Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation

Abstract

Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar – SAR) can address the issue and outperform methods based on passive microwaves. Thus, high-spatial-resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) investigate SD and depth hoar fraction (DHF) variability between different vegetation classes in the Ice Creek catchment (Qikiqtaruk/Herschel Island, Yukon, Canada) using in situ measurements collected over the course of a field campaign in 2019; (2) evaluate linkages between snow characteristics and CPD distribution over the 2019 dataset; and (3) determine CPD seasonality considering meteorological data over the 2015–2019 period. SD could be extracted using the CPD when certain conditions are met. A high incidence angle (>30∘) with a high topographic wetness index (TWI) (>7.0) showed correlation between SD and CPD (R2 up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.

Full text

The Cryosphere, 16, 2163–2181, 2022
https://doi.org/10.5194/tc-16-2163-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Potential of X-band polarimetric synthetic aperture radar co-polar
phase difference for arctic snow depth estimation
Joëlle Voglimacci-Stephanopoli1,2, Anna Wendleder3, Hugues Lantuit4,5, Alexandre Langlois1,2, Samuel Stettner3,6 ,
Andreas Schmitt7, Jean-Pierre Dedieu2,8, Achim Roth3, and Alain Royer1,2
1Département de géomatique appliquée, Centre d’Applications et de Recherches en Télédétection, Université de Sherbrooke,
Sherbrooke, J1K 2R1, Canada
2Centre d’Études Nordiques, Université Laval, Québec, Québec, G1V 0A6, Canada
3German Remote Sensing Data Center, German Aerospace Center, Oberpfaffenhofen, Germany
4Institute of Geosciences, University of Potsdam, Potsdam, Germany
5Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, 14473 Potsdam, Germany
6German Space Agency, German Aerospace Center, Bonn, Germany
7Institute for Applications of Machine Learning and Intelligent Systems, Munich University of
Applied Sciences, Munich, Germany
8Institute of Environmental Geosciences, Université Grenoble-Alpes/CNRS/IRD, 38058 Grenoble, France
Correspondence: Joëlle Voglimacci-Stephanopoli (joelle.voglimacci-stephanopoli@uqar.ca)
Received: 29 September 2021 – Discussion started: 13 October 2021
Revised: 27 April 2022 – Accepted: 6 May 2022 – Published: 9 June 2022
Abstract. Changes in snowpack associated with climatic
warming has drastic impacts on surface energy balance in
the cryosphere. Yet, traditional monitoring techniques, such
as punctual measurements in the field, do not cover the full
snowpack spatial and temporal variability, which hampers ef-
forts to upscale measurements to the global scale. This vari-
ability is one of the primary constraints in model develop-
ment. In terms of spatial resolution, active microwaves (syn-
thetic aperture radar – SAR) can address the issue and out-
perform methods based on passive microwaves. Thus, high-
spatial-resolution monitoring of snow depth (SD) would al-
low for better parameterization of local processes that drive
the spatial variability of snow. The overall objective of this
study is to evaluate the potential of the TerraSAR-X (TSX)
SAR sensor and the wave co-polar phase difference (CPD)
method for characterizing snow cover at high spatial reso-
lution. Consequently, we first (1) investigate SD and depth
hoar fraction (DHF) variability between different vegetation
classes in the Ice Creek catchment (Qikiqtaruk/Herschel Is-
land, Yukon, Canada) using in situ measurements collected
over the course of a field campaign in 2019; (2) evaluate link-
ages between snow characteristics and CPD distribution over
the 2019 dataset; and (3) determine CPD seasonality con-
sidering meteorological data over the 2015–2019 period. SD
could be extracted using the CPD when certain conditions are
met. A high incidence angle ( >30◦) with a high topographic
wetness index (TWI) (>7.0) showed correlation between SD
and CPD (R2up to 0.72). Further, future work should address
a threshold of sensitivity to TWI and incidence angle to map
snow depth in such environments and assess the potential of
using interpolation tools to fill in gaps in SD information on
drier vegetation types.
1 Introduction
Snow cover is a key component of the cryosphere which
plays an essential role for ecological processes and hydro-
logical dynamics. In arctic ecosystems, those processes in-
clude species survival (Dolant et al., 2018; Poirier et al.,
2019), thermal ground regime (Goodrich, 1982; Gouttevin
et al., 2012; Stieglitz et al., 2003), or vegetation coloniza-
tion and growth (Berteaux et al., 2017; Kankaanpää et al.,
2018; Myers-Smith et al., 2011a). In the past 40 years, we
observed a pan-arctic reduction in the snow cover duration
of 2–4 d per decade (AMAP, 2017), and maximum arctic
Published by Copernicus Publications on behalf of the European Geosciences Union.

2164 J. Voglimacci-Stephanopoli et al.: Potential of X-band
snow depth trends have shown a consistent decrease since
1980 (AMAP, 2017; IPCC, 2019). These trends will undeni-
ably change the arctic landscape. For instance, the duration
of snow patches impacts vegetation phenology (Kankaanpää
et al., 2018) and controls shrubs’ growth (Myers-Smith et
al., 2011b; Pomeroy et al., 2006). Hence, patterns of vegeta-
tion densification (also called greening) arise, and dense veg-
etation such as shrubs impact the snowpack physical prop-
erties. Twigs induce a decrease in snow density and an in-
crease in depth hoar formation (Domine et al., 2016; Gout-
tevin et al., 2018; Sturm et al., 2001). By protruding above
the snowpack surface, shrubs reduce surface albedo and ad-
vance the snowmelt timing (Sturm et al., 2001). Coupled
to a decreasing trend in maximum snow depth and snow
cover duration observed (AMAP, 2017; IPCC, 2019), the
greening of the arctic is likely to lead to a drastic modifi-
cation of the snowpack. A recent update on the classifica-
tion of Sturm et al. (1995) suggested by Royer et al. (2021)
demonstrates a positive feedback on climate warming owed
to snow–vegetation interaction. High-resolution land cover
classification is therefore needed to address changes in the
snowpack in a warming climate.
Current snow modules used in Earth system models are
based on coarse spatial resolution of tens of kilometers
(Bokhorst et al., 2016). Coarse spatial resolution hampers
our efforts to understand the dynamics driving snowpacks at
the landscape scale. Indeed, snow is characterized by a high
spatial and temporal heterogeneity (e.g., Rutter et al., 2014;
Thompson et al., 2016; Wilcox et al., 2019). Traditional ap-
proaches using in situ measurement can provide very de-
tailed spatial information on snow properties but cannot be
deployed over large areas. There is therefore a strong need
to bridge these two scales and provide means to monitor the
temporal and spatial variability in the snowpack over larger
areas.
Earth observation satellites can provide frequent measure-
ments over larger areas. Spaceborne platforms are widely
used to monitor snow on local, regional and global scales.
Yet, they also suffer from strong limitations. Optical sen-
sors allow measurements on surface characteristics of snow
but do not provide direct measurements of the properties of
the snowpack and are often limited by cloud cover. Passive
microwave monitoring methods are operational and provide
continuous data but suffer from the coarse spatial resolution
of satellite observations (e.g., Frei et al., 2012). Active mi-
crowave observations with synthetic aperture radar (SAR)
can overcome these issues by providing high-resolution fre-
quent snow measurements over large areas.
SAR can “see” through clouds while being independent
from solar illumination. SAR sensors are interesting to col-
lect data from the snowpack because they can, on the one
hand, transmit and receive microwaves in horizontal (H) and
vertical (V) polarization, and on the other hand, their mi-
crowaves can interact with and penetrate into the observed
material.
The objective of this paper is therefore to evaluate the
potential of the polarimetric-method co-polar phase differ-
ence (CPD) produced with the X-band satellite TerraSAR-
X (TSX) to retrieve snow depth (SD) from an arctic snow-
pack where vegetation is highly variable. This general ob-
jective requires a complete characterization of the snowpack
from field data to fully understand the sensitivity of CPD to
various snow characteristics. This requirement motivates the
following three specific objectives: (1) investigate SD vari-
ability between different vegetation classes in the Ice Creek
catchment (Qikiqtaruk/Herschel Island, Yukon, Canada) us-
ing in situ measurements collected over the course of a field
campaign in 2019, (2) evaluate linkages between snow char-
acteristics and CPD distribution over the 2019 dataset, and
(3) determine CPD seasonality considering meteorological
data over the 2015–2019 period.
2 Background: co-polar phase difference – snow
structure
2.1 Arctic snow properties
Snow cover in the arctic is mostly characterized by two main
layers (Domine et al., 2016; Royer et al., 2021; Sturm et al.,
2008). The upper layer, the wind slab, is very compact as it
is subject to sustained winds and cold temperatures that pro-
mote cohesion of snow grains (Domine et al., 2018b; Sturm
et al., 2008). The basal layer generally consists of depth hoar
(DH) grains that develop under a kinetic metamorphic regime
in dry snow conditions with a sustained strong temperature
gradient (Domine et al., 2016). Kinetic growth refers to the
formation of depth hoar crystals within the snowpack in-
duced by a strong thermal gradient.
The snowpack is driven by two types of metamorphic
regimes, namely wet and dry snow metamorphism (Bernier
et al., 2016). These regimes develop according to the temper-
ature gradient in the snowpack and to its liquid water con-
tent (Colbeck, 1973). Wet snow metamorphism, with liquid
water available in the snowpack, will lead to different meta-
morphic processes for saturated and unsaturated conditions
(Colbeck, 1982). As a result, there will be a major impact
on microwave radiative transfer given that wet snow acts as a
blackbody in such frequencies (e.g., Rott and Matzler, 1987).
In the case of an arctic snowpack, a regime of dry snow meta-
morphism is generally found when sustained cold tempera-
tures last during most of the winter (Domine et al., 2018a).
Schneebeli and Sokratov (2004) found that snow crystals are
highly anisotropic (dependency on a direction of an object),
which is correlated with snow metamorphism (Calonne and
al., 2014; Gouttevin and al., 2018). As such, over time, snow
crystals become elongated to a vertical direction after and
during the constructive snow metamorphosis in the snow-
pack.
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J. Voglimacci-Stephanopoli et al.: Potential of X-band 2165
Figure 1. Phase shift (1ρ) can also be caused by scattering effects
within the snowpack or by surface roughness (including vegetation)
(credit: Anna Wendleder).
The geometrical structure of the snow will characterize the
electromagnetic wave propagation through the snowpack by
scattering and absorption processes within each layer (Mät-
zler, 1987). Given the dry nature of the arctic snowpack,
the main source of backscattering should occur at the snow–
ground interface for frequencies in X-band (χ=3.1 cm), as
used in this study, or below as dry snow can be considered
as a homogeneous, “non-scattering” and non-absorbing vol-
ume (Leinss et al., 2014). This said, inhomogeneous layers
such as ice layers, melt–freeze crust and any strong verti-
cal change in dielectric properties (i.e., density, wetness) can
also affect the signal.
2.2 Co-polar coherence
The co-polar coherence (CCOH) indicates the correlation co-
efficient of HH and VV phase centers. The magnitude of the
CCOH ranges between 0 and 1, in which a weak correlation
(<0.5 as defined by Leinss et al., 2014) indicates a low scat-
tering with more chaotic and random phase shifts between
HH and VV waves and is hence omitted. Such weak corre-
lations will occur when HH and VV waves have different
phase centers and different scattering targets (see Fig. 1 with
the phase shift 1ρ). A decrease in correlation can also be in-
duced by a strong surface scattering caused by rough or wet
surfaces or volume scattering during winter or during snow-
free conditions in which vegetation is exposed (Fig. 1). The
equation of CPD is only valid when no volume scattering
occurs (Leinss et al., 2014) since an increase in volume scat-
tering will lead to a decrease in the CCOH.
2.3 Co-polar phase difference
CPD is a polarimetric method using the difference in the
phase between HH and VV polarization channels. The phase
difference refers to the difference in the propagation speed
of a wavelength in a material as a function of polarization,
which then causes a phase difference in the electromagnetic
wave between polarizations. The phase of a single polariza-
tion is assumed to have a uniform distribution over [−π,π]
(Leinss et al., 2014; Patil et al., 2020).
A relationship was found between CPD and snowfall by
Chang et al. (1996) and Leinss et al. (2014) which induces a
propagation delay among horizontal and vertical phases due
to horizontal alignments of fresh snow crystals. Recent stud-
ies focused on the boreal region (Leinss et al., 2014, 2016)
or were applied in arctic regions with no or sparse vegetation
(Dedieu et al., 2018), so the application of the CPD method
in the arctic remains poorly documented. It could be hypoth-
esized that the CPD can describe the entire snowpack in such
cold and dry environments. Strong vertical changes in den-
sity and grain size could also lead to a decrease in coher-
ence so that the use of CPD information might not be suitable
(i.e., when CCOH <0.5; see Fig. 1).
3 Data and methods
3.1 Study site
Qikiqtaruk/Herschel Island (69◦350N, 139◦060W) is located
about 2 km off the Yukon Coast in thenorthwestern Canadian
arctic (Fig. 2). With an approximate area of 108km2, this
island has a rolling topography (max. altitude: 183 m a.s.l.),
dissected by numerous geomorphological forms such as gul-
lies, valleys and polygonal soils (Short et al., 2011; Stettner
et al., 2018). The permafrost on Qikiqtaruk/Herschel Island
is continuous with a high ice content. Ground ice can be ob-
served on the island in the form of ice wedges, ice lenses,
or buried snowbanks, as observed by Pollard (1990). Results
from Wolter et al. (2016) suggested that geomorphological
processes, such as permafrost degradation, are strongly re-
lated to vegetation composition on Qikiqtaruk/Herschel. The
active layer thickness varies from 45 to 90cm in marine de-
posits (silty diamicton) and can reach over 110cm in porous
deposits (Lantuit and Pollard, 2008; Smith et al., 1989). A
thickening of the active layer by 15 to 25cm was documented
on the island during the period from 1985 to 2005, as well as
an increase in the mean annual air temperature by 2.7 ◦C be-
tween 1970 and 2005 (Burn and Zhang, 2009).
The spatial distribution of snow on the island is primarily
based on topography due to the low tundra-type vegetation
(Burn and Zhang, 2009). The snow is blown away from the
uplands and accumulates in topographic depressions such as
valleys and hummocky terrain (Burn and Zhang, 2009). The
dominant wind direction is northwest with frequent storms
in late August and September (Solomon, 2005). A study by
Myers-Smith et al. (2011) indicates an increase in the canopy
and vegetation height over the last century that can be ex-
pected to have an impact on the snow cover structure.
In this paper, we performed our measurements in the Ice
Creek catchment (area of 1.54 km2)located at the eastern end
of the island (Fig. 2). The digital elevation models (DEMs)
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2166 J. Voglimacci-Stephanopoli et al.: Potential of X-band
from ArcticDEM (2 m resolution) indicate average slopes of
2.9◦with maximums of 13.2◦at altitudes ranging from 5 to
94 m (Porter et al., 2018).
3.2 Snow distribution over Ice Creek
3.2.1 Snow measurements
Two sampling strategies were used for the snowpit character-
ization (Table 1). First, detailed snowpit measurements were
conducted along predefined locations at an average distance
of 200 m between each site (Fig. 2c). The snowpit locations
in the center of the Ice Creek catchment, as well as loca-
tions at the outlet of the catchment, were revisited during
each TerraSAR-X (TSX; see Sect. 3.3) acquisition so that
soil characteristics remain unchanged between snow sam-
pling and satellite measurements. Snow depths were mea-
sured using a GPS snow depth probe around the snowpits,
ensuring the representativeness of the snowpit location. This
was conducted by measuring depths in a growing circle mov-
ing away from the snowpit location until an approximate di-
ameter of 30 m was reached, which is typically the area re-
quired to ensure representativeness in tundra environments
(Clark et al., 2011). Snowpits and SD measurements were
then distributed spatially elsewhere in the catchment to refine
the characterization of snow within the catchment. Addition-
ally, two SD transects were conducted across the catchment
to analyze the SD distribution in the study site. Both tran-
sects were established from the west side to the east side of
the Ice Creek catchment. These transects were acquired on
1 May 2019 (Transect #2) and 4 May 2019 (Transect #1).
Detailed snow profiles were acquired in spring 2019 (mid-
April to early May). In each site, we dug snowpits in a way
to avoid direct solar illumination of the snow wall. High-
resolution vertical profiles of density, temperature, grain size
and type were conducted according to Fierz et al. (2009; see
Table 1). Specifically, layered density profiles were obtained
by extracting snow samples from each identified layer using
a 100 cm3density cutter and weighed using a Pesola light
series scale. Temperature profiles were measured at 3cm in-
tervals using a Cooper digital thermometer, and profile mea-
surements included shadowed surface temperature, as well as
soil–snow interface.
From the above observations, each layer was classified
according to their density and snow grain type across six
classes following Fierz et al. (2009): (1) depth hoar, (2) wind
slab, (3) surface hoar, (4) fresh snow, (5) melt–freeze crust
and (6) ice layer. The snow depth and mean density of each
layer classified were compiled for later linear regression
analysis with TSX data from the same period. Regression
analysis will be used to reach objective (2) of this paper.
3.2.2 Vegetation units
The classification of the different vegetation units was ob-
tained from Eischeid (2015) following the initial definition
developed by Smith et al. (1989). The classification was de-
termined by the soil type, vegetation observed and geomor-
phological features. The dataset used in this study was de-
rived from 2015 GeoEye satellite data (resolution: 1.65m)
(Eischeid, 2015). For the specific needs of this paper, we
focused on the following specific classes: Arctic Willow
and Dryas Vetch (hereinafter referred as Dryas), Arctic Wil-
low and Lupine (Lupine), Shrub Zone (Shrub), and Willow
Saxifrage Coltsfoot (Coltsfoot). These classes were selected
given that they are physically and spatially different (see
Fig. 3d), which is of primary importance from a snow mi-
crostructure and radar backscattering perspective.
The Lupine class is associated with an irregular and hum-
mocky terrain (Eischeid, 2015, see Fig. 3d). The high vari-
ability in microtopography results in equally heterogeneous
SD at a similar scale (Sturm and Holmgren, 1994). Erosion
rates and moisture content will vary greatly following ter-
rain instability (Eischeid, 2015). The Coltsfoot class is com-
mon in wetlands, where the ground is generally saturated
and composed of shrubs (Eischeid, 2015). This vegetation
class is located at the bottom of valleys, which is suitable
for snow accumulation (Burn and Zhang, 2009). The Shrub
class was added by Eischeid (2015) to the original classifica-
tion by Smith et al. (1989) to reflect the growing importance
of shrubs on the island. It is characterized by non-hydrophilic
vegetation with lower soil moisture. Finally, the Dryas class
is common on the gently undulating upland slopes (Smith
et al., 1989). The associated soil type is a moderately well-
drained Turbic Cryosol, which shows evidence of cryotur-
bation, as well as bare soil. Each snowpit characteristic and
SD measurement were grouped by vegetation units to ex-
tract means and standard deviation by vegetation classes. The
snowpits made along the two transects were grouped when
they were at a distance less than 30 m, and statistics of dis-
tribution (average and standard deviation) were extracted to
complete the data analysis.
3.3 Snow–SAR relationship
3.3.1 SAR acquisition and preprocessing
A total of five TSX acquisitions in HH and VV polarizations
over three different orbits were obtained during spring 2019,
encompassing areas where snow measurements and vegeta-
tion information was available (Table 2). Snowpits and SD
measurements taken before and after (±2 d) each TSX ac-
quisition were included in the analysis as no precipitation
occurred and air temperature was stable during the field cam-
paign. Additionally, a time series of TSX acquisitions for
the 2015–2019 period (orbit 24, θ=31◦) was analyzed to
evaluate the interannual variability in snow conditions on
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J. Voglimacci-Stephanopoli et al.: Potential of X-band 2167
Figure 2. (a) Location of the study site in the arctic. (b) Visual extent of TerraSAR-X passages on Qikiqtaruk/Herschel Island. (c) Ice
Creek study site including the location of measurements. The black crosses were revisited during each TerraSAR-X acquisitions (imagery
provided by Worldview 01.01.1001, true color). The meteorological station belongs to Environment and Climate Change Canada (ECCC).
AOI signifies area of interest.
Table 1. In situ measurements during the 2019 field campaign.
Snowpits See Fig. 2c
Stratigraphy Snow height
Size and grain type (visual estimation)
Temperature profile (measurement at 3cm, ±0.1◦C)
Snow density by layers (measurements at 5 cm when possible,
±0.5 kg m−3)
Environment and Climate Change Canada
(ECCC) meteorological station
69.5682◦N, 138.9134◦W
Precipitation gauge for total precipitation (mm) and rate (mm h−1),
temperature (◦C)
the island. The full TSX dataset was first processed at the
DLR (German Aerospace Center). The preprocessing is de-
scribed in Schmitt et al. (2015) and includes the determi-
nation of the Kennaugh elements, their radiometric calibra-
tion and orthorectification. The images were georeferenced
in UTM (Universal Transverse Mercator) with a ground sam-
pling distance of 5 m. To reduce speckle noise, we used the
multi-scale multi-looking algorithm developed by Schmitt
(2016) and Schmitt et al. (2015). CPD and CCOH can di-
rectly be derived from the radiometric and geometrically cal-
ibrated Kennaugh elements. The Kennaugh matrix describes
the polarimetric information and allows us to differentiate the
physical scattering mechanisms (e.g., double bounce, volume
and surface scattering) affecting the signal, which in turn can
be linked to snow characteristics. The following Kennaugh
elements were used in the CPD equation:
φHH −φVV =6K7
−K3,(1)
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2168 J. Voglimacci-Stephanopoli et al.: Potential of X-band
Figure 3. Vegetation classes occurring in the Ice Creek catchment: (a) irregular and hummocky terrain observed in Lupine class (credit:
Michael Fritz), (b) low vegetation in well-drained areas (such as Dryas) (photo by the authors), and (c) shrub and wetland (such as Coltsfoot
class) (credit: Michael Fritz). (d) Vegetation unit’s distribution in the study area (from Obu et al., 2017) as defined initially by Smith et
al. (1989). The classes in grey are not included in the analysis.
where K7is the phase shift between HH and VV phase center
described as
K7=SHHS∗
VV(2)
and where K3is the scattering difference between surface to
double bounce:
K3= −SHH S∗
VV.(3)
To reduce the loss of coherence, a threshold was applied
to CPD pixels in which CCOH was less than 0.5, follow-
ing Leinss et al. (2014). Again, the Kennaugh elements were
used in the CCOH equation.
γVV,HH ·eiφ γ
CPD =hSHH ·S∗
VVi
ph|SHH|2i · h|SVV|2i
≈2sK2
3+K2
7
K2
0−K2
4
·ei6K7
K3(4)
Note that the notation of the Kennaugh matrix is labeled ac-
cording to Schmitt et al. (2015). A total of 32 pixels had a
CCOH less than 0.5, hence showing a random phase shift
between waves which is not optimal for CPD applications.
These pixels were therefore removed from the analysis. To
discard the potential effect of slope on crystal grains orienta-
tion, 5 pixels with a slope greater than 10◦were subsequently
extracted (3 in the Dryas class, 2 in the Lupine class). To as-
sess the temporal variability in the CPD signal, pixels were
divided by vegetation class for the period 2015–2019.
3.3.2 Linking snow depth to CPD
Implication of snow geometry
We focused on the SD variability between vegetation classes.
We also evaluated depth hoar fraction (DHF) given that King
et al. (2018) found that X-band backscattering is highly sen-
sitive to depth hoar grains. DHF is the depth hoar ratio within
the total depth of a snowpit. This allowed us to assess if
any discrepancies in SD retrieval can be linked to large grain
size. In addition, horizontal structures such as ice layers and
melt–freeze crusts were identified for the same purpose of
testing the SD retrieval capabilities in different stratigraphic
contexts. Dedieu et al. (2018) showed that the attenuation of
the SAR signal was caused by ice layers of 3 to 5 cm thick-
ness, but lingering uncertainties remain with regards to the
contribution of thinner ice lenses such as the ones found on
Qikiqtaruk/Herschel Island.
Topographic wetness index
SD retrieval is challenging because it is impacted by snow
surface properties. SD retrieval with CPD may be impacted
by the dielectric properties of the snow surface, since the
main backscatter signal is expected at the snow–ground in-
terface. High moisture content at the soil surface would po-
tentially improve the performance of SD retrieval given that
the penetration of the signal into the soil would be limited
by the high dielectric constant of the soil. The topographic
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J. Voglimacci-Stephanopoli et al.: Potential of X-band 2169
Table 2. TSX acquisition on Qikiqtaruk/Herschel Island. All orbits were used for linear regression with in situ snow measurements. Orbit 24
has a sufficient time series and was used to extract temporal evolution of CPD.
Relative Flight Polarization Incidence Observation period (yyyy.mm.dd) Number In situ
orbit direction angle (acquisition date used for linear regression) of scenes data
24 Descending HH, VV 31◦2014.12.26–2018.03.06
2019.04.17–2019.05.20
(2019.04.17, 2019.04.28)
104 2019.04.18
152 Ascending HH, VV 24◦2019.04.15–2019.05.18
(2019.04.26)
1 2019.04.26
115 Descending HH, VV 38◦2019.04.23–2019.05.15
(2019.04.23, 2019.05.04)
3 2019.04.22
2019.05.03
2019.05.04
wetness index (TWI) was chosen to analyze the variance be-
tween vegetation groups as a potential indicator of variabil-
ity. Given the high sensitivity of microwaves to wetness, the
high variability in TWI between each vegetation class will
lead to different responses in backscattering through changes
in the dielectric constant of the soil. The TWI was first de-
veloped by Beven and Kirkby (1979) within the runoff model
TOPMODEL using the following equation:
TWI =lna
tanβ,(5)
where tanβis the local slope, and ais the upslope area
per unit which is obtained with the upslope area (the cells
contributing to the runoff to the cells of interest, A) and
the contour length (L) following a=A/L. The upslope
area calculated is based on the D8 flow direction algorithm
(O’Callaghan and Mark, 1984), and the TWI values were
computed based on the ArcticDEM constructed from the
DigitalGlobe Constellation (Porter et al., 2018; resolution:
2 m). Each TWI value derived from the catchment was com-
bined to vegetation classes and CPD cells as described above.
Analysis
The Shapiro–Wilk test was used to test the normality of dis-
tributions for SD and TWI. Since TWI and SD distributions
did not respect a normal distribution, the variance in TWI and
SD between each group was tested with the non-parametric
test Welch’s ANOVA in conjunction with a post hoc Games–
Howell test. Welch’s ANOVA allows testing at first if the
differences between the groups are statistically significant,
while the post hoc Games–Howell test highlights the dif-
ferences between specific groups. It may be possible that
some groups show no statistically significant difference of
the means. For instance, we could expect no difference of
the means on the SD and TWI between the groups Coltsfoot
and Shrub as both vegetation units are located in areas well
suited for snow and water accumulation. We use the Games–
Howell test as it does not assume equal variances and sample
sizes (Games and Howell, 1976).
We evaluated the correlation between snow characteris-
tics and CPD using a linear regression analysis. The me-
dian value was extracted when more than one snow mea-
surement was found in the same TSX pixel (5 m). Thus, a
total of 371 pixels were used in the analysis (average num-
ber of snow measurements per pixel: 1.7). The median SDs
by pixels were grouped by vegetation classes and orbit. The
Durbin–Watson test and the Breusch–Pagan test were used to
assess autocorrelations and homoscedasticity of distribution
data. Significance for all tests was calculated with α=0.05.
4 Results
4.1 Snow distribution
4.1.1 Snow depth and depth hoar fraction along
transects
Measurements of SD from Transect #1 (Fig. 4) varied be-
tween 20 and 250 cm where the peak was measured at the
valley bottom. Further west, SD values decreased substan-
tially on the slope with values between 20 and 50 cm. The
highest DHFs along the transect were found on the west side
of the transect and on the slopes with an average of 0.76,
while an average of 0.39 was observed on the east side of the
catchment. Along Transect #2 (Fig. 4), snow cover was also
deeper at the bottom of the valley (from 120 up to 200cm)
and decreased significantly on slopes and higher elevation
areas (30 to 50 cm).
4.1.2 Snow characteristics by vegetation classes
Snow depth
The average SD within Ice Creek catchment was
47.4 cm ±9.6 cm. The range of variability was substan-
tial, with minimum value at 8.0 cm and a maximum at
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2170 J. Voglimacci-Stephanopoli et al.: Potential of X-band
Figure 4. Snow depth (SD) transects surveyed in the Ice Creek catchment. Transect #1 and Transect #2 show snow depth (solid line) and
altitude (m.a.s.l., dashed line) along transect. Mean depth hoar fractions (DHFs) are indicated along transects by proportional size. Points 1a,
1b and 2c contain one observation. See Fig. 2c for their location in the catchment.
212.0 cm. The standard deviation of the snow depth was
variable yet strong among all classes (Table 3). The largest
standard deviation measured was over Lupine (22.3cm or
57 % of the mean SD) followed by Coltsfoot (67.6 cm or
54 % of the mean SD). Coltsfoot was by far characterized by
a greater SD than any other class.
Despite the great deviation around the mean for each class,
Welch’s ANOVA (Table 4) shows that SD is significantly
different between all vegetation groups (pvalue =5−13).
Games–Howell post hoc revealed that the Coltsfoot SD mea-
surement is significantly different from the other classes, as
well as Dryas. The difference between Lupine and Shrub is
not significant.
Topographic wetness index
The average TWI was 6.1±1.6. The minimum and the max-
imum ranged between 2.5 and 14.7, while the average by
vegetation classes ranged between 7.4 and 5.5 with a weak
deviation around the means (Table 3). Coltsfoot showed the
highest TWI which is consistent with its location in the
valley (Fig. 3) and its vegetation group type, characterized
by hydrophilic vegetation. Welch’s ANOVA shows that the
wetness index extracted with TWI is significantly differ-
ent between all vegetation groups with a pvalue <0.001
(pvalue =6−14), which again is expected to lead to differ-
ent responses from TSX. The Games–Howell post hoc test
revealed that the difference in TWI is not significant between
Coltsfoot and Shrub units and between Dryas and Lupine
(Table 5).
Depth hoar fraction
The DHF of the snowpack was larger than 0.5 for all
vegetation classes. However, standard deviations of classes
with shallow snow such as Lupine and Dryas were greater,
whereas the standard deviation was lower than 0.1 for Colts-
foot for which the SD is the highest. At least one horizon-
tal structure (ice layer or melt–freeze crust layer) was found
in each snowpit. The average thickness of each ice or melt–
freeze crust layer was 1.6 cm ±0.7cm, and the cumulative
thickness average by snowpit was 4.5cm ±2.8cm. The max-
imum ice thickness (4 cm) was found at the station down-
stream, which would suggest that the sensitivity of the CPD
to ice layers should be generally lower than what was found
by Dedieu et al. (2018). The high stratification otherwise may
attenuate the signal.
4.2 TerraSAR-X results
4.2.1 Spatial and temporal evolution of CPD
Figure 5a and b show the averaged temporal evolution of
CPD and CCOH with descending orbit 24 (incidence an-
gle =31◦) for each vegetation class, as well as the confidence
interval (95 %). The period with the presence of snow was set
between mid-September and mid-May based on prior obser-
vations (Burn and Zhang, 2009; Stettner et al., 2018). Fig-
ure 5c shows the monthly average temperature and cumula-
tive monthly precipitation on Qikiqtaruk/Herschel Island.
A periodicity was observed with the CPD signal with, on
one side, the period of snow-free conditions in which the
signal oscillates around zero and, on the other side, the pe-
riod with snow in which the signal decreased over the sea-
son, suggesting an influence from the snowpack. For the
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J. Voglimacci-Stephanopoli et al.: Potential of X-band 2171
Table 3. Averaged SD and DHF for each of the vegetation classes.
Vegetation class SD and TWI Averaged Averaged DHF no. Averaged
no. of samples SD ±σ(cm) TWI ±σof samples DHF ±σ(%)
Coltsfoot 29 126.0 ±67.6 7.4 ±0.9 8 0.55 ±0.06
Dryas 146 31.8 ±14.1 5.9 ±1.2 16 0.62 ±0.31
Lupine 118 38.9 ±22.3 5.5 ±1.5 21 0.60 ±0.21
Shrub 28 44.1 ±20.6 6.8 ±1.3 6 0.51 ±0.18
Other units 50 69.3 ±47.2 6.8 ±2.4 7 0.58 ±0.20
Average in catchment 371 47.4 ±39.6 6.1 ±1.6 58 0.59 ±0.22
Table 4. Post hoc analysis with Games–Howell for snow depth in vegetation classes (non-parametric test). Each row presents the variance
between snow depth means from two different groups. All vegetation groups were tested on each other.
Snow depth
Class 1 Class 2 Mean difference Standard error pvalue
(cm) (cm)
Coltsfoot Dryas +94.2 12.60 0.001
Coltsfoot Lupine +87.1 12.71 0.001
Coltsfoot Shrub +81.9 13.14 0.001
Dryas Lupine −7.14 2.36 0.014
Dryas Shrub −12.34 4.07 0.015
Lupine Shrub −5.2 4.41 0.622
2014–2019 period, the mean CPD value during the snow
was −8.59◦. The means of each winter are ranging between
13.41◦(2014–2015) and −6.42◦(2017–2018). During the
snow-free condition, the average CPD over the same pe-
riod increased to −0.87◦(2014–2019). Maximum and min-
imum values during snow-free conditions ranged between
−0.44◦(2015) and −1.32◦(2015–2016). The decrease gen-
erally started in January, when the average air temperature
is at its coldest (−20 ◦C) except during the snow season of
2016–2017, when a warming occurred, increasing the aver-
age temperature by 5 ◦C for that year. The two classes with
taller vegetation type (Coltsfoot and Shrub) stood out during
the winters of 2014–2015 and 2016–2017. There the CPD
decreased to −60◦towards the end of the winter. The de-
crease in the CPD was therefore similar between the vegeta-
tion classes and is about −15◦.
Overall, the coherence stayed greater than the 0.5
threshold over the 2014–2019 period with an average of
0.71 ±0.11. The signal was lower during the snow-free pe-
riod when the average is 0.63±0.11. The coherence then
increased around 0.76 ±0.08 during the winter. Coltsfoot
and Shrub classes showed greater variation in the coher-
ence over the seasons and the years compared to Lupine
and Dryas classes. The average CCOH by vegetation classes
ranged between 0.69 ±0.07 (for Coltsfoot) and 0.72 ±0.07
(for Dryas).
4.2.2 Retrieving SD per vegetation class using CPD
The dataset from 2019 was used to perform a simple linear
regression analysis allowing us to assess whether there is a
statistically significant relationship between snow measure-
ments (layer depth of the depth hoar, wind slab, melt–freeze
crust and ice layers and mean density of each layer) and
CPD. No significant correlation was found other than SD,
or the samples contained fewer than 10 observations, which
resulted in elusive correlations (see Appendix A for more de-
tails). The best correlations between SD and CPD were found
with Lupine (orbit 24, descending, incidence angle 31◦) and
Coltsfoot (orbit 115, descending, incidence angle 38◦) (Ta-
ble 6).
The Coltsfoot and Shrub classes were characterized by
similar TWI mean values, as well as low TWI variance.
These two classes were combined to be compared with other
classes (named Coltsfoot +Shrub in Table 6). This group-
ing led to an improvement in the coefficient of determination
of 0.044, as well as a decrease in pvalue and standard de-
viation. Samples with a coefficient of determination greater
than 0.50 met the assumptions of homoscedasticity, as well
as the absence of autocorrelation, except for the sample lo-
cated in Coltsfoot +Shrub in orbit 115 and samples located
in Coltsfoot in ascending orbit 152 (See Table B1 for further
details). These results show clearly that CPD can be used to
retrieve SD, albeit not in all vegetation classes.
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2172 J. Voglimacci-Stephanopoli et al.: Potential of X-band
Table 5. Post hoc analysis with Games–Howell for the TWI (non-parametric test). Each row presents the variance between TWI means from
two different groups. All vegetation groups were tested on each other.
TWI
Group 1 Group 2 Mean difference Standard error pvalue
(wetness index) (cm)
Coltsfoot Dryas +1.56 0.20 0.001
Coltsfoot Lupine +1.97 0.22 0.001
Coltsfoot Shrub +0.63 0.30 0.147
Dryas Lupine +0.41 0.17 0.076
Dryas Shrub −0.93 0.26 0.003
Lupine Shrub −1.34 0.28 0.001
Figure 5. (a) Average CPD and (b) average CCOH by vegetation class with interval of confidence (95%) for orbit 24 (31◦, descending).
Values were extracted from the GPS dataset (see Fig. 2c), in which NColtsfoot =33, NDryas =140, NLupine =118 and NShrub =29. The
winter period (mid-September to mid-May) is shown in the shaded area. The window over which vegetation class information was extracted
is the same size as a TSX pixel (5×5 m). (c) Meteorological data from Qikiqtaruk/Herschel Island station (dataset from Environment Canada,
2021). The meteorological station is not equipped with a telemetry system, and since the island is inaccessible during the winter, the lack of
data during the winters of 2014–2015 and 2017–2018 was caused by a malfunction at the station. Air temperatures during these periods were
gap-filled using Komakuk Beach meteorological station and are shown by the dotted red line. Please refer to Appendix A for further details
on the method.
5 Discussion
5.1 Snow distribution on Qikiqtaruk/Herschel Island
5.1.1 Snow depth
On the study site, snow gets quickly redistributed across the
landscape by winds. Burn and Zhang (2009) showed that SD
distribution patterns were primarily driven by topography in
close vicinity to the Ice Creeks. Our observations (Fig. 4)
concur and expand on those from Burn and Zhang (2009) by
highlighting the effect of microtopography and of vegetation
in controlling SD. The SD was greater (>100 cm) in areas
characterized by shrubs and wetlands (Coltsfoot), which are
mainly associated with valley bottom locations. There is a
significant difference in SD between Coltsfoot and any other
class, which shows that snow gets blown away on high points
and slopes and accumulates in spatially constrained areas at
the valley bottom (Fig. 3d and c). By contrast, grass-type or
shallow vegetation, such as Dryas and Lupine, is found in
wind-exposed areas. Deeper snow was found over Lupine
compared to Dryas. The microtopography may play a role
in this difference, as the standard deviation of SD is greater
in the Lupine class. There is greater variability in SD be-
tween the troughs and the top of hummocks, as documented
by Wilcox et al. (2019). Thus, we can relate in this study that
the distribution of snow in the Ice Creek catchment is driven
primarily by vegetation and topography.
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J. Voglimacci-Stephanopoli et al.: Potential of X-band 2173
Table 6. R2,pvalue and standard deviation (R2±SD and pvalue ±SD in bold) results from linear regression analysis between SD and
CPD obtained by vegetation classes and by orbits. The confidence interval was measured using the “bootstrap with replacement” resampling
technique (Nbootstrap =1000). The standard deviation of R2and the pvalue obtained by the technique are indicated in the results whose
variance is explained to more than 50%.
Lupine Dryas Coltsfoot Coltsfoot +Shrub
Orbit 24 (31◦) 0.55 ±0.11 0.01 (0.59) 0.10 (0.55)∗0.07 (0.47)
(0.001 ±0.004)
Orbit 115 (38◦) 0.01 (0.51) 0.004 (0.64) 0.72 ±0.16 0.74 ±0.09
(0.00 ±0.01) (0.00 ±0.00)
Orbit 152 (24◦) 0.02 (0.44) 0.0 (0.82) 0.68 ±0.18∗0.001 (0.91)
(0.08 ±0.08)
∗Fewer than 10 observations.
5.1.2 Depth hoar fraction
We suggest that the DHF is strongly driven by microtopog-
raphy. During winter 2019, the DHF amounted to an average
of 59 % of the snowpack (n=58). There is a greater stan-
dard deviation in these measurements in vegetation classes
for which the average SD is lower (less than 40cm aver-
age depth) such as Lupine and Dryas. The effect of micro-
topography allows snow capture in hummock hollows early
in the season, and the thermal gradient from the ground to
the surface varies accordingly (King et al., 2018; Wilcox et
al., 2019). Depth hoar develops when a strong thermal gradi-
ent occurs between the ground and the snow surface. There
are two situations when a strong gradient occurs: (1) when
the SD is low and (2) when the soil is warm and the snow
surface is cold. Sturm and Holmgren (1994) have shown that
the depressions in tussocks or hummock are warmer than the
top. The thermal gradient found in this type of vegetation
class may therefore explain the large standard deviation of
DHF found in Lupine class. Thus, the soil wetness should be
higher in the hollows, but that effect might not be captured
by the TWI used in this paper as its spatial resolution relies
on a 2 m resolution DEM.
5.2 CPD spatiotemporal evolution and SD correlation
The high-resolution vegetation classification used in this pa-
per allowed us to show that CPD varies greatly according to
seasons and vegetation class (Fig. 5). Overall, the CPD signal
decreased during winter and increased rapidly during melt.
This concurs with observations from Leinss et al. (2014,
2016) made in Sodankylä, Finland. According to the model
developed by Leinss (2014, 2016), the strong CPD decrease
observed in the 2015 and 2017 winters over shrub areas could
be explained by fresh snow accumulation or dominance of
horizontal structures. However, the snow distribution analy-
sis showed that the Shrub class has shallow snow, making
it, SD-wise, significantly different from the Coltsfoot class,
meaning the result does not show that the measured CPD sig-
nal is entirely governed by the snowpack. The CPD evolution
over different vegetation classes is significantly different be-
tween two distinct groups: tall vegetation zones (Coltsfoot
and Shrub) and low vegetation zones (Lupine and Dryas).
The small decrease observed for Lupine and Dryas classes
during the snow season (Fig. 5) could indicate an influence
from the ground as the snow depth measured is less than
30 cm and highly stratified. However, the effect from inho-
mogeneities within the snowpack does not support this case
as the CCOH is greater than 0.5 for each pixel. Dryas is
characterized by the lowest TWI, which could lead to less
backscattering at the snow–ground interface and hence de-
crease the change in the snow season. High DHF in Lupine
vegetation class indicates a potential of higher TWI in the
tussock’s hollow, which might not be captured by the TWI.
Hence, the TWI variability within a TSX pixel at this vege-
tation class area could also explain the low decrease in CPD
observed in Fig. 5.
Although the snowpack was highly stratified, each ice
layer or melt–freeze crust was on average less than 2cm
thick, which is thinner than the ice layers in the snowpacks
studied by Dedieu et al. (2018). It may explain why the lin-
ear regression analysis of CPD shows the best results with
the total SD (i.e., less sensitive to small crusts), which has
never been observed before.
A high level of moisture in the ground will lead to ma-
jor dielectric contrast at the snow–soil interface, hence limit-
ing the penetration depth of the radar signal (Duguay et al.,
2015). Thus, the sensitivity of the signal to ground condi-
tions decreases. Duguay et al. (2015) also showed a strong
saturation of TSX signal in the areas with shrubs greater
than 50 cm. Warmer ground temperatures were previously
observed in permafrost (e.g., Myers-Smith and Hik, 2013;
Domine et al., 2016), which could delay the freezing process
and enhance the contrast at the snow–ground interface. In
the case of the study area, Myers-Smith et al. (2019) report
an increase in the canopy where the measured shrubs at the
bottom of the valley were more than a meter.
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2174 J. Voglimacci-Stephanopoli et al.: Potential of X-band
Figure 6. Correlation analysis between CPD and snow depth.
The TWI variance analysis shows that there is no signifi-
cant variance between Coltsfoot and Shrub classes and be-
tween Lupine and Dryas classes, which could explain the
strong decrease in the signal observed in mid-winter (Fig. 5).
A high TWI indicates a high water accumulation potential
and hence a higher saturation of the soil. In the microwave
range, soil saturation increases the dielectric properties of the
soil. The sensitivity of the X-band radar signal is then higher,
which allows the interface between the snowpack and the
ground to be well discriminated. Thus, CPD captures snow
accumulation well across winter in areas with a higher po-
tential of soil moisture, while soils with a lower potential of
moisture are likely to contribute to the CPD signal and thus
reduce the correlation between snow depth and CPD signal.
We suggest the increase in the R2depends on the soil
moisture because there is less contribution from the ground
to the backscatter signal. A higher incidence angle (>30◦)
improves the results, agreeing with Leinss et al. (2014). The
use of the TWI is promising for the snow-SAR dynamics as
it is easy to compute and relies on topographic datasets that
are now widely available for the entire arctic. Furthermore,
no correlation was found between retrieval performance of
SD and DHF, suggesting that the poor performance over
the Dryas class is explained by soil contributions. The rela-
tively conclusive results for the Lupine class at orbit 24 show
an inverse correlation (Fig. 6) which contradicts Leinss et
al. (2014). We hypothesize that the tussock depressions are
preferential areas for the formation of depth hoar, caused by
the effect of microtopography. Thus, vertical structures are
dominant in the snowpack, which could explain an increase
in vertical structure in which the snowpack is deeper in this
vegetation class. Further analysis should be done on the soil
moisture and on the effect of the depth hoar distribution to
better capture the wetness of hummocky areas and how it
can improve retrievals of SD.
6 Conclusions
This study was the first to investigate the potential of co-
polar phase difference (CPD) derived from TerraSAR-X data
in combination with snowpit characterization over Qikiq-
taruk/Herschel Island. We were able to find a variability
in SD and TWI depending on vegetation classes extracted
from a high-resolution map of vegetation cover. Classifying
snowpits by vegetation classes on Qikiqtaruk/Herschel Is-
land shows respectable results, helping to demonstrate the ef-
fect of topography and hence the moisture rate of the ground
on the CPD signal. The 2019 dataset shows a high heteroge-
nous snowpack with different ice layers and with a DHF rep-
resenting on average more than half of the snowpack.
Despite this complex snowpack, we demonstrate a corre-
lation between the CPD and the SD when certain conditions
are met. With a high incidence angle (>30◦) and a high TWI
(>7.0), a significant correlation between SD and CPD can be
found with an R2of 0.72. CPD cannot be used to extract the
fresh snow in an arctic context as the penetration of the elec-
tromagnetic wave tends to go through the entire snowpack.
The in situ data used for the present study do not cover the
entire winter on Qikiqtaruk/Herschel Island, which brings
uncertainties on snow depth characterization with CPD. The
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J. Voglimacci-Stephanopoli et al.: Potential of X-band 2175
lack of consistent stratigraphy measurement over the winter
is still a major limit in snow studies. Consistent stratigraphy
measurement over the winter would improve the understand-
ing of the snowpack metamorphism regime.
The maritime climate of Qikiqtaruk/Herschel Island may
advance the snowmelt period and provoke a shift to a wet
metamorphism regime in the snowpack. To address the re-
maining question about the specific climate of the study site,
we compared our statistics to the classification proposed by
Royer et al. (2021) and observed a good fit with the herba-
ceous and low shrub tundra snow class (Table B2). The snow
characteristics observed over the Ice Creek catchment are
consistent with the literature.
The standard deviations of the mean snow depth from
our study site and from the Royer et al. (2021) classifica-
tion are both greater than 80 %. The local topography inher-
ited from the last glaciation (Late Wisconsin) is specific to
Qikiqtaruk/Herschel Island and could explain higher snow
depth and therefore higher SWE (snow water equivalent) and
density. The maritime effect observed on Qikiqtaruk (Cray
and Pollard, 2015) could also explain the warmer mean tem-
perature during winter. All study sites used in the Royer et
al. (2021) classification are in the east of Canada. Further
studies and datasets from the western part of Canada would
greatly improve the snow classification.
A focus of future studies could be the threshold sensitivity
to TWI and the incidence angle of snow depth retrievals to
map snow depth in such environments and to evaluate the
potential of using interpolation tools to cover the gaps in
SD information over vegetation types. SD variability within
a TSX pixel should be studied further, especially in hum-
mocky areas where the highest variability was found, which
could suggest a variability in the TWI as well. Statistical ap-
proaches using the coefficient of variation of snow depths
(CVsd), as suggested by Winstral and Marks (2014) and Lis-
ton (2004), could be an interesting avenue in the develop-
ment of a representative mapping of the terrain. Meloche et
al. (2022) demonstrated recently the effectiveness of CVsd to
improve passive microwave SWE retrievals in a similar envi-
ronment to that found on Herschel Island (i.e., arctic snow-
pack with tundra vegetation type).
Appendix A: Meteorological data gap-filling
The meteorological station is not equipped with a telemetry
system, and since the island is inaccessible during the win-
ter, the lack of data during the winters of 2014–2015 and
2017–2018 was caused by a malfunction at the station. Air
temperatures during these periods were gap-filled using Ko-
makuk Beach meteorological station as performed by Burn
and Zhang (2009). The following equation was applied on
the Komakuk Beach dataset:
Th=0.97Tk+0.75,(A1)
Figure A1. Result from linear regression between air temperature
measured on Herschel Island and predicted value using the Burn
and Zhang (2009) equation.
Figure A2. Comparison during 1994–2022 of air temperature mea-
sured on Herschel Island and predicted value using the Burn and
Zhang (2009) equation.
where This the monthly mean air temperature at Herschel
Island and Tkat Komakuk Beach. Predicted values showed
good correlation (R2: 0.93; pvalue =<0.001) with RMSE
of 3.32 ◦C (Fig. A1).
Figure A2 shows a visual comparison between the air tem-
perature predicted and measured along the time series. Un-
fortunately, no precipitation datasets were available at Ko-
makuk Beach station.
Appendix B: Complementary results
Table B1 shows the complementary results retrieved during
the linear regression analysis between CPD and every snow
variable measured on the field. Results with R2greater than
or equal to 0.5 are shown in bold. The samples may vary
when a measurement was not possible during the field cam-
paign.
Each variable describes a characteristic sampled in the
snowpit.
–H_tot: snow depth (cm)
–H_ws: wind slab height (cm)
–H_dh: depth hoar height (cm)
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2176 J. Voglimacci-Stephanopoli et al.: Potential of X-band
Table B1. Complementary results retrieved during the linear regression analysis. The standard deviation with bootstrap is not show as the results with R2greater than 0.5 have samples
with fewer than eight observations.
Orbit 115
ratio_dh ice
cumul
cumul
crust
density
moy
density
dh
ratio_ws h_tot_mf h_tot_
ice
h_dh h_ws h_tot ssa_ws
R2(p) 0.0 (0.86) 0.16
(0.15)
0.17
(0.13)
0.06
(0.42)
0.03
(0.60)
0.00
(0.92)
0.00
(0.93)
0.07
(0.36)
0.22
(0.08)
0.09
(0.28)
0.31
(0.03)
0.05 (0.46)
Sample 15 15 15 14 13 15 15 15 15 15 15 13
Orbit 152
ssa_
ws
ice_
cumul
cumul
tot
h_
tot
h_ws h_dh h_tot
mf
h_tot
ice
ratio_ws density
dh
density
moy
cumul
crust
ratio
dh
R2(p) 0.04 (0.7) 0.46
(0.06)
0.64
(0.02)
0.45
(0.07)
0.48
(0.06)
0.02
(0.72)
0.38
(0.19)
0.19
(0.28)
0.09
(0.56)
0.0 (0.98) 0.51
(0.05)
0.34 (0.17) 0.27 (0.19)
Sample 6 8 8 8 8 8 6 8 6 8 8 7 8
Orbit 24
cumul
crust
ratio
ws
density
moy
density
dh
ratio
dh
ssa
ws
h_tot_ice h_dh h_ws h_tot ice_
cumul
h_tot_mf cumul_
tot
R2(p) 0.02
(0.64)
0.02 (0.6) 0.01 (0.7) 0.09
(0.33)
0.0 (0.83) 0.01
(0.76)
0.03
(0.59)
0.04
(0.49)
0.03
(0.59)
0.0 (0.87) 0.2 (0.14) 0.03 (0.55) 0.08 (0.33)
Sample 13 14 14 13 14 12 12 14 14 14 12 13 14
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J. Voglimacci-Stephanopoli et al.: Potential of X-band 2177
Table B2. Comparison of snow characteristics with the Royer et al. (2021) classification. Mean temperature was extracted from the 1974–
2019 meteorological station from Qikiqtaruk/Herschel Island and for the winter season (December to March as defined by Royer et al.,
2021).
Latitude Range Mean temperature SWE ±SD SD ±SD Density ±SD
(◦N) (◦C) (mm) (cm) (kg m−3)
Qikiqtaruk/Herschel Island 68–69 −22.1 142.6 ±99.1 47.4 ±39.6 343.8 ±73.7
Herbaceous and low shrub tundra 58–74 −23.6 132.9 ±57.6 43.1 ±35.2 315.3 ±49.1
snow (from Royer et al. (2021)
Figure B1. Linear regression between CPD and snow depth on de-
scending orbit (orbits 24 and 115) without TWI threshold.
–H_tot_ice: snow height to the first ice layer observed
in the snowpit (cm) and ice layer thickness greater than
2 cm
–H_tot_mf: snow height to the first melt–freeze crust
(cm)
–Density_moy: average snow density (kgm−3)
–Density_dh: average density for the depth hoar layer
(kg m−3)
–Ssa_ws: average snow surface area measured in the
wind slab layer
–Ratio_df: depth hoar fraction in the snowpit (%)
–Cumul_tot: cumulative thickness of horizontal layers
(melt–freeze crust, ice lens, in cm)
–Ice_cumul: cumulative thickness of ice lens (cm)
–Cumul_crust: cumulative thickness of melt–freeze crust
(cm)
–Ratio_ws: wind slab ratio in the snowpit (%).
Figure B2. Topographic wetness index (TWI) map compared to
vegetation units located on Qikiqtaruk/Herschel Island.
https://doi.org/10.5194/tc-16-2163-2022 The Cryosphere, 16, 2163–2181, 2022

2178 J. Voglimacci-Stephanopoli et al.: Potential of X-band
Appendix C: Statistical analysis assumptions and results
C1 Homoscedasticity
Linear least squares regression assumes that the residuals
come from a population in which the variance is constant.
When heteroscedasticity is present, the result is therefore un-
reliable. The Breusch–Pagan statistical test evaluates the as-
sumption of homoscedasticity, i.e., the consistency of the er-
ror variance in a linear regression model.
Table C1. Statistical test results for CPD and snow depth correlation analysis. Each model represents a divided sample in function of
vegetation class and TSX orbit. Results with an R2greater than 0.5 are shown. The Durbin–Watson test (DW) and the Breusch–Pagan test
(LMS, Lagrange multiplier statistic) were selected to assess the autocorrelation for the first and the homoscedasticity for the latter.
Vegetation class Orbit Sample R2Adjusted R2DW LMS LMS pvalue
Lupine 24 17 0.55 0.52 1.80 2.51 0.11
Coltsfoot 115 16 0.70 0.68 2.01 1.65 0.19
Coltsfoot +Shrub 115 19 0.75 0.73 0.81 3.48 0.06
Coltsfoot 152 5 0.68 0.58 1.37 2.03 0.15
The assumptions are as follows.
–Null hypothesis (H0). Homoscedasticity is present.
–Alternative hypothesis (Ha). Homoscedasticity is not
present (heteroscedasticity is present).
If the pvalue of the Lagrange multiplier statistic (LMS)
is greater than 0.05, the probability of homoscedasticity is
greater than 5 %. The null hypothesis is therefore retained.
In the opposite case (pvalue <0.05), the probability of ho-
moscedasticity is less than 5 %. The alternative hypothesis is
then adopted.
C2 Autocorrelation
The Durbin–Watson test (DW) is used to test the autocor-
relation of residuals in linear regression models. It assesses
whether the residuals are independent.
The assumptions are as follows.
–H0. There is no correlation between the residuals.
–Ha. The residuals are autocorrelated.
The results are expected between 0 and 4. Values between
1.5 and 2.5 indicate no autocorrelation. Results near 0 show
positive autocorrelation, while results near 4 show negative
autocorrelation.
The Cryosphere, 16, 2163–2181, 2022 https://doi.org/10.5194/tc-16-2163-2022

J. Voglimacci-Stephanopoli et al.: Potential of X-band 2179
Code and data availability. Data and code are made available upon
request to the corresponding author.
Author contributions. JVS performed this study as part of her mas-
ter thesis project, co-supervised by AL and HL. JVS, SS, AL, AW
and HL designed the methodology. JVS wrote the code and per-
formed the field measurements and the analysis. The original idea
and method were developed by JVS, AL and HL. AW and AS per-
formed the SAR preprocessing, AlR, AcR, AW, AL, AS and JPD
supported the SAR analysis. The manuscript was written by JVS
and edited by all the co-authors.
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. The authors acknowledge the use of the Arc-
ticDEM (© 2019 Polar Geospatial Center) and the TerraSAR-X data
(© DLR 2019). We would like to thank the Aurora Research Insti-
tute, the Yukon Territorial Government and Yukon Parks (Herschel
Island Qikiqtaruk Territorial Park) for administrational and logis-
tical support, as well as the Inuvialuit people for the opportunity
to conduct research on their traditional lands. Joëlle Voglimacci-
Stephanopoli would like to thank Vincent Sasseville for the field-
work support and Silvan Leinss for the many exchanges and dis-
cussions on the CPD method. The authors would like to thank
Georg Fisher, the reviewers and the editor, Carrie Vuyovich, for
their help in improving this paper.
Financial support. This research has been supported by the Hori-
zon 2020 (grant no. Nunataryuk (773421)), Mitacs Globalink and
Polar Knowledge Canada.
Review statement. This paper was edited by Carrie Vuyovich and
reviewed by two anonymous referees.
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