We propose a waveform mixture algorithm to detect leads from CryoSat-2 data,
which is novel and different from the existing threshold-based lead detection
methods. The waveform mixture algorithm adopts the concept of spectral
mixture analysis, which is widely used in the field of hyperspectral image
analysis. This lead detection method was evaluated with high-resolution
(250 m) MODIS images and showed comparable and promising performance in
detecting leads when compared to the previous methods. The robustness of the
proposed approach also lies in the fact that it does not require the
rescaling of parameters (i.e., stack standard deviation, stack skewness,
stack kurtosis, pulse peakiness, and backscatter
Sea ice leads (hereafter referred to as “leads”), linearly elongated cracks in sea ice, are a common feature in the Arctic Ocean. Leads facilitate an amount of heat and moisture exchanges between the atmosphere and the ocean because of the temperature differences (Maykut, 1982; Perovich et al., 2011). Although leads occupy a small portion of the Arctic Ocean, there is much more heat transfer between the atmosphere and ocean through leads than sea ice (Maykut, 1978; Marcq and Weiss, 2012). Furthermore, Lüpkes et al. (2008) showed that a 1 % change in sea ice concentration owing to an increase in lead fraction could increase near-surface temperature in the Arctic by 3.5 K. Thus, detecting and monitoring leads in the Arctic Ocean is crucial because they are closely related to the Arctic heat budget and the physical interaction between the atmospheric boundary layers and sea ice in the Arctic.
Satellite sensors have been the most efficient way to monitor leads in the entire Arctic region since the 1990s (Key et al., 1993; Lindsay and Rothrock, 1995; Miles and Barry, 1998). Advanced Very High Resolution Radiometer (AVHRR) and Defense Meteorological Satellite Program (DMSP) satellite visible and thermal images were used to detect leads in the early 1990s. Recently, the Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) product with 1 km spatial resolution was used to detect leads to map pan-Arctic lead presence (Willmes and Heinemann, 2015, 2016). They mitigated cloud interference using a fuzzy cloud artefact filter and investigated lead dynamics based on a comparison between pan-Arctic lead maps and the characteristics of the Arctic Ocean such as shear zones, bathymetry, and currents. While optical sensors have a finer spatial resolution, they are not pragmatic in the dark regions during polar nights (from December to February). In addition, leads are easily contaminated by clouds. Microwave instruments such as passive microwave sensors and altimeters have been used to detect leads and produce lead fractions. Röhrs and Kaleschke (2012) utilized the polarization ratio of the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) channels and retrieved daily thin ice concentration. With the help of the thin ice concentration, lead orientations and frequencies were derived using an image analysis technique (i.e., Hough transform) (Bröhan and Kaleschke, 2014). Airborne and space-borne radar altimeters can detect leads as well. Zygmuntowska et al. (2013) used Airborne Synthetic Aperture and Interferometric Radar Altimeter System (ASIRAS), similar to CryoSat-2, to identify leads based on waveform characteristics and a Bayesian classifier. Zakharova et al. (2015) and Wernecke and Kaleschke (2015) used the space-borne altimeters Satellite with Argos and Altika (SARAL) and CryoSat-2, respectively, to identify leads. While Zakharova et al. (2015) applied simple thresholds to identify leads along with Satellite with Argos and Altika (SARAL/Altika) tracks and estimated regional lead fractions, Wernecke and Kaleschke (2015) optimized thresholds to detect leads and produced pan-Arctic lead fraction maps using CryoSat-2 with an analysis of lead width and sea surface height.
Spectral mixture analysis based on the assumption that the spectra measured by sensors for a pixel are a linear combination of the spectra for all components within the pixel (Keshava and Mustard, 2002) was first applied to the altimetry research field in the polar regions by Chase and Holyer (1990). They estimated sea ice type and concentration using spectral mixture analysis based on Geosat waveforms. However, Geosat with a relatively small number of range bins and coarser spatial resolution is not sufficient to detect small leads in the winter (DJF) and spring seasons (MAM) in the Arctic. In this study, we adapted the linear mixture algorithm concept to waveforms from Synthetic Aperture Interferometric Radar Altimeter (SIRAL), CryoSat-2, to identify leads and produce monthly pan-Arctic lead fractions from January to May and October to December between 2011 and 2016. Waveform endmembers are crucial for implementing the spectral mixture algorithm (Fig. 1). The N-FINDR (N-finder) algorithm was used to select waveform endmembers from extracted waveforms by decision tree (DT) from Lee et al. (2016), which avoids the subjective selection of endmembers. The detected leads were visually evaluated with MODIS images (at 250 m resolution) and compared with other threshold-based lead detection methods. The proposed waveform mixture algorithm does not require changes to any of the parameters used in the algorithm to detect leads when the CryoSat-2 baseline is updated, which is a significant advantage compared to the existing threshold-based lead detection methods. The main objectives of this study are to (1) develop a novel lead detection method based on the waveform mixture algorithm, (2) compute recent pan-Arctic lead fractions, and (3) examine the spatiotemporal distribution of lead fractions.
CryoSat-2, carrying SIRAL, was launched in April 2010 by the European Space
Agency (ESA). CryoSat-2 is a satellite dedicated to polar research. SIRAL is
a radar altimeter with a central frequency of 13.575 GHz (
CryoSat-2 transmits bursts of radar pulses (i.e., 64) with high pulse repetition frequency (PRF, 18.181 kHz), which forms Doppler beams because of the along-track movement of the satellite (Wingham et al., 2006). With the help of the high PRF, each Doppler beam is coherently correlated and pointed at the same location on the Earth's surface. This is called beam stacking. Multi-looking is conducting by averaging the stacking beams to reduce speckles and thermal noises (Salvatore, 2013). Exemplary results of waveforms in the L1b SAR data are shown in Fig. 1. These waveforms represent the temporal distribution of reflected power when the radar pulses reach the surface, describing a flat or rough surface. In this case, since the leading edge of each waveform starts from a different range bins, the beginning of the waveform was set to 1 % of the maximum echo power (Fig. 1). For a more detailed explanation of the processes used to develop L1b waveform data, refer to Salvatore (2013).
Representative waveforms of
The EUropean organization for the exploitation of METeorological SATellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provides multiple sea ice products such as sea ice concentration, sea ice edge, sea ice type, sea ice emissivity, and sea ice drift. The sea ice edge product was developed using the polarization ratios of 19 and 91 GHz, the spectral gradient ratios of 37 and 19 GHz from Special Sensor Microwave Imager/Souder (SSMIS), and anisFMB from the Advanced Scatterometer (ASCAT) with a Bayesian approach (Aaboe et al., 2016). In this study, monthly averaged sea ice edge data were used to mask monthly lead fraction maps. The open ice cover in the sea ice edge product was regarded as an open ocean.
Lead fraction maps produced from previous studies (Röhrs and Kaleschke,
2012; Wernecke and Kaleschke, 2015; Willmes and Heinemann, 2016) were
compared to the lead fraction maps generated using the proposed waveform
mixture algorithm in this study. Röhrs and Kaleschke (2012) produced
daily thin ice concentration maps using AMSR-E data with a 6.25 km grid,
which can detect leads that are wider than 3 km. The daily thin ice
concentration that was over 0.5 (i.e., 50 %) was considered to be a lead
and binary daily lead maps were averaged to properly compare other monthly
lead fraction maps. A threshold-optimization-based lead detection method with
the CryoSat-2 was used in Wernecke and Kaleschke (2015) and monthly lead
fraction maps were calculated with the grids of 99.5 km. The thin ice
concentration maps (Röhrs and Kaleschke, 2012) and the lead fraction maps
using CryoSat-2 (Wernecke and Kaleschke, 2015) are available on their website
(
An endmember in remote sensing data represents a spectrally pure ground component in a single pixel. For example, it could be pure water, vegetation, bare ground, or a soil crust pixel in remote sensing data. Endmembers play the most important role in conducting spectral mixture analysis. Spectral mixture analysis assumes that the spectra measured by sensors for a pixel is a linear combination of the spectra of all components within the pixel (Keshava and Mustard, 2002). This technique is widely used to resolve spectral mixture problems in image analysis (Foody and Cox, 1994; Lu et al., 2003; Wu, 2004; Iordache et al., 2011). Spectral mixture analysis determines the fractions of the components (i.e., classes) found in mixed pixels by producing abundances of the components based on endmembers. The proposed waveform mixture algorithm adopts the concept of spectral mixture analysis. Since the waveform of altimetry within a footprint could be considered to be a mixture of leads and various types of sea ice, spectral mixture analysis can be applied in this framework. In this study, waveforms of CryoSat-2 L1b data were used as endmembers such as the waveform of pure lead and first-year ice (FYI) (Fig. 1). The lead and ice endmembers are used as reference data for separating leads and ice. In order to successfully implement the waveform mixture algorithm, the proper selection of lead and ice endmembers is essential.
The basic waveform mixture model is defined as follows in Eq. (1).
Chase and Holyer (1990) were concerned by two problems with the application of spectral mixture analysis to the waveform of altimeter data. First, the waveform within a footprint may not be linearly mixed between leads and sea ice. CryoSat-2 is more sensitive to the specular reflection of leads than the diffuse reflection of sea ice when both leads and sea ice exist within the same footprint, which implies the waveform may tend to be similar to the endmember of the leads (Chase and Holyer, 1990). Since CryoSat-2 data have a large number of range bins than Geosat, indicating higher vertical resolution, they could be used to reduce the overestimation of leads. Secondly, the waveform of the altimeter (i.e., Geosat) is somewhat weighted on the center of a footprint rather than representing an entire footprint. This could be an error source when applying spectral mixture analysis to waveform data (Chase and Holyer, 1990). However, the CryoSat-2 L1b waveform is produced by averaging more than 200 weighted waveforms with various incidence angles, which can alleviate this a problem.
The selection of endmembers is essential in the framework of the waveform
mixture algorithm. Among CryoSat-2 orbit files from January to May and October to
December between 2011 and 2016, a total of 48 orbit files were selected to
extract endmember samples by month (15th day of the month for January to
May and October to December), which fully transverse the broad Arctic Ocean
(Fig. 2). The lead and ice waveforms are extracted by using the DT
algorithm developed for lead detection by Lee et al. (2016). The DT has
proven to be very effective in various remote sensing classification tasks
(Kim et al., 2015; Torbick and Corbiere, 2015; Amani et al., 2017; Tadesse
et al., 2017; Hisabayashi et al., 2018). The lead and sea ice endmembers
(i.e., the most representative waveforms) are a key factor in the successful
implementation of the waveform mixture algorithm. In order to avoid the
subjective selection of endmembers, a number of endmember candidates were
extracted by the DT algorithm (Lee et al., 2016) and the N-FINDR algorithm
determined the optimum lead and ice endmembers. The N-FINDR algorithm
basically uses the fact that the N spectral dimension and the N-volume (
The 48 CryoSat-2 orbit files from January 2011 to December 2016 used for extraction endmember waveforms. The CryoSat-2 orbit files almost cover the entire Arctic Ocean.
The DT model from Lee et al. (2016) was developed using data (i.e., stack
standard deviation, stack skewness, stack kurtosis, pulse peakiness, and
backscatter
The lead classification based on the waveform mixture algorithm was evaluated with 250 m MODIS images collected from March to May and in October. We used Earth View 250 m Reflective Solar Bands Scaled Integers in MOD02QKM and adjusted the contrast to emphasize leads from sea ice in the images. It should be noted that since MODIS images with a spatial resolution of 250 m were not available in January, February, November, and December due to polar nights, the evaluation with MODIS images and lead classification results based on CryoSat-2 could not be used. To secure the reliability of the comparison, the temporal difference between the MODIS images and CryoSat-2 data was always under 30 min.
Lead and ice abundance derived by the waveform mixture algorithm on
10 October 2015.
The waveform mixture model produces abundance data (i.e., lead and sea ice abundance) at along-track points with respect to each endmember of the leads and sea ice (Fig. 3). While the lead abundances are high on the leads, the ice abundances are low on the leads, and vice versa (Fig. 3). Thresholds have to be determined for a binary classification between leads and sea ice. Optimum thresholds to produce binary lead classification from lead and sea ice abundances were identified through an automated calibration. To implement the automated calibration, reference point data of leads and sea ice were determined by visual inspection of four MODIS images collected on 17 April 2014, 25 May 2015, 10 October 2015, and 27 March 2016. While the calibration was conducted using half of the randomly selected reference data, the validation was performed using the remaining data. The size of the leads detected by the proposed waveform mixture algorithm is 250 m or greater because the calibration and validation processes were conducted using MODIS images with 250 m spatial resolution. It should be noted that leads smaller than 250 m are hardly seen in MODIS images, which implies that there is some uncertainty in the comparison of the lead detection methods for small leads. Threshold combinations from 0.2 to 0.9 with a step size of 0.01 for both lead and sea ice abundances were tested, and the one resulting in the highest accuracy was determined to be the optimum threshold combination.
Lead detection results were evaluated using three accuracy
metrics – producer accuracy, user accuracy, and overall accuracy (Table 1).
Producer accuracy (i.e.,
Error matrix for calculation of user, producer and overall accuracy in terms of lead and ice classification.
The monthly lead fraction was derived by dividing the number of lead observations by the number of total observations within a 10 km grid in a month. It is noted that, while there are more than 30 CryoSat-2 observations in the 10 km grid around the center of the Arctic, fewer than five observations are generally found in each 10 km grid in the marginal zones of Arctic Ocean. This will be dealt with in the results section in more detail. It also should be noted that it is hard for the altimeter-based lead detection methods to be used to identify the propagating, opening and closing of leads, such as in Wernecke and Kaleschke (2015) and this study, because sea ice and leads generally move when the altimeters revisit a certain grid.
Since each grid has a different number of CryoSat-2 observations, a
sensitivity analysis was conducted in terms of the number of observations by
grid. We tested various percentage values to identify which percentage
appropriately represents grid sensitivity. As the percentage increased, the
grid sensitivity (i.e., standard deviation) also increased but the spatial
difference was not significant; hence 30 % was chosen. Thirty percent of
the lead and ice observations in 10 km
Figure 1 shows representative waveforms of leads and sea ice extracted by the N-FINDR algorithm as endmembers. The waveform of leads is dominated by specular reflection, resulting in a narrow peak curve. The representative waveform of sea ice has a wider distribution due to its rough surface when compared to that of leads. Considering different types of sea ice such as young ice, FYI, and multiyear ice (MYI), the representative waveform of sea ice is not significantly different from that of FYI based on visual inspection (Zygmuntowska et al., 2013; Ricker et al., 2015; Lee et al., 2016).
The optimum thresholds for the lead and sea ice abundances were determined
to be 0.84 and 0.57 through the automated calibration, respectively.
According to the thresholds, leads were identified with the conditions of
lead abundance
Multiple lead classification methods based on CryoSat-2 data were evaluated by visual inspection with high-resolution (250 m) MODIS images. Leads (i.e., red dots) and sea ice (i.e., light blue dots) are distinguished, depending on the surface conditions of lead and sea ice (Fig. 4). For better comparisons, a quantitative assessment is required (Fig. 4). DT from Lee et al. (2016) produced the highest overall accuracy (95.19 %), followed by the waveform mixture algorithm (95 %), Rose et al. (2013) (93.26 %), and Laxon et al. (2013) (91.70 %). DT from Lee et al. (2016) produced the highest user accuracy for leads, while the proposed approach produced the highest producer accuracy for leads, which implies a slight overdetection of leads by the proposed waveform mixture algorithm. The user accuracy for leads of Laxon et al. (2013) is the lowest, resulting in much overdetection of leads (i.e., many leads on sea ice; Fig. 4). Similarly, the user accuracy for ice in Rose et al. (2013) is lower than that of the proposed waveform mixture algorithm, indicating the detection of leads on sea ice, which is shown in Fig. 4b and c. While the performance of the waveform mixture algorithm was comparable to the DT algorithm from Lee et al. (2016), the waveform mixture algorithm slightly overestimated leads, resulting in a lower user accuracy for leads than that by DT (Figs. 4 and 5). These are inevitable results because waveforms used in the waveform mixture algorithm are basically extracted by DT from Lee et al. (2016). The lead classification results should be assessed during all months (i.e., January to May, and October to December) and years (i.e., 2011 to 2016), using MODIS images to thoroughly evaluate the proposed waveform-based algorithm for lead detection. However, the lead classification results in January, February, November, and December were not assessed using MODIS images due to polar nights. Thus, the lead classification results in these months possibly have uncertainties. It should also be noted that the validation was limited as the MODIS images did not fully cover the entire Arctic region (top of Fig. 4).
Visual comparison of lead classifications
Accuracy assessment results for lead detection with three existing methods and the proposed waveform mixture algorithm (WMA).
The monthly lead fraction maps with a 10 km grid from January to May, and October to December from 2011 to 2016 are shown in Figs. 6 and 7. The period from June to September is generally considered to be the melting season. In this season, the presence of leads as well as melt pond in sea ice are dominant. It is difficult to accurately distinguish leads from sea ice due to the fact that the waveform of the melt pond is quite similar to that of leads. Since the lead detection methods for the retrieval of sea ice thickness do not work well in the melting season, the sea ice thickness during the melting season is still unavailable (Tilling et al., 2017). We have compared lead fraction maps to the different spatial resolutions (i.e., 10, 50, and 100 km) to decide the proper spatial resolution. The spatial distribution of all lead fraction maps looked similar (not shown) because the ratios of lead observations to the entire CryoSat-2 observations did not significantly change among different spatial resolutions. Although the number of CryoSat-2 observations with a 10 km grid around the coastline is small (5–10), the greater number of observations in larger grids (50 and 100 km) resulted in a similar distribution of lead fraction around the coastline. It is believed that the lead fraction maps with 10 km spatial resolution better represent the detailed spatial distribution of leads. The areas in the marginal ice zones of the Arctic Ocean clearly show a high lead fraction due to the shear zone (i.e., an area of deformed sea ice along the coast and outflow of sea ice (Serreze and Barry, 2005). In particular, the high lead fraction was found around the Beaufort Sea during the spring season (MAM) because of the Beaufort Gyre, a wind-driven ocean current. It is widely known that the Chukchi Sea is the main strait through which warm Pacific water flows into the Arctic (Woodgate et al., 2006, 2010). However, the lead fraction around the Chuckchi Sea was lower than the lead fraction around the Beaufort Sea from January to April (i.e., winter season) 2011 and 2016, excluding 2015. While the lead fraction decreases from October to March (i.e., freezing season) with a minimum in March, the lead fraction starts to increase from April.
Changes in the Arctic Ocean circulation have contributed to the change in state of sea ice. The lead fraction along the coast of northwestern Greenland in Figs. 6 and 7 is low because of the convergence of sea ice by two major circulations, as shown in Kwok (2015). Kwok et al. (2013) revealed that the currents speed of Beaufort Gyre and Transpolar Drift increased from 1982 to 2009, leading to a decrease in the fraction of MYI. However, we do not find an increase in lead fraction between 2011 and 2016, likely due to the high interannual variability in lead fraction (Fig. 8). In order to properly compare the Arctic current circulations and lead fraction, long-term lead fraction data are needed.
Monthly lead fraction maps based on the waveform mixture algorithm from January to May and October to December between 2011 and 2013. The range of the color bar was set from 0 to 0.5 to emphasize lower values.
Monthly lead fraction maps based on the waveform mixture algorithm from January to May and October to December between 2014 and 2016. The range of the color bar was set from 0 to 0.5 to emphasize lower values.
Averaged seasonal lead fraction in spring (MAM), fall (ON), and winter (DJF) between 2011 and 2016. The lead fraction from June to September was not available because leads were hard to distinguish from melt ponds using CryoSat-2 in the summer season.
The high standard deviation values around the coastline of the Arctic Ocean
imply that the reliability of lead fractions was low. This might further
explain why we do not observe an increase in the lead fraction in marginal
zones as reported in the literature. On the other hand, the relatively large
number of CryoSat-2 observations around the North Pole produced low standard
deviations, indicating less sensitivity (Fig. 9i–l). As mentioned in
Sect. 3.2, the number of CryoSat-2 observations decreases from the North Pole
toward the coastline of Arctic Ocean. This results in an increase in
statistical uncertainties when calculating monthly lead fraction around the
coastline of Arctic Ocean based on the small number of CryoSat-2
observations. The number of lead and ice observations is shown in Fig. 9a–h.
While there are a few lead observations in the central Arctic, a large number
of ice observations was found in the central Arctic. The high standard
deviation values around the coastline of the Arctic Ocean imply that the
reliability of lead fractions was low, while the relatively large number of
CryoSat-2 observations around the North Pole produced low standard deviation
indicating less sensitivity (Fig. 9i–l). There was a spatial difference of
sensitivity by month (i.e., January to April) because of the different number
of lead observations. Especially since there was no lead observation in the
East Siberian coast and eastern Laptev Sea, the sensitivity (i.e., standard
deviation) was also zero (Fig. 9c and d). It should be noted that the
corresponding lead fraction might not represent an actual lead fraction in a
10 km
Since the overall accuracy metrics of the proposed waveform mixture algorithm
approach was comparable to those of the existing methods, especially DT, the
waveform-based method can be used for estimating sea surface height anomaly. Threshold-based lead
detection methods have to be rescaled whenever baseline data are updated. For
example, beam behavior parameters and backscatter
The use of the waveform mixture algorithm might not work well for detecting
refreezing leads. In Fig. 4 c, g, k, and o, the dark area in the MODIS scenes
around the latitude of 84.26
Four monthly lead fraction maps (Röhrs and Kaleschke, 2012; Wernecke and
Kaleschke, 2015; Willmes and Heinemann, 2015) were compared to evaluate the
pros and cons of each method used to produce the maps (Fig. 10). All four methods represent the spatiotemporal pattern of leads well for the
freezing season from January to March. Scene-based lead fraction maps (i.e.,
AMSR-E in Fig. 10a–c, and MODIS in Fig. 10d–f) and
altimeter-based lead fraction maps (i.e., CryoSat-2 in Fig. 10g–l) have
fundamentally different spatial characteristics, as AMSR-E and MODIS are
sensitive to different surface features. Scene-based lead fraction maps
better represent the linear feature of leads and coastal polynya than
altimeter-based lead fraction maps. Since the AMSR-E-based approach only
detects relatively large (
Altimeter-based monthly fraction maps might be insufficient to represent monthly lead fractions in the coastline of the Arctic Ocean due to the limited number of CryoSat-2 observations in a month. Nonetheless, altimeter-based lead fraction maps documented the overall spatial distribution of leads reasonably, in particular for high lead fractions in the shear zone. Wernecke and Kaleschke (2015) used a random cross-validation technique to derive optimum thresholds based on ground references (i.e., MODIS images). They identified leads conservatively to reduce false classifications. The classification results strongly depend on ground reference data. Since relatively high-resolution (250 m) MODIS images were used to construct reference data in this study, the waveform mixture algorithm was able to identify small leads through the calibration process of the abundance data (Fig. 4). Although the proposed waveform mixture algorithm produced lead fraction maps with higher spatial resolution than those in Wernecke and Kaleschke (2015), the lead fractions around the coastline of the Arctic Ocean from Wernecke and Kaleschke (2015) appeared to have less sensitivity. This is because of the larger number of lead observations in a much coarser grid than that from our results. The grid sensitivity analysis should be considered when interpreting the lead fraction maps around the coastline of the Arctic Ocean derived by the proposed waveform mixture algorithm.
The choice of monthly lead fraction maps depends on the user's interest. Scene-based lead fraction maps better represent coastal polynya and the intrinsic form of leads (Röhrs and Kaleschke, 2012; Willmes and Heinemann, 2016). CryoSat-2-based lead fraction maps might not represent the linear shape of typical leads well like cracks which include deformed and fragmented sea ices that are not in linear form. This is also a way to exchange heat and momentum transfer between the atmosphere and ocean, which can be detected as leads.
Comparison to other lead fraction maps from January to March 2011.
In this study, we developed an alternative lead detection method (i.e.,
waveform mixture algorithm) using CryoSat-2 L1b data, which can overcome the
drawbacks of the previous threshold-based lead detection methods. Regardless
of an update in CryoSat-2 baseline data, the proposed waveform mixture
algorithm can consistently identify leads without rescaling parameters such
as beam behavior parameters, pulse peakiness, and backscatter
However, the waveform mixture algorithm depends on the quality of
the endmembers. Although the use of the N-FINDR algorithm decreased the
subjective selection of endmembers, waveform samples of leads and sea ice
derived by DT algorithm from Lee et al. (2016) may introduce uncertainty
because the algorithm was validated for March and April from 2011 to 2014.
The leads that are not identifiable in the MODIS images were not considered
in this study. Detecting leads smaller than the along track resolution of
CryoSat-2 (
The waveform mixture algorithm was proposed to detect leads with CryoSat-2 L1b data. The lead and sea ice waveforms were considered to be endmembers that are essential for implementing the waveform mixture algorithm. The endmembers (i.e., representative waveforms of leads and sea ice) were extracted by the N-FINDR algorithm among numerous waveforms (i.e., 420 858 waveforms of sea ice and 8501 waveforms of leads). The thresholds used for the binary classification were determined by calibrating lead and sea ice abundances with reference data extracted from a high-resolution (250 m) MODIS images. The results show that the proposed approach robustly classified leads with comparable performance to DT from Lee et al. (2016) and slightly better than the existing simple thresholding approaches for lead detection (Rose et al., 2013; Laxon et al., 2013). Furthermore, the lead detection of the waveform mixture algorithm was comparable to the DT-based lead detection method (Lee et al., 2016), suggesting that a sea ice freeboard can be retrieved with the robust lead detection method using the waveform mixture algorithm. Monthly lead fraction maps were produced using the proposed waveform mixture approach, showing clear interannual variability. The results of the lead fraction maps are consistent with the findings of recent studies (Tilling et al., 2015; Ricker et al., 2017; Kim et al., 2017).
Threshold-based lead detection methods heavily depend on beam behavior parameters. However, the proposed waveform mixture algorithm directly uses waveforms, which does not require it to change any parameters when the CryoSat-2 baseline version is updated. This method can be easily adapted to future missions. In this context, this waveform mixture algorithm can be used to consistently produce monthly lead fraction maps during the extended CryoSat-2 mission for monitoring Arctic sea ice. In addition, this study showed the high interannual variability of pan-Arctic lead fractions in recent years (i.e., 2011–2016).
The research data can be obtained by request to the corresponding author (ersgis@unist.ac.kr).
The authors declare that they have no conflict of interest.
This study was supported by the Korea Polar Research Institute (KOPRI) grant PE18120 (Research on analytical techniques for satellite observations of Arctic sea ice). Edited by: Bert Wouters Reviewed by: Sascha Willmes and two anonymous referees