Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice
classification and estimates of sea ice drift and deformation since it first
became widely available in the 1990s. Here, we examine the potential to
distinguish surface features created by sea ice deformation using ice type
classification of SAR data. Also, we investigate the cross-platform
transferability between training sets derived from Sentinel-1 Extra Wide (S1
EW) and RADARSAT-2 (RS2) ScanSAR Wide A (SCWA) and fine quad-polarimetric (FQ)
data, as the same radiometrically calibrated backscatter coefficients are
expected from the two C-band sensors. We use a novel sea ice classification
method developed based on Arctic-wide S1 EW training, which considers
per-ice-type incident angle (IA) dependency of backscatter intensity. This
study focuses on the region near Fram Strait north of Svalbard to utilize
expert knowledge of ice conditions during the Norwegian young sea ICE
(N-ICE2015) expedition. Manually drawn polygons of different ice types for S1
EW, RS2 SCWA and RS2 FQ data are used to retrain the classifier. Different
training sets yield similar classification results and IA slopes, with the
exception of leads with calm open water, nilas or newly formed ice (the
“leads” class). This is caused by different noise floor configurations of S1
and RS2 data, which interact differently with leads, necessitating
dataset-specific retraining for this class. SAR scenes are then classified
based on the classifier retrained for each dataset, with the classification
scheme altered to separate level from deformed ice to enable direct comparison
with independently derived sea ice deformation maps. The comparisons show that
the classification of C-band SAR can be used to distinguish areas of ice
divergence occupied by leads, young ice and level first-year ice
(LFYI). However, it has limited capacity in delineating areas of ice
deformation due to ambiguities between ice types with higher backscatter
intensities. This study provides reference to future studies seeking
cross-platform application of training sets so they are fully utilized, and we
expect further development of the classifier and the inclusion of other SAR
datasets to enable image-classification-based ice deformation detection using
only satellite SAR.
Introduction
The general thinning of Arctic sea ice in recent decades has led to reduced
internal strength , which together with increased wind
forcing (as indicated by atmospheric reanalyses) has caused accelerated ice
drift speed and hence increased ice deformation
. As surface features created by ice
deformation, e.g., lead edges, rafted ice and pressure ridges, are the primary
snow-trapping sea ice surface types , the shifting regime of
Arctic sea ice deformation will directly impact snow accumulation and thus
affect heat fluxes through ice, thereby influencing winter sea ice growth
. Also, ice deformation influences ice surface and bottom
roughness and thus affects the transfer of momentum between the atmosphere and
the ocean , preconditions the ice layer for more
lateral melt , and increases ice drift
speed due to reduced floe sizes following ice breakups
. Additionally, ice
deformation has a significant impact on ice primary productivity, as it
provides a sheltered growth environment for ice flora and fauna in deformed ice
and favorable light
conditions under lead ice , creating biological
hotspots. Reliable examination of sea ice deformation is therefore crucial for
the evaluation and modeling of Arctic sea ice changes.
Sea ice deformation is traditionally estimated from spatial derivatives of ice
motion using in situ, airborne and spaceborne data
e.g.,. However,
consistent Arctic-wide monitoring of sea ice deformation can only be achieved
through satellite remote sensing. Wide-swath synthetic aperture radar (SAR)
data, e.g., RADARSAT-1 (RS1, 1995 to 2013), RADARSAT-2 (RS2, 2008 to present),
the recently launched RADARSAT Constellation Mission (RCM, 2019 to present)
and Sentinel-1A/B (S1, 2014 to present) data, have been used to generate
large-scale ice drift and deformation fields
e.g.,, benefiting
from large spatial coverage and good temporal resolution. For example, the
RADARSAT Geophysical Processor System (RGPS; ) generates the
most widely used sea ice motion and deformation dataset using
cross-correlation-based ice tracking on RS1 data for the western Arctic from 1997
to 2008 . Data from other types of satellite sensors, e.g.,
visible, infrared, and microwave radiometers and radar scatterometers, can
also be used to generate ice drift fields with coarser resolution through
feature tracking algorithms, for example those used by the National Snow and
Ice Data Center (NSIDC; ) and the Ocean and Sea Ice
Satellite Application Facility (OSI SAF;
).
In addition to sea ice deformation retrieval from ice motion, the potential of
separating areas of deformed and level ice as classes in wide-swath SAR image
classification is valuable, as the automated or semi-automated nature of such
methods permits fast processing of data with large spatial and temporal
coverage. Various supervised and unsupervised SAR sea ice classification
methods have been developed, as reviewed by, for example,
. Under the same radar parameters, the intensity of SAR
backscatter on sea ice is the combined signal from several scattering
mechanisms, of which surface scattering is the dominant factor
. Surface scattering is controlled by surface
parameters including roughness and dielectric properties. Therefore, the
separation of level and deformed ice, which have distinctly different surface
roughness levels, is theoretically achievable through the classification of
SAR backscatter intensities. Studies have isolated deformed ice using the
classification of airborne and fully polarimetric high-resolution satellite
SAR data e.g., and linked sea ice
roughness to wide-swath SAR backscatter intensities through correlation
analyses, thus mapping sea ice deformation
. estimated the
degree of ice ridging from the classification of texture features from
segmented RS2 ScanSAR Wide A (SCWA) data. However, no study has specifically
targeted separating level from deformed ice from the classification of
wide-swath SAR backscatter intensities.
This study investigates the feasibility of such a task with an ultimate goal
of Arctic-wide monitoring of sea ice deformation. In this context, a
classification method consistently applicable to multiple satellite platforms
is desirable to utilize their respective advantages. This study is a first
step towards this goal, which explores how the cross-platform application of a
SAR classifier between S1 and RS2 data is influenced by their comparative SAR
characteristics. This is achieved by examining the transferability of
classification training sets between these two sensors. S1 and RS2 are widely
used for sea ice monitoring, and their wide-swath acquisition modes provide
extensive spatial and temporal coverage for Arctic-wide sea ice analyses
. This transfer learning process is theoretically
feasible as the two sensors are expected to yield the same radiometrically
calibrated normalized radar cross section (backscatter coefficient, or
σ0) values for the same surface, as they operate at the same center
frequency (5.405 GHz). Studies have confirmed that they yield
consistent ocean feature extraction results , and other
studies have used the fusion of coincident SAR data in different bands in sea
ice classification . However, S1 and RS2
differ in various other SAR parameters (Table , more details
discussed in Sect. ), thus requiring examination of
between-sensor differences in sea ice backscatter, based on which the
transferability of training can be assessed.
Parameters of satellite data used in this study. SGF: SAR georeferenced fine; SLC: single look complex; GRD: ground range multi-look detected; Rng: range direction; Az: azimuth direction; NESZ: noise-equivalent sigma zero. Spatial resolution of S2 and L8 data is for bands 2, 3 and 4.
ParametersRS2 SCWARS2 FQS1 EWS2L8PolarizationDual (HH + HV)Quad (HH + VV + HV + VH)Dual (HH + HV)––Acquisition modeSCWAFQEW––Product typeSGFSLCGRDLevel-2A BOA reflectanceLevel-2 surface reflectanceNominal pixel spacing [Rng × Az] (m)50×504.7×5.140×40––Nominal resolution [Rng × Az] (m)163–73×78–1065.2×7.693×8710×1030×30Nominal scene size [Rng × Az] (km)500×50025×25250×250100×100185×180NESZ range (dB, approximate)-25 to -30-31 to -39-23 to -34––IA range20–49∘18–49∘20–46∘––Number of looks [Rng × Az]4×21×16×2––DateNumber of scenes 8 January 20151–1––10 January 20151–1––12 January 20151–1––21 January 20151–1––26 January 2015111––5 March 2015111––19 March 2015111––17 April 20151–1–119 April 2015–31––21 April 20151–12123 April 2015–21––28 April 2015111–130 April 20151–1–226 April 20191–16–30 April 20191–131
The SAR classifier used for transfer learning is a newly developed sea ice
classifier based on Arctic-wide S1 EW training . This
classifier provides a novel solution to the effect of surface-type-dependent
change of SAR backscatter intensity with incident angle (IA) and is hereafter
referred to as the Gaussian incident angle (GIA) classifier. Accordingly, the
examination of the effect of sensor differences on the transfer of training
will focus on different IA dependencies, mainly involving IA slopes, of ice
surfaces. Based on this examination, an optimal way of applying the classifier
to S1 and RS2 data in our study area can be derived. All datasets are then
classified, where the classification scheme is altered to separate level from
deformed ice, allowing for direct comparison with areas of ice convergence and
divergence produced by tracking drifting ice parcels.
In summary, this study has two objectives: (1) to examine IA dependency of
different ice types in S1 and RS2 data and evaluate the cross-platform
transferability of training in the application of the GIA classifier and (2) to
test to what extent sea ice classification based only on HH and HV backscatter
intensities of C-band SAR data can be used to separate level from deformed
ice.
Materials and methodsMaterials
The materials and methods of this study are summarized in a flowchart in
Fig. . SAR data used in this study are mainly wide-swath RS2 and S1
data, i.e., RS2 SCWA and S1 EW (hereafter referred to as S1) data. The spatial
resolution of these datasets is too coarse to detect individual leads and
ridges less than approximately 100 m wide, but it is sufficient for
isolating leads several hundred meters wide and separating areas dominated by
deformed or level ice . RS2 FQ data are
also included to investigate the use of its higher spatial resolution to
delineate ice deformation features. The analysis of SAR data focuses on the HH
and HV channels, based on which the GIA classifier is trained.
Flowchart of materials and methods used in this study.
Several datasets are used as reference to the derivation of training and
validation polygons. Firstly, Sentinel-2 (S2, Level-2A bottom-of-atmosphere
(BOA) reflectance) and Landsat-8 (L8, Level-2 atmospherically corrected
surface reflectance) data with less than 50 % nominal cloud coverage
are collected to provide optical reference. Secondly, back-tracking of sea ice
source regions from ice drift fields derived from passive microwave data
, as well as in situ observations from the Norwegian young
sea ICE 2015 (N-ICE2015) campaign, provides knowledge of the general
distribution of ice types, especially first-year ice (FYI) and multi-year ice
(MYI). Finally, the global sea ice type product from OSI SAF (10 km
resolution), which provides separation between FYI and MYI using passive and
active microwave scatterometers, is used as an additional reference
. No in situ data collected during N-ICE2015 are usable as a
reference due to minimal spatial overlap with satellite data.
This study focuses on sea ice surrounding the N-ICE2015 expedition north of
Svalbard at the western end of the Transpolar Drift
to utilize expert knowledge from co-authors
who participated in the campaign. Data collected during N-ICE2015 are used as
the primary dataset, while SAR scenes with optical imagery overlap from 2016
to 2019 are also used to expand the dataset for retraining and validation. In
situ observations show that sea ice investigated during N-ICE2015 primarily
consisted of a mixture of FYI and second-year ice (SYI), while other thinner
ice types including nilas and young ice also existed. SYI belongs to the MYI
category in SAR-based sea ice classification and was the only type of MYI
observed in the N-ICE2015 campaign. Frost flower coverage of young ice was
observed for the entire N-ICE2015 drift
. This
study mainly uses data covering the core of winter after freeze-up and before
melt onset, i.e., January to April, as defined by based on
time series evolution of C-band backscatter coefficients. This is to avoid the
influence of wet snow on radar backscatter, which reduces radar penetration
depth and result in dominant backscatter from the air–snow or dry–wet snow
interface e.g.,.
Specific selection procedures of satellite data are specified in
Sect. , and the final list is summarized in Table
(; (MDA), 2016, 2018). Image boundaries of RS2 SCWA
and S1 scenes are shown in Fig. .
Extents and overlaps of S1 and RS2 SCWA scenes used in this study.
The GIA classifier is used for sea ice classification to utilize its
class-specific correction of IA dependency of SAR backscatter. This phenomenon
is traditionally treated as an image property and remedied by a global
correction based on the approximate linear decrease rate in the log domain
. However, per-class IA correction is found
to be necessary as the decrease rates (slopes of backscatter intensities
vs. IA, i.e., IA slopes) are different for different surfaces
. The GIA classifier
directly incorporates IA dependency of classes into a Bayesian classifier
, which is achieved through the replacement of the
constant mean vector of the Gaussian probability density function with a
linearly variable mean. This shows significantly improved performance compared
to classification with global IA correction. The terminology of sea ice
classes used in this study is defined in Sect. .
Data selection and processing
The following selection and masking processes are conducted on RS2 scenes.
The original GIA classifier has limited separating capacity between open
water and sea ice, as IA slopes and backscatter intensities of open water in
different sea states vary significantly, creating ambiguities with ice
surfaces . To mitigate this known issue and also to serve our
purpose of separating level from deformed ice within pack ice, daily sea ice
concentration data generated from SSMI/S (Special Sensor Microwave
Imager/Sounder, DMSP F18 satellite) from NSIDC (National Snow & Ice Data
Center, ) are used to filter out areas in RS2 SCWA scenes with
ice concentration values lower than 87 %. This is an empirical
threshold derived from visual inspection for the removal of large, contiguous
open-water areas and the marginal ice zone.
The RS2 scenes are further selected based on the availability of
overlapping S1 data and optical imagery. For 2015, RS2 scenes with at least
one overlapping S1 scene are retained for analysis. From 2016 to 2019, RS2
scenes with at least one S1 and one optical (S2 or L8) scene with significant
near-coincident overlap (overlapping area ≥30% of the masked
RS2 SCWA scene) are selected to ensure optical reference, resulting in only two
RS2 SCWA scenes investigated in 2019 (Table ). The maximum
temporal separation between overlapping RS2 data and S1, S2 and L8 data is 1,
5.3 and 8 h, respectively. These selection parameters are empirically
determined according to data availability. The overlap analysis is conducted
through a script in Google Earth Engine , from which
overlapping S1 scene IDs are derived and used for downloading using the
Sentinelsat Python API , while RGB composites of S2 and L8
data (bands 4, 3 and 2) are directly generated and downloaded.
Pre-processing of RS2 and S1 data is performed using the SNAP software
package . All scenes are radiometrically
corrected and calibrated to σ0 values, so that RS2 and S1 backscatter
is directly comparable. For RS2 FQ scenes which are single look, 2×2
multi-looking is performed to reduce speckle and reach a similar number of
effective looks compared to the wide-swath scenes, while considering the
preservation of linear features of interest, especially leads. Then, speckle
filtering (boxcar, 3×3) is applied on all SAR scenes, and backscatter
intensities are converted to decibels.
Typically several S1 scenes overlap with each RS SCWA scene, but the one with
the largest overlapping area is selected to avoid redundancy. The first
sub-swath of each S1 scene is removed for more reliable classification
results, as radiometric variations are especially pronounced between the first
sub-swath and others e.g.,. The identical masking process
to remove pixels with low ice concentration is conducted on S1 scenes. No
further processing is conducted on S2 and L8 RGB composites, as they are used
qualitatively as reference data. Subsequent data analyses are performed using
MATLAB and Python .
Cross-platform application of the GIA classifier
The original GIA classifier aims for Arctic-wide
applicability and thus has been trained on S1 scenes spread across the Arctic
region from 2015 to 2019, in “winter and early spring” months when ice is
under freezing conditions . Therefore, the class-specific IA
dependencies in the classifier are produced from a generalized training set,
and in theory encompass all IA dependencies of these classes within the
spatial and temporal domain of that study. This study focuses on the
transferability of training between S1 and RS2 data, for which we do not
target Arctic-wide generalization. Instead, we focus on the N-ICE2015 region
during boreal winter 2015 to provide confidence to the validity of training
and validation with input of expert knowledge. Thus, we investigate the
applicability of local training sets separately derived from S1 and RS2 scenes
to both platforms, through which between-sensor differences in sea ice
backscatter can be assessed.
For this purpose, reference polygons are derived for the SAR datasets based on
visual examination of the scenes for retraining and validation. Polygons in
the overlapping areas of corresponding RS2 SCWA and S1 scenes are used for
both sensors, with manual adjustments accounting for their time difference
(change of ice types in the polygons across different scenes due to sea ice
drift). This study uses the ice classes in the original GIA classifier
excluding open water, for reasons mentioned above. These classes are leads,
young ice, level FYI (LFYI), deformed FYI (DFYI) and MYI (explained in more
detail in Sect. ). The criteria for selecting the polygons are as
follows.
Size. Polygons of the same size within each dataset are used to standardize the number of pixels in each class: 300 m by 300 m for RS2 SCWA and S1 scenes and 30 m by 30 m for RS2 FQ scenes, which are approximately 3 times their effective pixel sizes given their number of looks. The choice of this size takes into account typical widths of linear features, mainly leads and young ice.
Distribution. A minimum distance of 30 pixels is kept between polygons to minimize spatial dependence between polygons of the same class. For each class, the polygons are distributed evenly across the entire range of IAs (where possible).
Numbers. In total 100 polygons are delineated for each scene, and the same number (20) of polygons are selected for each class (where possible). As the five classes are usually uneven in spatial coverage, probability sampling, e.g., spatially random or systematic sampling, is not used to avoid underrepresentation of scarcely occurring classes.
For classes usually with small spatial coverage (leads and young ice) where
criteria 2 and 3 cannot both be satisfied (i.e., small spatial coverage leading
to inevitable selection of polygons in small areas), criterion 3 is given
priority to yield more polygons. Examples of reference polygons are shown in
Fig. . Polygons shown in the SAR scenes are used in the
analysis, while those in the optical scenes are manually shifted polygons
which account for time differences from the SAR scenes and are therefore only
for illustration purposes. Polygons in each scene are randomly split in half,
with the number in each class also split in half, for retraining and
validation. Training polygons for all S1 scenes are merged into an S1 training
set, and those for RS2 SCWA scenes and FQ scenes are separately merged into
RS2 SCWA and RS2 FQ training sets. Thus, for each dataset (S1, RS2 SCWA or RS2
FQ), retraining incorporates IA dependencies in all scenes. This is especially
essential for RS2 FQ scenes where their small extent (spatial coverage:
25 km by 25 km; IA range: 1.24 to
1.94∘) necessitates the combined investigation of IA dependencies
across multiple scenes.
(a) Examples of reference polygons in different ice types in overlapping parts of SAR (false-color RGB composites: R:HV, G:HH, B:HH) and optical scenes and (b) examples of zoomed-in subsets for each class.
To evaluate cross-dataset transferability of training, the original GIA
classifier is firstly applied to all datasets, providing a baseline for
further analyses. Secondly, the regional training sets for S1, RS2 SCWA and
RS2 FQ scenes are used to retrain the classifier and classify each
dataset. Based on the evaluation of the results using validation polygons, the
transferability of training is assessed, and the classification maps derived
using the optimal classifier are selected to separate level from deformed ice.
Adaptation to separate level from deformed ice
To allow for direct comparison with ice deformation maps (see
Sect. ), the five-class classification scheme of the GIA classifier
is altered to a three-class one: deformed ice, level ice and others, i.e., the
classification of “deformation states.” The correspondence between the two
schemes is as follows.
DFYI and MYI: deformed ice. An ideal classification of level and deformed ice requires separation between deformation states in every ice age category (mainly young ice, FYI and MYI). However, the GIA classifier does this only for FYI, such is common practice of SAR-based sea ice classification (e.g., see review by ). Specifically, the DFYI class refers to rough FYI with stronger backscatter due to either ridging or the presence of other rough surface features, e.g., pancake ice.
MYI surface is usually rougher than younger ice types due to more accumulated
deformation. The separation of MYI deformation states using SAR-based
classification is challenging due to stronger presence of volume scattering
from the desalinated and porous upper ice layer (increasing backscatter from
level ice) as well as weathering of deformation features (decreasing
backscatter from deformed ice;
). As this study uses C-band HH
and HV intensities only, this volume scattering component in MYI is
significant, while the capacity of fully polarimetric data to distinguish
between surface and volume scattering is not available
. For the same reason, the contribution from volume
scattering and ice deformation to strong SAR backscatter (characteristic of
both DFYI and MYI) cannot be perfectly separated, creating ambiguities between
DFYI and MYI. Thus, although MYI is not necessarily physically associated with
ice deformation, this study groups MYI together with DFYI as “deformed ice.”
Young ice and LFYI: others. Among the many forms and stages of
growth, the young ice class defined in the GIA classifier corresponds to rough
young ice covered by frost flowers (mostly in re-frozen leads), i.e., fragile
ice crystals typically 10–30 mm in height. The presence of frost
flowers creates small-scale (millimeters to centimeters) surface roughness on the young ice
surface, which has been shown to cause a C-band backscatter increase of
6–15 dB. The
examination of SAR scenes used in this study shows that these young ice areas
can reach similar backscatter intensities to MYI. It has been demonstrated
that typical deformation in young ice, i.e., ice rafting, is difficult to
distinguish using C-band SAR and therefore is not
included in the analysis. Observations taken during N-ICE2015 show that
rafting seldom occurred for young ice in the study area. Young ice was
predominantly level with frost flower coverage, while ridging primarily
occurred where young ice was close to thick ice. Example photographs of young
ice from the campaign are shown in Fig. . Level young
ice is not part of the five-class scheme, as the LFYI class does not exclude
level ice less than 30 cm thick due to similar backscatter
. As we are interested in ice deformation
occurring on thicker ice, i.e., FYI and MYI, young ice is labeled “others”
along with LFYI.
Leads. The leads class in the GIA classifier corresponds to ice
openings occupied by calm open water, nilas or newly formed ice, thus having
the lowest backscatter intensities. The separation between open water in
different wind states is not within the scope of this study. As mentioned
above, an ice concentration product is used to filter out large contiguous
water bodies. The remaining water bodies all reside in leads that are away
from the marginal ice zone and thus more sheltered from winds. Within the SAR
scenes used in this study, visual examination shows that open water in all
major leads is calm. This class is of direct interest in the second goal of
this study and is labeled “leads” in the three-class scheme.
Example photographs of young ice witnessed in the N-ICE2015 campaign within the study area (photos: P. Itkin).
Deformation parcel tracking
Six pairs of S1 scenes from 21 to 26 January 2015 (one image pair per day)
surrounding the N-ICE2015 region are used to construct ice deformation history
for drifting ice parcels based on the sea ice drift algorithm developed by
. Sea ice deformation is calculated from line integrals as
described in, for example, and and further
filtered for noise. Sea ice drift is calculated on a
400 m regularly spaced grid and deformation on the corresponding
triangular grid. The deformation states are classified as no deformation,
divergence and convergence. The rectangular parcels are initiated on the first
day of the image sequence on a regular grid with centroids spaced by
300 m at a size of 120 km by 120 km. Initially, all parcels are
undeformed. For each subsequent day the parcels move with the average velocity
of the drift calculated within a 300 m radius of each parcel
centroid. At every step, each ice parcel accumulates counts of each
deformation class based on the average value inside the 150 m radius
from the centroid. Finally, based on the total number of counts, every parcel
is classified into undeformed, predominantly convergence, predominantly
divergence or a mix of both. These Lagrangian parcel products are then gridded
onto a 100 m grid based on the nearest-neighbor value. In the 6 d
of parcel tracking, the ice pack undergoes episodic deformation limited to
several active linear kinematic features (LKFs). The effects of this recent
deformation can then be visually compared with the classification results,
thus examining the ability of the classification to recognize the most
recently formed ice deformation features.
Results and discussionCross-platform application of the GIA classifierClassification accuracy and qualitative comparisons
The classification accuracies (CAs; Fig. ) show that for all
datasets regional retraining leads to similar and significant improvements in
classification performance over the original GIA classifier. Firstly, regional
training sets (Fig. a–c, columns S1, RS2 SCWA and RS2 FQ)
yield average CAs significantly higher than the original GIA classifier
(Fig. a–c, columns O), which is expected as regional
validation is used. Secondly, within each dataset, regional training sets
yield similar overall CAs, despite the “corresponding” training sets
(Fig. a column S1, b column RS2 SCWA and c column RS2 FQ),
yielding higher average CAs (87.63 %, 89.31 % and
91.70 %) than the rest (p values shown in
Fig. ). This corresponds well with our expectation of similar
sea ice backscatter (σ0) for the two C-band sensors, while suggesting
dataset-specific retraining can be used to achieve optimal overall accuracies.
CAs for S1, RS2 SCWA and RS2 FQ scenes using the original and retrained GIA classifier. O: training for the original GIA classifier; S1, RS2 SCWA, RS2 FQ: regional S1, RS2 SCWA and RS2 FQ training, respectively. Mean CAs are displayed in red below the box plots. P values of the difference in mean CAs are also shown.
Example comparisons between classification results using the GIA classifier with different training.
Examples of qualitative comparison between results using the GIA classifier
with different training are shown in Fig. . The original
GIA classifier yields classification maps dominated by DFYI (green) and MYI
(blue), while visual inspection of the SAR RGB composites
(Fig. a1–c1) indicates significantly more prominent
existence of LFYI, rough young ice and leads. This is caused by frequent
misclassification of young ice as MYI and leads as LFYI. The three
underrepresented ice types (LFYI, rough young ice and leads) are better
recognized by the regionally retrained GIA classifier for all datasets
(Fig. a3–c3, a4–c4, a5–c5). This shows the expected
improvement of classification performance from regional retraining as
evaluated by regional validation. The GIA classifier with different regional
training (Fig. a3–c3 vs. a4–c4 vs. a5–c5) yields results
with similar spatial distribution of ice types.
IA dependencies
Theoretically, the performance of cross-dataset application of training is
driven by the different IA dependencies of the ice types they record. To
investigate this, scatter plots of HH intensities and IAs for different ice
types are derived using the validation polygons
(Fig. ). NESZ values across the IAs are plotted for
comparison. Corresponding least-squares linear regression lines are also
plotted. HH–IA slopes derived from validation polygons as well as the original
and retrained (using corresponding training sets) GIA classifier are
summarized in Table , with slope values from previous studies
using winter C-band satellite SAR data listed for reference. For all datasets,
the HV signals show less IA dependency and are much more affected by noise, but
their inclusion in the classifier has been demonstrated to improve class
separations . For our study area, the difference in HV–IA
dependencies provides additional separating capability between MYI and young
ice, while those for leads, LFYI and DFYI are severely influenced by noise
floor configurations. Otherwise, the analysis of HV–IA dependencies provides
little additional information relating to our objectives and is not shown
here.
HH–IA scatter plots, least-squares regression lines for different ice types and NESZ values for different datasets.
HH–IA slopes (dB/∘) of different ice types derived in this and previous studies (SCN: ScanSAR Narrow; ASAR WS: Advanced Synthetic Aperture Radar, Wide Swath; QEI: Queen Elizabeth Islands). Slope values in previous studies are presented in their original forms.
LeadsYoung iceLFYIDFYIMYIS1 scenesValidation polygons -0.120-0.203-0.306-0.287-0.153Original GIA classifier -0.146-0.155-0.179-0.255-0.106Retrained classifier (S1 training) -0.130-0.233-0.290-0.265-0.150RS2 SCWA scenesValidation polygons -0.065-0.230-0.272-0.202-0.147Original GIA classifier -0.108-0.138-0.252-0.266-0.114Retrained classifier (RS2 SCWA training) -0.058-0.242-0.251-0.243-0.149RS2 SCWA FQ scenesValidation polygons -0.241-0.161-0.230-0.225-0.092Original GIA classifier -0.157-0.150-0.389-0.289-0.102Retrained classifier (RS2 FQ training) -0.272-0.169-0.225-0.219-0.073Previous studiesData (no. ofscenes)TimeLocationMäkynen et al. (2002)RS1 SCN (42)February–April 1998–2002Baltic Sea-0.19 to -0.34a-0.12 to -0.30aZakhvatkina et al. (2013)ENVISAT ASAR WS (14)Winter 2005–2006Various-0.167-0.255-0.196Gill et al. (2015)RS2 FQ (9)May 2008Franklin Bay, Canada-0.25 to -0.33bLiu et al. (2015)RS2 SCW (2)October 2009Beaufort Sea-0.198c-0.16d-0.134Mäkynen and Karvonen (2017)S1 EW (33)February and May 2016Kara Sea-0.25 to -0.26-0.23 to -0.24Mahmud et al. (2018)RS2 SCW (45)February–March 2009–2010QEI, Canada-0.32-0.22-0.14 to -0.19Lohse et al. (2020)S1 EW (–)Winter 2015–2019Arctic-wide-0.27-0.23
a Slopes of FYI with dry snow on top.b Slopes of land-fast smooth FYI with thin (7.7±3.9cm) to thick (36.4±12.3cm) snow cover.c Slope of nilas.d Slope of deformed gray ice.
The original GIA classifier (Fig. a2–c2) yields similar
separation between ice types across datasets, showing the characteristics of
its generalized S1 training. Comparatively, the validation polygons yield
different class separations (Fig. a1–c1) representing the
local condition in the study area. The class separations and IA slopes of ice
types from regional training are reflected in the retrained classification
results (Fig. a3–c3, a4–c4, a5–c5). The training set
corresponding to each dataset (Fig. a3, b4 and c5) produces
class separation most similar to the validation polygons.
The comparative class separations and IA slopes from different training sets
explain the above qualitative comparisons (Sect. ): (1) the
generalized training shows flatter HH–IA slopes and lower-extending HH values
for LFYI, which results in its strong overlap with leads
(Fig. a2–c2), while the regional training sets yield
steeper slopes for LFYI (Fig. a1–c1), resulting in better
separation between the two classes; (2) young ice and MYI are shown in all
training sets to have similar HH intensities (Fig. a1–c1),
but they show better separation in the HV channel for the regional training sets
than the generalized one (not shown).
HH–IA slopes of different ice types are within the range of values reported by
previous findings (Table ), having considered that they are
derived from different areas across the Arctic region. Comparative IA slopes
for different ice types also conform to the literature: (1) IA slopes of DFYI
are less than those of LFYI, as deformation features are strong scatterers
which lead to higher standard deviation in backscatter intensities in small
(local) IA intervals, and this added randomness in backscatter is not
sensitive to IA; (2) compared to FYI, MYI has lower IA slopes due to the
sensitivity of C-band radar signal to air bubbles in MYI, leading to
substantial presence of volume scattering (when compared to SAR sensors at
longer wavelengths, e.g., L-band), which is less sensitive to IA
. For
each dataset, when compared to the original GIA classifier, the classifier
retrained using the corresponding training set yields slope values closer to
the validation polygons.
Leads and noise floors
Between the two wide-swath datasets (S1 EW and RS2 SCWA), the IA slopes for
young ice, FYI and MYI follow similar trends (Fig. ,
columns a and b). However, the IA slope for leads in the RS2 SCWA training is
visibly flatter than that in the S1 training, which is confirmed by their
respective slope values in Table
(-0.12 dB/∘ for S1 and -0.065 dB/∘ for RS2 SCWA). The
plotted NESZs show that leads for S1 scenes are above the noise floor
throughout the IA range (Fig. b1), while for RS2 SCWA
scenes (with a flatter noise floor), it is very close to and reaches the noise
floor at an approximate IA of 30∘
(Fig. b2). This explains the flatter IA slope for leads in
RS2 SCWA scenes.
For the RS2 FQ scenes, IA slopes for the young ice, FYI and MYI
(Fig. c1) are similar to the wide-swath datasets. The RS2
FQ scenes are well within pack ice, and the leads that these scenes
additionally recognize due to higher spatial resolution (compared to
wide-swath scenes) are very narrow and scarce. Therefore, the multi-looking
and speckle filtering processes have mixed the pixel values in narrower leads
to surrounding pixels with higher backscatter intensities, resulting in small
numbers of reference polygons. This has prevented full evaluation of its IA
dependency due to uneven representation across IAs. However, the existing
reference polygons show that HH intensities of RS2 FQ lead pixels do not
reach the noise floor across the IA range and therefore do not present the
above issue in the RS2 SCWA training.
HH–IA slopes for different IA maximums and F1 scores of the leads class, with the overall slopes (maximum IA = 50) shown in corresponding colors (black: S1 scenes; red: RS2 SCWA scenes).
To further investigate the interaction between leads and respective noise
floors of the sensors, IA slopes for leads from the near range to increasing
IA maximums are plotted in Fig. . F1 scores
of the leads class, which combines producer's and user's
accuracies, and are shown to evaluate the overall accuracies of this class. S1
validation polygons show relatively constant HH–IA slopes across the range of
IA maximums (Fig. a, black lines). For RS2 SCWA scenes
(Fig. a, red lines), the slopes are steeper (higher absolute
values) for IA maximums in the near range and stabilize after reaching a
similar slope level to S1 scenes (approximately -0.16 to
-0.12 dB/∘, at IA maximums from 38 to
44∘, as shown between the dashed lines). The slopes then quickly
flatten (lower absolute values) as the IA maximum approaches the far range,
eventually reaching a much flatter overall slope of
-0.065 dB/∘ compared to
-0.120 dB/∘ for S1 scenes. This confirms the findings
from Fig. a2 where the lead pixels reach the noise floor
at approximately 30∘, and remain at a similar decibels level due to
the noise floor. Thus, using the S1 training on RS2 SCWA scenes will
inevitably introduce incorrect IA dependency for leads, leading to
misclassification and vice versa. It then follows that for a specific lead in
a SAR scene, its spatial coverage in the range direction, influenced by its
positioning, length and orientation, will impact the degree of this
misclassification of pixels inside.
For the classification results (Fig. b–d), the training
used by the original GIA classifier yields different overall IA slopes than
the regional validation for leads in both datasets, resulting in relatively
low classification accuracies, as shown by the F1 scores
(Fig. b, F1leads). When applied to their
corresponding datasets, the regional S1 and RS2 SCWA training sets
(Fig. c, black; Fig. d, red) yield HH–IA
slopes across IA maximums similar to their respective validation polygons
(Fig. a). Comparatively, cross-platform application of
training sets (Fig. c, red; Fig. d, black)
produces lower accuracies, confirming the findings above.
To inspect this effect in classification maps, an example classification of an
RS2 SCWA scene is given in Fig. , which shows the difference
between different training in recognizing leads in different IA ranges. For
the near range, the GIA classifier with S1 and RS2 SCWA regional retraining
(Fig. c1 and d1) yields very similar spatial coverage of
leads. In IAs between 34 and 39∘, classification using the RS2
SCWA training (Fig. d2) produces a more complete
representation of the leads than the S1 training (Fig. c2)
where parts of the leads are identified as LFYI. In IAs between 40 and
45∘, the RS2 SCWA training preserves all visible leads
(Fig. d3) while the S1 training keeps only part of the main
ice opening (Fig. c3, circled in black) but almost entirely
misses the other leads. This gradual increase in misclassification of leads as
LFYI with IA corresponds well with Fig. (column b): the
stronger HH–IA dependency for leads in S1 training (steeper IA slopes than RS2
SCWA training) yields the same lead–LFYI separation in the near range but
shows gradually more misclassification of leads to LFYI
(Fig. b3 compared to b4) in IAs greater than approximately
37∘. The same is true for RS2 SCWA training when used to classify
S1 scenes (Fig. a4 compared to a3), where its flatter HH–IA
slope leads to misclassification of LFYI to leads in the far range.
Comparison of classification results in different IA ranges for the RS2 SCWA scene on 5 March 2015, where IA contours with values are shown in white, and main areas containing the leads class are circled in white in (a1–a3).
From the above analyses of CAs, HH–IA dependencies and qualitative
comparisons, it can be concluded that in our study area, S1 and RS2 SCWA
training sets are transferable with the exception of the leads class. This is
caused by the different interactions between backscatter from leads and the
noise floors in the two datasets, i.e., the flattened IA slope of leads in RS2
SCWA data due to contact with the higher noise floor. This means that between
wide-swath S1 and RS2 data, transfer learning can only be conducted on classes
other than leads for whole scenes or on all classes in the near
range. Otherwise, retraining is needed for reliable separation between leads
and LFYI. The RS2 FQ scenes yield similar IA slopes for classes other than
leads compared to the wide-swath datasets, while full assessment of leads is
impeded by the lack of reference polygons. Retraining to the study area also
increases performance of the GIA classifier when applied to S1 scenes. Based
on these assessments, the S1, RS2 SCWA and FQ scenes are classified using the
GIA classifier regionally retrained using their corresponding training sets
and are used for the following comparison to ice deformation.
Comparison between classification results and deformation parcels
The five classes in the classification results are summarized into three
deformation states and compared with the deformation parcels
(Fig. ). During the period of ice parcel tracking
(21–26 January 2015), a storm with a peak wind speed of
10.8 ms-1 passed through Fram Strait (storm M1, 21 to 22 January;
) and hit the area surrounding the N-ICE2015 research camp
. The storm first pushed ice northward, compacting
the ice pack and causing ice deformation along re-frozen leads and cracking
thicker ice floes. It then transported ice southward towards the ice edge in
the second phase, generating strong divergence and opening along the same
leads and cracks. Following the storm passage, newly opened leads rapidly
re-froze following the returning of dry and cold conditions, creating new ice
. Accordingly, the parcels
(Fig. , row e) indicate major presence of new and deformed
ice concentrated along several LKFs. Divergence zones with new lead ice
prevail but are mixed with convergence zones where deformed ice is expected
to be produced, mainly in the middle of the maps (Fig. e1
and e3). The northeastern and southern parts of the maps experienced mainly
ice divergence, marked by solid ellipses.
Comparison of classification results and deformation parcels. Classification results (b1–b3) are derived using the GIA classifier retrained using corresponding training sets. The position of the RS2 FQ scene is shown as white rectangles in the wide-swath scenes (a1, a3).
It is difficult to interpret direct correspondence between the deformation
parcels and the classification maps, as areas of ice divergence and
convergence do not directly correspond to specific ice types, and the
deformation parcel maps only represent ice motion accumulated in the
6 d period. Still, observations can be made for (1) whether the
classified ice types correctly correspond to ice divergence or convergence
indicated by the deformation parcels and (2) whether the classification maps and
deformation parcels each identify visible deformation features not shown by
the other.
Classification results. An overall examination of classification maps (Fig. b1–b3) shows that deformed ice is pervasive. The same can be concluded from visual examination of the SAR RGB composites (Fig. a) and is reported by N-ICE2015 records and observations . However, the true percentage of deformation in the scene cannot be retrieved or confirmed using available data. This is due to the inclusion of ice surface with other rough surface features in the DFYI class, as well as ambiguity between DFYI and MYI, as mentioned above. The deformed ice class (three-class scheme) is comprised of mainly DFYI (five-class scheme).
On the other hand, many LKFs in the others and leads class (three-class
scheme) are visible in the classification maps (white in
Fig. b1–b3). These are comprised of mostly LFYI in
re-frozen leads (five-class scheme), which can physically correspond to smooth
young ice or FYI, as mentioned above. To more clearly examine the
correspondence of the leads and others classes to the deformation
parcels, pixels in these classes are extracted, from which small, disconnected
pixel groups (<100 pixels for wide-swath scenes and <500 pixels for the
RS2 FQ scene) are removed. These filtered pixels
(Fig. c1–c3, in red) clearly show the abovementioned LFKs, with
the primary ones marked by dashed lines and numbered in
Fig. d1–d3. Most of these level ice areas also appear in
the deformation parcel maps (Fig. e1–e3), where they are
numbered accordingly. Lines 5, 8 and 9 in Fig. d3 do not
appear in the deformation parcel maps (Fig. e3) as they are
out of the maps' areal coverage. Open-water areas in line 1 in
Fig. b1 and b3 are correctly classified as leads (in
red). The RS2 FQ scene shows similar distribution of ice types, but its higher
spatial resolution picks up some more visible ice openings with more spatial
details (lines 1, 3, 4 and 5 in Fig. d2) compared to d3).
Deformation parcels. For the deformation parcels, the most
prominent (widest) features have more recognizable correspondence to features
delineated by the classifications. These are either mostly ice divergence
mixed with convergence or exclusively divergence. For example, the end states
of lines 2 and 3 in Fig. e1 and e3 are dominated by LFYI and
young ice, surrounded by DFYI (not shown), corresponding well to the
co-authors' field experience of deformation occurrence at the interface
between young ice and older ice. The pixels showing “mostly convergence” are
derived from values accumulated in the 6 d period and therefore
cannot represent deformation accumulated over longer periods. The abovementioned areas indicating mainly ice divergence (Fig. e1
and e3, solid ellipses, excluding the major lead, i.e., line 1) are less
recognizable in the classification maps. Five-class classifications indicate
these are narrower and smaller leads occupied mostly by LFYI and young
ice. Areas in which deformation parcels indicate prominent presence of ice
convergence are mainly classified as the deformed ice class, interrupted
by small areas of others.
The areal fraction of deformation parcels occupied by each class is shown in
Fig. . Due to the coarse resolution of the deformation
parcels compared to the SAR scenes, the contrast between parcels of mainly
convergence and “mainly divergence” in terms of the comparative
proportions of deformed ice and others is not prominent. Nevertheless,
the fractions of the leads and others classes in areas of ice
divergence are higher than in those of ice convergence, corresponding well
with the above findings. For the same reason regarding spatial resolution, a
large fraction of the parcels are classified as deformed ice, which is the
dominant class in areal coverage in the SAR scenes. The others class takes
up 22.13 % to 34.38 % of all types of deformation parcels,
indicating that on average, approximately a quarter of a deformation parcel
pixel (a 300 m× 300 m area) is comprised of
others. This matches typical widths of deformation features in the study
area and period.
Areal fractions of different classes (three-class scheme) within deformation parcel pixels.
To summarize, the classifications capture ice openings in the leads and
others classes that correspond well with areas of ice divergence. This
good correspondence is also partly due to the surface features created by ice
divergence being more spatially confined. On the other hand, the deformed
ice class includes a mix of DFYI and MYI that is spatially widespread,
where the true proportions of deformed ice cannot be reliably verified and
hence has limited correspondence with areas of ice convergence. This is
due to both the accumulation of ice deformation in a period longer than the parcel
tracking and also the limitation of the classifier working only with HH
and HV channels of C-band sensors. Classification on the RS2 FQ scenes
performs similarly to the wide-swath scenes but can serve to preserve more
spatial details of surface features. The capacity of the classification
results to identify these surface features (mainly ice divergence) in the
deformation parcels serves as another validation of the regionally retrained
GIA classifier.
Limitations and future steps
This study is a first step towards the goal of Arctic-wide ice deformation
detection based on a consistent classification method applicable to multiple
SAR platforms and thus investigates the cross-platform application of the GIA
classifier in a regional setting. We work within the limitations of both the
classifier and the characteristics of HH and HV channels of S1 and RS2, which
affects the separation of level from deformed ice, as summarized above. Very
limited ground truth of ice types from in situ data are available for
retraining and validation, hence the heavy preference given to the N-ICE2015
dataset to utilize the co-authors' expert knowledge on ice conditions.
This study expands the application of the GIA classifier from S1 to RS2
data. Additional studies will be conducted, seeking further expansion of its
application to more SAR platforms, e.g., X- and L-band SAR, which provides
potential for better separation between ambiguous class pairs. IA dependency
in SAR data with these different frequencies needs to be rigorously examined
and validated. It is expected that frequency- and region-specific retraining
will still be essential for deformation detection using the altered
classifier, as SAR intensity contrast between level and deformed ice is
sensitive to SAR properties as well as ice properties that vary across regions,
e.g., small-scale roughness and ice volume structure . The
inclusion of more features into the classification is also desirable, e.g.,
polarimetric features sensitive to sea ice deformation
e.g.,, and also texture features
e.g.,. For example, the recent study by
investigated the IA dependencies of common texture features
and demonstrated that incorporating these features into ice type
classification can improve the separation of young ice and MYI, as well as the
classification of open-water areas. However, the improvement comes at the cost
of reduced spatial resolution due to the applied texture windows. Further
integration of IA dependency into classifiers other than the Bayesian
classifier is also desirable to seek better classification
performances. Finally, successful cross-platform application of an optimal
classification method can be used to create a reliable time series of
classification maps, which can be better used to derive or compare with ice
deformation products.
Conclusions
This study demonstrates that in our study area, S1 and RS2 data produce
similar IA dependencies of different ice types, except the leads class due
its interactions with different noise floors of the two sensors. Accordingly,
based on the GIA classifier, our results have demonstrated that the direct
transfer of training between the two platforms is applicable with the
exception of leads. Dataset- and region-specific retraining is found to
provide optimal classification performances, and the GIA classifier retrained
specific to S1, RS2 SCWA and RS2 FQ datasets produces similar and improved
classification results compared to the original classifier. The cross-platform
application of the GIA classifier extends usable C-band SAR data over the
study area from 2015 to present (S1) to 2010 to present (RS2). This study
further provides reference to future cross-platform application of training
between S1 and RS2, so valuable training sets can be better utilized, e.g.,
with proper retraining, or direct application in the near range or when leads
are not of interest.
The comparison between deformation parcels and classification results with
dataset-specific regional retraining shows the best correspondence in leads
with open water and nilas, young ice, or LFYI, as prominent ice openings
created by divergence following the storm passage are in linear forms and well
captured by both analyses. The DFYI and MYI classes in the classification
results do not clearly correspond to linear ice convergence zones indicated by
deformation parcels, due to both the limitation of the classification method
and the difference in the period of deformation accumulation. RS2 FQ scenes
can be used to provide more spatial details in delineating deformation
features. The comparison with deformation parcels further serves to partially
validate the classification results.
In summary, through the cross-platform application of the GIA classifier, this
study demonstrates the potential and obstacles in the transfer of training
between S1 and RS2 data, as well as in the use of the classification to
separate level from deformed ice. We expect future development of the
classifier and the inclusion of additional datasets will enable the
possibility of large-scale monitoring of ice deformation merely from the
classification of widely available satellite SAR data.
Code availability
The processing of SAR images was achieved using the open-source software SNAP provided by the European Space Agency (ESA) at https://step.esa.int/main/toolboxes/snap/ (last access: January 2021).
Data availability
RADARSAT-2 data used in this study are not publicly available and were provided by NSC/KSAT under the Norwegian–Canadian RADARSAT agreement (2015 and 2019). Sentinel-1 and Sentinel-2 data are publicly available from the Copernicus Open Access Hub (https://scihub.copernicus.eu/, last access: January 2021; ). Landsat 8 images are publicly available from EarthExplorer developed by the (https://earthexplorer.usgs.gov/, last access: January 2021). The OSI SAF global sea ice type product (OSI-403-d) is publicly available from https://osi-saf.eumetsat.int/products/osi-403-d (last access: January 2021; .
Author contributions
PI acquired funding for this study. PI, MJ and APD were involved in project administration and supervision. All co-authors were involved in the conceptualization of the study. WG was responsible for data curation, methodology design, formal analysis and result visualization. JL provided his codes and knowledge of the GIA classifier. PI produced the deformation parcel maps for comparison with the classification results. WG prepared the manuscript, with contributions from all co-authors in reviewing and editing.
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 N-ICE2015 expedition was supported by the Centre
of Ice, Climate and Ecosystems (ICE), Norwegian Polar Institute, Tromsø,
Norway. The authors also extend their thanks to all who participated in the
N-ICE2015 campaign, including personnel from the Norwegian Polar Institute, as
well as many partner organizations and the R/V Lance crew. They would like to
thank the personnel from UiT The Arctic University of Norway and the Norwegian
Polar Institute, who made the co-located satellite image acquisitions possible, and
Max König from NPI and Thomas Kræmer and Malin Johansson from UiT.
Financial support
This research has been supported by the Norges Forskningsråd (grant nos. 287871, 237906 and 280616).
Review statement
This paper was edited by Petra Heil and reviewed by two anonymous referees.
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