Canadian Space Agency. Support for K. Langley
was provided by ESA project Glaciers-CCI (4000109873/14/I-NB) and GlacioBasis Nuuk of the Greenland Ecosystem Monitoring programme. T. Dunse and G. Moholdt
were supported by ESA-Prodex project 4000 110 725/724 “CRYOVEX” and T. Dunse
was supported by the Nordforsk-funded project Green Growth Based on Marine
Resources: Ecological and Sociological Economic Constraints (GreenMAR).
Wesley Van Wychen and Tyler de Jong helped with the 2011 kinematic GPS
survey on Devon. NSERC funding to L. Copland is gratefully acknowledged. We
also acknowledge NASA and NSIDC for the provision of the Landsat 8 and MODIS
imagery.
Tommaso Parrinello, ESA, and Michele Scagliola, Aresys, provided information
on the problem in processing the star-tracker data. We also appreciate the
work of the editor, Ian Howat, and the four anonymous reviewers who provided thoughtful and
helpful reviews.
Edited by: I. M. Howat
Reviewed by: four anonymous referees
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