Automated Detection of Ice and Open Water From Dual ... - IEEE Xplore

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 55, NO. 10, OCTOBER 2017

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Automated Detection of Ice and Open Water From Dual-Polarization RADARSAT-2 Images for Data Assimilation Alexander S. Komarov, Member, IEEE, and Mark Buehner

Abstract— In this paper, we present a new technique for automated detection of ice and open water from RADARSAT2 ScanSAR dual-polarization HH-HV images. Probability of the presence of ice within 2.05 km × 2.05 km areas is modeled using a form of logistic regression as a function of the difference between the wind speeds estimated from synthetic aperture radar (SAR) data and those obtained from numerical weather prediction short-term forecasts, the spatial correlation between HH and HV backscatter signals, and the spatial standard deviation of the wind speed estimated from SAR. The resulting ice probability model was built based on thousands of SAR images and corresponding Canadian Ice Service (CIS) Image Analysis products covering all seasons and all Canadian and adjacent Arctic regions being monitored by CIS. Extensive verification of the proposed technique was conducted for an entire year (2013) against independent Image Analysis products and Interactive Multisensor Snow and Ice Mapping System ice extent products. Using a probability threshold of 0.95, 72.2% of the retrievals were classified as either ice or open water with an accuracy of 99.2% in the most clean verification scenario against Image Analysis pure ice and water data. The ability to obtain such a large number of retrievals with a very high accuracy makes it feasible to assimilate the resulting retrievals in an ice prediction system. Consequently, the developed ice/water retrieval technique will be implemented as a part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System. Index Terms— Ice probability, logistic regression, RADARSAT-2, Regional Ice-Ocean Prediction System (RIOPS), synthetic aperture radar (SAR), wind speed.

I. I NTRODUCTION

C

OUPLED ocean–sea ice–atmosphere numerical models become an increasingly important tool for studying physical, chemical, and biological processes in the rapidly changing Arctic environment [1]. Sea ice model forecasts also play a considerable role in supporting various operational applications such as increased shipping and navigation, and

Manuscript received December 29, 2016; revised April 20, 2017; accepted May 21, 2017. Date of publication July 14, 2017; date of current version September 25, 2017. This work was supported by the Canadian Space Agency RCM Data Utilization and Application Plan Program. (Corresponding author: Alexander S. Komarov.) A. S. Komarov is with the Data Assimilation and Satellite Meteorology Research Section, Environment and Climate Change Canada, Ottawa, ON K1A 0H3, Canada (e-mail: [email protected]). M. Buehner is with the Data Assimilation and Satellite Meteorology Research Section, Environment and Climate Change Canada, Dorval, QC H9P 1J3, Canada. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2017.2713987

oil and gas exploration activities in the Arctic which take advantage of the recent reduction in sea ice cover. Despite substantial progress that has recently been made in understanding and modeling the dynamics and thermodynamics of snowcovered sea ice cover and its coupling with both the ocean and the atmosphere (e.g., see [2]), the use of these models for forecasting the ice conditions over a wide range of lead times (hours to seasons) requires the specification of accurate initial conditions. Thus, the assimilation of various types of ice observations is a key component of any ice prediction system. Satellite remote sensing provides the main source of observations for data assimilation due to the large scale and difficult accessibility of the Arctic region. The Regional Ice-Ocean Prediction System (RIOPS) is an operational short-range ice prediction system developed at Environment and Climate Change Canada (ECCC) [3], [4]. Currently, the assimilation component of RIOPS uses information on ice conditions provided by optical AVHRR, passive microwave SSM/I, SSMIS and AMSR2, and scatterometer ASCAT data [5]. In addition, ice concentration is assimilated from various manually derived products from Canadian Ice Service (CIS). However, data from optical sensors are often limited by cloud coverage and limited sun illumination over the Arctic during winter. Passive microwave and scatterometer observations are generally not limited by clouds and sunlight and therefore are well suitable for assimilation [5]. Nevertheless, the low resolution (∼20–50 km) of these types of data makes them barely applicable in the vicinity of a shoreline and within narrow channels in the Canadian Arctic Archipelago. Spaceborne synthetic aperture radar (SAR) high resolution (at or below 50 m) observations from the ongoing satellite missions such as Canadian RADARSAT-2 (C-band), European Sentinel-1A and 1B (C-band), TanDEM-X (X-band), and Japanese PALSAR-2 (L-band) currently provide the most reliable information on ice conditions. SAR measurements from the surface are collected during both day and night time, and they are not dependent on weather and cloud conditions compared to optical data. High resolution of SAR data provides a number of advantages over passive microwave observations (with much lower resolution ∼10 km). For instance, the fine scale of SAR observations allows to delineate sea ice floes and leads, and estimate their size. The ability of microwaves to penetrate into the sea ice makes it possible to discriminate ice types at a high spatial resolution. Wide swath of SAR images (up to 500 km) make them also suitable

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for monitoring changes in sea ice characteristics at regional scales [6]. Therefore, SAR is used for numerous research and operational applications. For example, RADARSAT-2 has become the main source of information for operational production of ice chart products at CIS [7] since its launch in 2007. The Canadian RADARSAT constellation mission (RCM) to be launched in 2018 will be equipped with three SAR platforms [8] which will further increase the amount of SAR data over the Arctic region. One of the key future applications of the ever increasing amount of SAR data is assimilation of SAR-derived sea ice information in ice prediction systems to both produce high resolution estimates of the current ice conditions and provide the initial conditions for highresolution ice forecasts. However, implementation of SAR data assimilation is not straightforward because automated interpretation of SAR images remains a difficult task. While automated extraction of sea ice motion [9], [10], and ocean surface winds from SAR [11], [12] can be conducted with relatively high accuracy, retrieval of sea ice concentration and, even more so, thermodynamic parameters of sea ice from SAR imagery remains challenging. This is associated with the complexity of factors governing electromagnetic wave scattering from snow-covered sea ice [13], [14] leading to significant variations of the normalized radar cross section (NRCS) both spatially and temporally throughout the ice season [15], [16]. The NRCS from open water is also highly variable due to its strong dependence on roughness conditions governed by surface winds. The high variability of NRCS from sea ice and open water often leads to the overlapping of their NRCS signatures making it problematic to unambiguously detect ice and water areas from SAR. At the same time, reliable ice/water detection is important for many applications, including the use of SAR for data assimilation. A number of algorithms for the detection of ice and open water from various SAR platforms over different Arctic regions have been developed. For example, a method for detection of sea ice from fully polarimetric L-band ALOS images was proposed in [17]. This approach based on scattering entropy was developed for the Sea of Okhotsk. In [18], a neural network-based approach was proposed to discriminate open water/nilas, deformed and leveled first-year ice, and multiyear ice from C-band ENVISAT HH images acquired over the central Arctic in winter time. The reported accuracy of the classification approach (based on 20 SAR images) is around 85%. An algorithm for open water and sea ice discrimination from C-band RADARSAT-1 HH images based on segmentation and local SAR signal autocorrelation is presented in [19]. The approach was developed and tested for the Baltic Sea region where the surface scattering mechanism dominates. The availability of the cross-polarization (HV) channel in RADARSAT-2 data has facilitated various ice applications including ice and water separation from SAR [20]. Several approaches have been recently developed for automated classification of ice and water from RADARSAT-2 HH-HV images [21]–[23]. These image classification methods are based on machine learning techniques (such as support vector machine). In [21]–[23], a limited number (less than 25) of

SAR images acquired over a specific geographical area is used for training, as each of them must be manually classified at a pixel level. Furthermore, the conventional image classification techniques are found to be limited for data assimilation by the fact that they do not typically indicate the level of confidence for ice and water retrievals. Meanwhile, only ice observations with a high level of confidence should be assimilated in a sea ice prediction system to avoid translating and magnifying errors in the resulting ice forecasts. The objectives of our study are: 1) to develop an automated technique for ice and water detection from RADARSAT2 ScanSAR dual-polarization HH-HV images which provides the probability of ice/water at a given location and 2) verify the developed technique against CIS Image Analysis and Interactive Multisensor Snow and Ice Mapping System (IMS) products throughout a year cycle. The ice/water retrievals from SAR should be suitable for further assimilation into RIOPS system. II. DATA C OLLECTION A. Data Sources In this paper, we use the following three complementary sources of data: 1) RADARSAT-2 ScanSAR HH-HV images; 2) CIS Image Analysis products; and 3) ECCC global environmental multiscale model (GEM) regional deterministic forecasts. We collected 15405 RADARSAT-2 HH-HV ScanSAR images which have corresponding CIS Image Analysis information over the period of time between November 1, 2010 and September 30, 2016. The collected images cover the Canadian, Alaskan, and West Greenland waters according to the standard RADARSAT-2 geographic coverage required for the CIS operational needs. Each ScanSAR product contains HH and HV backscatter images at 50-m resolution covering approximately 500 km × 500 km area. The incidence angle varies from 20° to 50° in the range direction [24]. The CIS Image Analysis product is an operational data set manually produced from a specific RADARSAT-2 image by specially trained ice analysts at the CIS. Thus, there is no time difference between the RADARSAT-2 image and corresponding Image Analysis product. Each Image Analysis represents a set of spatial polygons with information on the total and partial ice concentrations, stage of development, and predominant floe size range for each ice type within the polygon [7]. When the Image Analysis product is generated, other supportive information such as optical satellite images and visual observations from ships and aircrafts are taken into consideration. Availability of the complementary data sources significantly depends on the region and time of year. Ice analysts also use their profound knowledge on the history of the ice and environmental conditions such as winds and air temperature when interpreting a SAR image. In this paper, we use a raster version of Image Analysis product at 5-km resolution. Both total and partial ice concentrations in CIS Image Analysis products are quantized with 10% increment [7]. Therefore, the minimum error in ice concentration could be estimated as 5%. According to [25], observational and mapping types of

KOMAROV AND BUEHNER: AUTOMATED DETECTION OF ICE AND OPEN WATER

errors are present in the CIS manual analysis. Observational errors are mainly associated with personal errors which could be introduced by ice analysts who must estimate distances and lengths, and mentally integrate various ice characteristics over large areas in a relatively short period of time. Mapping errors are relevant to ice chart preparation which include drawing error and error due to generalization of ice information. To the best of our knowledge, in the literature, there is no detailed information on the accuracy of the Image Analysis ice concentrations. At the same time, many studies (e.g., see [19]) use ice concentrations from ice charts under the assumption that this is the most reliable source of information. In support of SAR observations, we employ surface wind speeds produced by the regional version of the ECCC operational numerical weather prediction (NWP) system that uses the GEM model [26]. The GEM regional atmospheric outputs are available every hour at ∼10 km spatial resolution. B. Creation of Database To automatically collect collocated samples from SAR, Image Analysis, and GEM data we developed the algorithm described below. Both HH and HV images of the RADARSAT-2 product are converted to decibel units at the original 50-m resolution. Also, the latitude and longitude as well as the noise equivalent sigma zero (NESZ) and incidence angle were calculated for each pixel. To reduce the speckle noise, a 3 × 3 median filter is applied to both HH and HV calibrated images. Latitude and longitude of each Image Analysis sample point were converted to the SAR image coordinates (pixel and line) using rational functions described in [24]. If there is any presence of land or image boundary within the (4w + 1) × (4w + 1) pixel window surrounding the central pixel, then such a sample is discarded. Otherwise, the (w + 1) × (w + 1) pixel window around the central pixel is analyzed. The auxiliary larger (4w + 1) × (4w + 1) pixel window is selected in order to be at a distance from the image boundaries, and to be confident that small land features which may not be present in our land mask, but could be located close to those land elements which are present in the land mask, do not fall within the working (w + 1) × (w + 1) pixel window. Parameter w defining the window sizes described above was chosen to be 40 pixels. In this case, the working (w + 1) × (w + 1) pixel window which is 41 × 41 pixels (i.e., 2.05 km × 2.05 km) can adequately represent image texture, and scales of various features in sea ice such as lead openings, floe boundaries, and ridges. At the same time it is still considerably smaller than the nominal resolution of the RIOPS system (5 km). Various quantities suitable for ice and water detection can then be extracted from the sampling (w + 1) × (w + 1) pixel window. The rationale behind our choice of the predictor variables for automated ice/water separation is based on the following two factors: 1) they should not depend on the instrument characteristics such as incidence angle and NESZ and 2) ice and water should be distinguishable in the space of predictor variables. We propose three useful predictor variables as described in the following section.

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TABLE I N UMBER OF S AMPLES IN T RAINING AND T ESTING S UBSETS

From the entire time period between November 1, 2010 and September 30, 2016, the subset for the whole year of 2013 was put aside for testing, while the rest of the data set was used for training. Several years of data were used for training in order to acquire comprehensive statistics of the predictor variables for different Arctic regions, ice conditions, and seasons, and to sufficiently populate the 3-D space of the predictor variables described below. One year of testing data was chosen in order to include all seasons in the verification of the algorithm. The testing subset represents an independent subset which is not used for training our technique, but required for verification of the developed approach against CIS Image Analysis data. We note that over a 12-day period between June 16 and June 27, 2016 GEM model data were not available, and, therefore, no samples were obtained from this period. Table I summarizes the number of collected samples in both the training and testing data sets. III. P REDICTOR VARIABLES In this section, we introduce the following three predictor variables useful for ice/water detection from SAR: 1) difference between SAR and NWP wind speed; 2) spatial correlation between HH and HV backscatter signals; and 3) standard deviation of SAR wind speed. These variables are physically based, and they do not depend on the instrument characteristics such as the incidence angle and the noise floor. Furthermore, the only auxiliary input required is the NWP wind speed. A. Difference Between SAR and NWP Wind Speeds SAR wind speed for each pixel at a given sample (w + 1) × (w + 1) window resolution is calculated according to the HH-HV model proposed in [11]. Then, the average SAR wind speed value over the window is computed. The HHHV wind retrieval model takes advantage of the additional HV NRCS signal which is independent of wind direction and does not saturate at high wind speeds as opposed to the HH NRCS [11]. Therefore, the HH-HV model is independent of wind direction and it shows a better performance in [11] than the commonly used CMOD5.N proposed in [27]. Despite the fact that the SAR wind speed model is valid over the open water only, this parameter was calculated for both ice and water samples. The difference between the SAR and NWP wind speeds is written as follows: SAR − VNWP V = V

(1)

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SAR is the average SAR wind speed over the (w + where V 1) × (w + 1) sampling window, and VNWP is NWP wind speed interpolated to the corresponding location. Since the SAR wind speed model is valid over open water only, the difference between SAR and NWP wind speeds naturally tends to be close to zero over the open water areas and is often different from zero over the ice-covered areas. The range of SAR wind speeds computed over ice in the training subset for 99% of the data is from −0.73 to 23.77 m/s, with the mean value of 14.21 m/s and standard deviation of 3.72 m/s. For comparison, the range of SAR wind speeds over water in the same training subset for 99% of the data is between −13.17 and 17.6 m/s, with the mean value of 8.5 m/s and standard deviation of 4.29 m/s. Negative SAR wind speeds were intentionally preserved as discussed below in Section IV-A. B. HH-HV Correlation The spatial correlation coefficient between HH and HV sample windows is calculated as follows:   0 0 cov σHH lin , σHV lin cHH−HV =     0 0.5 (2) 0 0 0 cov σHH lin , σHH lin cov σHV lin , σHV lin where the covariance between HH and HV backscatter windows is defined as follows:  0  0 cov σHH lin , σHV lin 1 = (w + 1)2 − 1 w+1   0  0 0 0 σHH ¯ HH ¯ HV × lin (i, j) − σ lin σHV lin (i, j) − σ lin . (3) i, j =1 0 0 In (3), σ¯ HH ¯ HV lin and σ lin are the average values of HH and HV signals (taken in linear units) over the (w + 1) × (w + 1) sampling window. The HH-HV correlation is a measure of similarity between two HH and HV subimages, and it varies between −1 and 1. This parameter is often used for feature matching between two images [10]. The spatial pattern of backscatter signal from water is rather noisy at the pixel scale due to the erratic nature of water surface roughness induced by turbulent nature of wind and air–water interactions. Over water there is lack of signal variability at scales larger than the pixel size. In contrast, the snow-covered sea ice surface and subsurface roughness as well as the internal structure (i.e., brine and air inclusions) are stable which results in less noisy pattern at the pixel level compared to water. At the same time, often heterogeneities at spatial scales much larger than the pixel size (i.e., ice ridges) occur. Therefore, the corresponding changes in the backscatter signal over ice may result in higher spatial correlations between HH and HV signals compared to those for open water.

C. Standard Deviation of SAR Wind Speed The standard deviation of wind speed estimated from SAR over the (w + 1) × (w + 1) sampling window is calculated as

Fig. 1. Distributions of the difference between SAR and NWP wind speed for water and ice samples in the training subset.

follows: VSAR

⎫0.5 w+1 ⎬  1 SAR ]2 = [V (i, j) − V . SAR ⎭ ⎩ (w + 1)2 − 1 ⎧ ⎨

i, j =1

(4) We use the standard deviation of SAR wind speed as a measure of SAR wind speed changes within the sampling window. Spatial distribution of surface wind speeds normally does not contain sharp changes or discontinuities at our working spatial scale of 2.05 km × 2.05 km. Therefore, spatial variability of SAR wind speeds over open water is expected to be relatively small. Over ice areas, the SAR wind speed retrieval algorithm is not valid; however, SAR wind speed values calculated over ice-covered areas tend to have large variability than over open water. This is because SAR wind speeds are linked to HH and HV NRCS signals which could have relatively large variability over scales exceeding the pixel size. Therefore, VSAR could further contribute to ice and water detection. IV. A NALYSIS OF DATABASE FOR I CE /WATER D ETECTION For our analysis presented below, we select samples with 0% (open water) or 100% ice concentrations only from the created training data set. The 100% ice concentration samples may include mixtures of multiple ice types such as new, gray, first-year, and multiyear ice [7]. The 0% ice concentration samples could be located outside the ice edge and within the ice cover. In what follows, we demonstrate how the predictor variables introduced in the previous section contribute to the ice and water detection problem. A. Ice/Water Detection Using Single Variable Fig. 1 shows distributions of the difference between SAR and NWP wind speeds for both ice and open water samples. One may observe that V distribution for water samples is centered at zero, while the V distribution for the ice samples

KOMAROV AND BUEHNER: AUTOMATED DETECTION OF ICE AND OPEN WATER

is prominently shifted to the positive direction. From Fig. 1, it is also seen that both distributions have tails in the negative direction. This is explained by the fact that we intentionally did not force SAR wind speeds to zero in the cases where it becomes negative. This way we obtain a wider range of SAR wind speeds which is useful for ice/water detection. Negative SAR wind speeds may occur at ocean slick areas with very low levels of backscatter signals. Negative wind speed values represent a natural element of the geophysical inversion of SAR images. It is expected that the NWP wind speed could differ from SAR wind speeds, because winds derived from SAR observations have a significantly higher resolution and accuracy compared to the NWP model. However, in many situations they are close to each other over open water. Our comparison of NWP wind speeds against SAR wind speeds over water indicates a relatively good agreement, considering the large difference in resolved spatial scales, with root-mean-square error (RMSE) of 2.05 m/s. For comparison, RMSE between SAR wind speeds derived from the HH-HV wind speed retrieval model (which is the same as the wind speed retrieval algorithm used in this study) against buoy observations is 1.59 m/s [11]. The more accurate and spatially detailed the NWP model is, the more narrow the V distribution for the water samples is expected to be. The SAR wind speed parameter calculated over sea ice is predominantly higher than the NWP wind speed. In fact, it is seen from Fig. 1, that if the difference between SAR and NWP wind speeds V exceeds 8 m/s for a certain area, one could confidently classify that area as ice. However, at lower V values the two distributions in Fig. 1 overlap, and it becomes difficult to unambiguously separate ice and water. In the following two sections, we demonstrate how the addition of the HH-HV correlation and SAR wind speed standard deviation parameters further aids in ice/water separation in these situations. In order to further demonstrate the advantage of using the introduced V parameter over the original HH and HV NRCS for ice and water detection, we present distributions of HH and HV NRCS for both water and ice samples in Fig. 2 for the same training subset. One may observe that HH NRCS distribution for water [Fig. 2(a)] does not have a peak and completely overlays on the HH NRCS distribution for ice. This is primarily explained by the strong dependence of HH NRCS over water on both the wind speed, and the incidence angle. The HV NRCS over water is significantly less dependent on the incidence angle compared to the HH NRCS; therefore, the HV NRCS for water has a prominent peak [Fig. 2(b)]. At the same time, the HV NRCS water distribution is fairly wide due to the strong dependence of HV NRCS on wind speed. Therefore, there is a significant overlap between HV NRCS distributions for ice and water [Fig. 2(b)]. For comparison, the overlap between ice and water distributions in the space of V parameter (Fig. 1) is substantially smaller than the overlap between ice and water histograms in the HV NRCS domain. These factors suggest the advantage of using the difference between SAR and NWP wind speeds over the original HH and HV NRCS signals.

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Fig. 2. Distributions of (a) HH and (b) HV NRCS for water and ice samples in the training subset for all incidence angles.

B. Probability of Ice and Water For the sake of convenience, we denote the introduced predictor variables as x 1 = V , x 2 = cHH−HV , and x 3 = VSAR . Assuming that only two states (i.e., ice and open water) are possible, the Bayesian probability of ice Pi in the vicinity of a given vector x = {x 1 , x 2 } (in the 2-D case) or x = {x 1 , x 2 , x 3 } (in the 3-D case) can be computed as follows: Pi (x) =

pi (x) pi (x) + pw (x)

(5)

where pi (x) and pw (x) are probabilities of having values for the predictor variables in the vicinity of x in the case of ice and water, respectively. Accordingly, within our approach, the probability of water Pw in the vicinity of x is derived as follows: Pw (x) = 1 − Pi (x).

(6)

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Fig. 3. Distributions of spatial correlation between HH and HV signals for water and ice samples in the training subset.

Fig. 4. Empirical probability of ice in the space of two predictor variables for the training subset.

Probabilities pi,w (x) in (5) are defined as follows: pi,w (x) =

n i,w (x) Ni,w

(7)

where n i,w (x) are the number of pure ice/water samples (collected from many images) in the vicinity of vector x, and Ni,w is the total number of ice/water samples. In practice, n i,w (x) could be calculated on a rectangular mesh grid in the space of predictor variables. C. Ice/Water Detection Using Two and Three Variables Distributions of the second predictor variable (i.e., HH-HV correlation coefficient) for all water and ice samples in the training subset are shown in Fig. 3. One may observe that the distribution of the HH-HV correlation for open water has a peak located close to zero while the distribution for ice is prominently shifted to the right, and does not have a distinctive peak. In order to investigate how ice/water detection improves with introducing the second predictor variable (HH-HV correlation) we calculated the probability of ice Pi in the 2-D case according to (5) and (7). Fig. 4 displays the calculated probability of ice in the space of V and cHH−HV parameters. One may observe that the introduction of the HH-HV correlation variable significantly facilitates detecting ice and water with V values lower than 8 m/s. The overlap of ice and water histograms in the region of V lower than 8 m/s shown in Fig. 1 is resolved by adding the second HH-HV correlation predictor variable. Furthermore, the area with the ice probability close to 1 (indicating ice) and the area with the ice probability close to 0 (indicating water) are very well distinguished. A well-defined and quite narrow transition zone between these two areas is also apparent in Fig. 4. Fig. 5 shows distributions of the third predictor variable (standard deviation of SAR wind speeds) for all ice and water samples in the training subset. This predictor variable further contributes to ice and water separation, particularly,

Fig. 5. Distributions of standard deviation of SAR wind speed for water and ice samples in the training subset.

in situations where V is close to zero or negative and cHH−HV is close to zero. Fig. 6 demonstrates empirical probability of the presence of ice as a function of three predictor variables. It is seen that there is a group of points with low probability of ice (i.e., high probability of water) in the area of low standard deviation values. As shown in Section VI-A, this leads to significant improvement in the percentage of water samples detected. Below we build 2-D (without VSAR ) and 3-D (with VSAR ) versions of the ice/water detection technique and show the advantage of the 3-D version. V. I CE P ROBABILITY M ODEL To model the behavior of the ice probability rapidly changing from 0 to 1 in a narrow range of predictor variables (as shown in Fig. 4), we use the logistic regression

KOMAROV AND BUEHNER: AUTOMATED DETECTION OF ICE AND OPEN WATER

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TABLE II 2-D IPM M ODEL C OEFFICIENTS

probability values (i.e., the shape which can be seen in Fig. 4). In our study, we considered 2-D and 3-D options for the ice probability model (IPM) as follows. 1) 2-D IPM as a function of two variables, i.e., x = {x 1 , x 2 }. In this case, the function inside the natural exponential function is defined as follows: f (x) = a1 + a2 x 1 + a3 x 2 + a4 x 12 + a5 x 22 + a6 x 1 x 2 + a7 x 13 + a8 x 23 + a9 x 1 x 22 + a10 x 2 x 12 .

(9)

2) 3-D IPM model with three variables, i.e., x = {x 1 , x 2 , x 3 }. In this case the function inside the natural exponential function is f (x) = b1 + b2 x 1 + b3 x 2 + b4 x 3 + b5 x 12 + b6 x 22 + b7 x 32 + b8 x 1 x 2 + b9 x 2 x 3 + b10 x 1 x 3 + b11 x 2 x 12 + b12 x 3 x 12 + b13 x 1 x 22 + b14 x 3 x 22 + b15 x 1 x 32 + b16 x 2 x 32 + b17 x 1 x 2 x 3 + b18 x 13 + b19 x 23 +b20 x 33 . (10)

Fig. 6. Empirical probability of ice in the space of three predictor variables for the training subset. (a)–(c) Same plot, but from three different viewpoints.

approach described in, e.g., [28]. According to this approach, the probability of ice is expressed as a function of the predictor vector x as follows: Pi (x) =

1 1 + exp[ f (x)]

(8)

where f (x) is a function of the predictor vector x. We chose f (x) to be a third-degree polynomial function. The rationale behind selecting the third-degree polynomial is that we wanted to keep the polynomial degree as low as possible in order to have the regression model more robust (i.e., with no sharp changes/peaks within the range of predictor variables). At the same time, the polynomial must adequately represent the shape of the transition zone between zero and one ice

Tables II and III show the derived coefficients a1 , a2 , . . . , a10 and b1 , b2 , . . . , b20 , respectively. Fig. 7 displays the ice probability as a function of V and cHH−HV calculated according to the 2-D version of IPM. One may observe that the transition zones between open water and ice areas are well represented by the model. Also, a much larger area in the space of predictor variables is covered with ice probability values in Fig. 7 compared to Fig. 4, which allows the predictors to be used over a larger value range. Fig. 8 demonstrates three cross sections of the 3-D IPM function at the following three levels of standard deviation of SAR wind speed 0.56, 1.03, and 1.5 m/s. One may observe that the transition zone between zero and one values becomes closer to the center of the coordinate system with increasing value of the standard deviation of SAR wind speed. In Fig. 8(a) the probability values close to zero are considerably smaller than those provided by the 2-D IPM (Fig. 7). Therefore, the introduction of the third parameter into the model may improve detection of water as demonstrated below in Section VI-A.

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TABLE III 3-D IPM M ODEL C OEFFICIENTS

belonging to polygons with different ice concentrations; and 3) verification against IMS ice extent products. In order to implement the second verification scenario, the ice concentration threshold defining ice and water states is determined based on the training subset as described later. A. Image Analysis With 0% and 100% of Ice Concentrations

Fig. 7.

Modeled ice probability according to the 2-D IPM.

To obtain retrievals of ice and open water, the IPM model is applied to a given (w + 1) × (w + 1) area in a SAR image as follows. First, the ice probability Pi as a function of the predicted variables is calculated. Second, the derived probability value is compared against a threshold Pt . If Pi > Pt then the tested area is assigned as ice. If Pi < 1 − Pt then the tested area is assigned as water. If 1 − Pt ≤ Pi ≤ Pt then the tested area is assigned to be unknown denoting that the application of our technique to SAR data was not able to reliably classify that area as ice or water. The ice probability threshold Pt was chosen to be 0.95. This relatively high threshold value was chosen so that the resulting ice and water retrievals should have a high level of confidence which is required for further data assimilation. VI. V ERIFICATION In this section, we describe the following three verification scenarios for the completely independent subset (year 2013): 1) verification against Image Analysis pure ice and water samples; 2) verification against all Image Analysis samples

In the first verification test, we run the developed IPM on the independent testing data set of year 2013, containing 0% (open water) and 100% ice concentrations samples only. For ice, water, and both the ice and water subsets, we calculate the following four statistical parameters: 1) fraction of correctly classified samples defined as the number of correctly classified samples over the total number of samples in the subset; 2) fraction of misclassified samples defined as the number of misclassified samples over the total number of samples in the subset; 3) fraction of unknown samples (which were neither classified as ice nor water) defined as the number of unknown samples over the total number of samples in the subset; and 4) detection accuracy defined as the number of correctly classified samples over the number of correctly classified plus misclassified samples. All four quantities are expressed in percent. We note that for data assimilation purposes, it is important to obtain values for both the detection accuracy and the fraction of correctly classified samples as high as possible. Tables IV and V display verification results for 2-D and 3-D versions of the IPM, respectively. One may observe that both models demonstrate very high overall accuracy (exceeding 99%). Therefore, both of them could be utilized. However, from Tables IV and V one may also observe that the 3-D version of IPM is capable of correctly classifying approximately twice as many water samples (61.5%) compared to the 2-D version of IPM (31.4%). This result is achieved by introducing the third VSAR variable into the IPM model which facilitates ice and water detection in difficult situations for 2-D IPM, where V is close to zero or negative and cHH−HV is close to zero. By comparing Fig. 7 illustrating the 2-D IPM function and Fig. 8 showing a set of cross sections of the 3-D IPM function, it is seen that the introduction of the third

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TABLE IV 2-D IPM M ODEL V ERIFICATION R ESULTS A GAINST I MAGE A NALYSIS P URE I CE AND WATER S AMPLES (Y EAR 2013). Pt = 0.95

TABLE V 3-D IPM M ODEL V ERIFICATION R ESULTS A GAINST I MAGE A NALYSIS P URE I CE AND WATER S AMPLES (Y EAR 2013)

predictor variable aids in detecting water in the vicinity to the center of the coordinate system (indicated by a white rectangle in Fig. 8). Based on the conducted analysis, the 3-D version of IPM is recommended. Therefore, further verification for all ice concentrations is conducted for the 3-D version of IPM only. It is worthwhile to note, that we attempted to introduce other predictor variables into the IPM model such as melting degree hours (based on GEM time-series of near-surface air temperatures) which would presumably characterize surface conditions. However, after introducing the forth variable, both the accuracy and the fraction of correctly classified samples did not improve. Generally, the IPM model is more robust when the number of predictor variables is rather small. Therefore, we chose to keep the selected three predictor variables. B. Ice Concentration Analysis The IPM has been designed and tested for pure ice and pure water samples scenarios. However, the ultimate goal is to apply the approach to an arbitrary SAR image where any ice conditions could be possible. Therefore, it is interesting to study how the IPM performs at different ice concentrations. Thirteen ice concentration categories spanning from 0% to 100% of ice concentrations are available in Image Analysis products. Therefore, we divided the training subset into 13 ice concentration categories. For each category we applied 3-D IPM and used the same 0.95 threshold for classification to calculate: 1) the fraction of samples identified as ice with respect to the number of samples identified as either ice or water and 2) the fraction of samples identified as water

with respect to the number of samples identified as either ice or water. Fig. 9 shows the resulting fractions of ice and water samples as functions of the Image Analysis ice concentration. We note that for “Open Water” and “Bergy Water” categories, ice concentrations are below 10%, but specific values are not defined in Image Analysis products. Therefore, for “Open Water” and “Bergy Water” categories we arbitrary assigned concentration values of 2% and 4%, respectively. Fig. 9 demonstrates that with increasing ice concentration, the fraction of ice samples is increasing from 0% to 100%, while the fraction of water samples is decreasing in the opposite way. The largest change in the fraction of ice/water samples is observed for the intermediate range of ice concentration, between 10% and 60%. This result is explained by the fact that we deal with different spatial scales of Image Analysis products and the IPM model. In Image Analysis data, some average ice concentration value is assigned to a selected polygon in a SAR image, while local variations in concentration are not reported [25]. The size of the polygon is usually 1–2 orders of magnitude larger than the scale of ice/water retrievals (in our case 2.05 km × 2.05 km) from SAR. Therefore, samples with truly 0% or 100% ice concentrations could be found and correctly classified by the IPM model within large Image Analysis polygons with an intermediate ice concentration value. We also note that the IPM model could provide ice or water in situations when the true ice concentration at 2.05 km × 2.05 km scale has an intermediate value. Hence, in Fig. 9 the transition zone is fairly wide, and the ice concentration threshold separating ice and water states is somewhat vague. Nevertheless, the estimated

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Fig. 9. Fraction of ice/water samples in each Image Analysis ice concentration category for the training subset. The point of intersection denotes ice concentration threshold for ice/water retrievals.

Fig. 10. Fraction of ice/water samples in each Image Analysis ice concentration category for the testing subset (year 2013). Fig. 8. Cross sections of 3-D IPM function at the following three levels of SAR = 0.56 m/s, (b) VSAR = standard deviation of SAR wind speed: (a) V SAR = 1.5 m/s. Note changes within the white rectangular. 1.03 m/s, (c) V

ice concentration value of I Ct = 21% corresponding to the intersection point of the two curves in Fig. 9 could serve as the ice concentration threshold separating ice and water states. The minimum level of uncertainty for this threshold could be estimated as 5% which is half of the ice concentration increment (10%) used in the manual analysis. It is worthwhile to note that in Fig. 9 for the ice concentrations between 10% and 60% where the fractions of ice/water retrievals change the most, the number of ice/water Image Analysis samples is only 9.6% of the total number of samples. This is likely associated with the fact that intermediate ice concentrations are less common in nature. Furthermore,

Ice Analysts may tend to select polygons with either very low or very high ice concentration values. It is also more difficult to accurately assign an intermediate ice concentration value to a polygon compared to the low and high ice concentrations. Therefore, the accuracy of ice concentrations provided by the Image Analysis product could be significantly lower in the intermediate range compared to the low and high ice concentrations. C. Image Analysis With All Ice Concentrations In the second verification test, the IPM model was run for samples belonging to Image Analysis polygons with all ice concentrations in the testing subset (year of 2013). Fig. 10 demonstrates very similar behavior of the fractions of ice and water samples as functions of ice concentrations

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TABLE VI 3-D IPM M ODEL V ERIFICATION R ESULTS A GAINST A LL I MAGE A NALYSIS I CE C ONCENTRATION S AMPLES (Y EAR 2013)

TABLE VII 3-D IPM M ODEL V ERIFICATION R ESULTS A GAINST IMS (Y EAR 2013)

to those shown in Fig. 9. The training subset with all ice concentration samples was divided into two parts. The first part contained samples which belong to Image Analysis polygons with ice concentrations exceeding the defined threshold value, i.e., I C > I Ct and the second part contained the rest of the samples which belong to polygons with ice concentrations below the threshold, i.e., I C ≤ I Ct . Then the IPM model was run for both subsets, and statistical parameters similar to those described in Section VI-A were calculated. Table VI shows the statistical parameters for this verification experiment. One may observe that the overall accuracy (96.3%) is slightly lower compared to the accuracy obtained for pure ice and water samples (99.2%) in Table V. This is mainly explained by the fact that the scale of IPM ice/water retrievals is significantly finer than the size of Image Analysis polygon areas. Furthermore, the accuracy of Image Analysis ice concentrations within the intermediate range is lower compared to low and high ice concentration values. Therefore, Image Analysis data with intermediate ice concentrations are somewhat limited for verification of ice/water retrievals. D. IMS Ice Extent IMS ice extent products represent another independent source of data which could be used for verification of the IPM model. IMS ice extent is generated by the US National Ice Center on the daily basis on a 4-km grid using various satellite images and surface observations [29], [30]. A cell is considered to be ice covered if ice concentration within that cell (manually derived) exceeds 40% threshold [31]. IMS product is manually generated by an ice analyst looking at

all available data sources over a day period. The analyst begins with a map from the previous day to initialize the process and then integrates all data sources to create sea ice cover at 4-km resolution. Therefore, errors associated with manual analysis could be introduced by the analyst. According to [31], occasionally, the IMS product might not be updated for a given region which may introduce an additional error. Nevertheless, IMS data is often used for verification (see [5]) as it covers the entire Northern hemisphere. Therefore, any ice/water retrieval provided by the IPM for a given SAR image could be compared against IMS data. For verification of IPM against IMS data, we collected all available 7411 RADARSAT-2 images throughout the independent time period (year of 2013). Most of the collected SAR images do not have corresponding Image Analysis products, and they were not used in the verification tests previously described in this paper. For each SAR image, we conducted ice/water retrievals using the 3-D version of the IPM model as follows. A grid of points with 5-km spacing (similar to RIOPS resolution) was built on a given SAR image with original resolution (50 m). Then each grid point was surrounded by (w+1)×(w+1) window. If land is not present within that area, the three predictor variables are calculated as described in Section III. Following this, probability of ice as a function of the predictor variables is calculated according to the 3-D IPM model [(8) and (10)]. A similar approach will be applied to a SAR image for actual ice/water retrievals to be further assimilated into RIOPS. Latitude and longitude of each grid point are converted to the IMS Polar Stereographic projection and the nearest

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Fig. 11. IPM versus IMS verification example for an image acquired on April 29, 2013, 21:36 GMT to the East of Baffin Island. Each square represents a 41 × 41 pixel area at 50-m resolution used for ice/water retrieval. Red squares: ice agrees with IMS, blue squares: water agrees with IMS, red/blue squares circled in yellow: ice/water disagrees with IMS.

Fig. 12. Same as Fig. 11, but for the image acquired on May 3, 2013, 09:51 GMT over the Labrador Sea.

neighbor IMS value (indicating either ice or water) is extracted. As a result, we collected 24 719 977 ice and 12 813 635 water samples as stated in IMS products and corresponding IPM retrievals. Table VII demonstrates statistical parameters computed in a similar way to those described in Section VI-A. One may observe that the overall accuracy for ice and water data sets combined (95.5%) is slightly lower compared to the overall accuracy obtained in the previous verification test against Image Analysis with all ice concentrations (96.3%) in Table VI. This is mainly explained by the fact that IMS products represent daily aggregates of multiple satellite images, while the IPM result is derived from a single SAR image acquired at a certain time. Therefore, the relatively low temporal resolution of IMS products represents a significant limitation in dynamic areas as illustrated below. Fig. 11 demonstrates an example of verification of IPM ice/water retrievals against IMS data for a specific image taken on April 29, 2013. Very good agreement between IPM and IMS is observed in this case. Most of the disagreements (circled in yellow) are observed at the ice edge. A significantly larger number of disagreements between IPM and IMS in a dynamic ice area are observed in the second example presented in Fig. 12. Such situations are quite typical in a marginal ice zone where ice is very dynamic, and daily coverage of IMS product is not sufficient to capture rapid changes in the ice cover. In these situations, the IMS data could often be wrong. From Fig. 13, it is seen that in the first case the surface wind speeds in the vicinity of the ice edge were relatively low between 3 and 6 m/s. Winds were consistently low at 6 h prior to the image acquisition time [Fig. 13(a)] and at the image acquisition time [Fig. 13(b) and (c)]. Assuming that ice moves with the speed of 2% of the wind speed [32] under free drift conditions, the ice edge displacement for 12 h (which is half of the temporal resolution of IMS products) could be estimated between 2.6 and 5.2 km. This corresponds to 1–2

grid cells with a size of 2.05 km × 2.05 km across the ice edge, where the retrieved ice/water information may not correspond to IMS ice/water data. In the second example, the wind speeds are substantially higher and also more variable in both space and time (Fig. 14) compared to the first example (Fig. 13). In the vicinity of the ice edge the wind speeds vary between 4 and 12 m/s. A moving front could be observed by comparing Fig. 14(a) and (b). Similar to the first example, the ice displacement for a 12-h time interval could be estimated between 3.5 and 10.4 km. This corresponds to 1–3 grid cells across the ice edge, where IPM results may not correspond to IMS data. The larger number of disagreements is observed at the upper part of the ice edge likely due to the moving front across that part of the ice edge. It is also noticeable, that in the second example, the area of ice is surrounded by water from three sides and the ice edge has a complex shape. This indicates that in the second case, the ice could be more responsive to winds compared to the first example, where the ice edge has rather a linear shape. Note that empty grid cells in Figs. 11 and 12 indicate that the probability of ice at these locations is between 1 − Pt and Pt (i.e., between 0.05 and 0.95); therefore, it was not possible to reliably classify these areas as ice or water. We note that both IPM and IMS products are limited by the fact that they both could show ice values at 2.05 km × 2.05 km scale in situations where the actual ice concentration has an intermediate value at that scale. It is also worthwhile to note that another possible reason for the difference between IPM results and IMS products is that occasionally the IMS product may not be updated for certain regions where there is not enough information on a given day. In such situations, the ice analyst preserves the ice edge from the previous day [31]. In our verification against IMS, we also noticed that the use of model wind speeds in the IPM model could rarely

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Fig. 13. Wind speeds at 1-km resolution and wind directions at every 20 km corresponding to the image shown in Fig. 11. (a) GEM wind speeds and directions at 6 h before the image was acquired. (b) GEM wind speed and direction at the time the image was acquired. (c) SAR-derived wind field from the image and GEM wind directions.

lead to erroneous ice retrievals in situations where the SAR wind speed significantly exceeds NWP wind speeds. Such situations may occur when the development and motion of an atmospheric front is not accurately modeled by the NWP model. However, the accuracy and resolution of NWP models

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Fig. 14. Wind speeds at 1-km resolution and wind directions at every 20 km corresponding to the image shown in Fig. 12. (a) GEM wind speeds and directions at 6 h before the image was acquired. (b) GEM wind speed and direction at the time the image was acquired. (c) SAR-derived wind field from the image and GEM wind directions.

at forecast centers are being constantly improved; therefore, the distribution of the difference between SAR and NWP wind speeds over open water (shown in Fig. 1) will become narrower in the future. The difference between SAR and NWP

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wind speeds for open water areas would be less scattered around the central zero value, which would lead to an even narrower transition zone between 0 and 1 values of ice probability (shown in Fig. 4), and ultimately to a larger number of ice and water retrievals. Furthermore, we are currently investigating how the use of sequential SAR images could help to prevent erroneous ice retrievals and also resolve the unknown states provided by the IPM model.

TABLE VIII S UMMARY OF V ERIFICATION R ESULTS C ONSIDERING B OTH I CE AND WATER S AMPLES

VII. C ONCLUSION In this paper, we developed a new technique for automated detection of ice and water from RADARSAT2 dual-polarization HH-HV ScanSAR images. We collected 15405 SAR images and corresponding CIS Image Analysis products over the period of time between November 1, 2010 and September 30, 2016. In support of SAR observations, NWP GEM regional wind speeds were also collected. Data for the year of 2013 were set aside for testing, and the remaining data were used for training. We propose the following three physically based predictor variables for detecting ice and water from SAR at a scale of 2.05 km × 2.05 km: 1) difference between SAR and NWP wind speeds; 2) spatial correlation between HH and HV backscatter signals; and 3) standard deviation of SAR wind speed. Probability of the presence of ice was calculated in the space of the first two (2-D version) and then all three variables (3-D version) in a form of logistic regression. Both 2-D and 3-D versions of IPM demonstrated very high detection accuracy (exceeding 99%) against pure ice and water Image Analysis data; however, the 3-D IPM is able to provide approximately twice the number of water retrievals compared to the 2-D IPM which is important for data assimilation purposes. Therefore, the 3-D version of the IPM is recommended. We investigated the behavior of the IPM model for samples belonging to Image Analysis polygons with all ice concentrations (in the training subset). The fraction of ice/water samples increases/decreases with increasing the ice concentration. Ice concentration threshold of 21% separating ice and water states was determined. Using this threshold, verification of IPM against all Image Analysis samples in the testing subset was conducted. Overall accuracy in this case is slightly lower (96.3%) compared to the accuracy for pure ice and water samples (99.2%). This is explained by the limitation of Image Analysis data with intermediate ice concentrations which is associated with the fact that the size of Image Analysis polygons is significantly coarser than the scale of IPM retrievals. We also conducted extensive verification of the IPM model against IMS ice extent products for all available 7411 images for the year of 2013. The overall accuracy in this case is 95.5% which is slightly lower compared to verification against Image Analysis. This is likely associated with a relatively low temporal resolution of IMS data which is not sufficient to capture rapid changes in ice cover, particularly at a marginal ice zone. Table VIII summarizes the three verification experiments conducted in this study. The accuracy of the IPM model is the

greatest in the most clean verification scenario against pure ice and water samples, and it slightly decreases in the other two verification tests where the aforementioned limitations are found. The large number of ice/water retrievals with very high accuracy provided by the proposed technique makes it possible to directly assimilate them into ice forecast models. The developed method for ice/water retrieval from SAR will be further implemented as part of the ECCC RIOPS data assimilation component for operational use. One limitation of the current approach is that only categorical information (i.e., ice/water) is produced, whereas the data assimilation system is designed to estimate the value of the continuous variable ice concentration. A possible approach for addressing this is to use an appropriate observation operator within the data assimilation system as described in [33]. Furthermore, having reliable SAR-based ice/water information would allow assimilating in the NWP system the readily available SAR wind speeds corresponding to the open water retrievals into RIOPS. We note that the RADARSAT-2 HH-HV wind speed retrieval model used in this paper may not be directly applied to other C-band SAR platforms, such as Sentinel-1A/B, due to a different NESZ pattern. However, our concept for ice/water detection could be potentially adapted to Sentinel-1A/B as long as an HH-HV wind speed retrieval model is available for Sentinel-1A/B. The upcoming RCM mission to be launched in 2018 will significantly increase the amount of SAR images acquired over the Arctic region. We believe that the proposed ice/water retrieval technique could be adapted to the stream of SAR data from the RCM mission. We expect that assimilation of the increased number of ice/water retrievals from RCM into RIOPS could significantly improve the ice forecast skill. ACKNOWLEDGMENT The author would like to thank Dr. A. Caya for providing CIS Image Analysis and IMS data and sharing a few data processing codes, L. Pogson and Dr. T. Geldsetzer for useful discussions, Dr. Y. Luo (CIS) for producing a high-resolution raster land mask used in this study, and Dr. B. Montpetit (CIS)

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for his assistance in interpretation of Image Analysis data. The authors would also like to thank two anonymous reviewers for their constructive comments that helped to improve this paper. R EFERENCES [1] A. Proshutinsky, M. Steele, and M. L. Timmermans, “Forum for Arctic Modeling and Observational Synthesis (FAMOS): Past, current, and future activities,” J. Geophys. Res., vol. 121, pp. 3803–3819, Jun. 2016. [2] J.-F. Lemieux, L. B. Tremblay, F. Dupont, M. Plante, G. C. Smith, and D. Dumont, “A basal stress parameterization for modeling landfast ice,” J. Geophys. Res., vol. 120, no. 4, pp. 3157–3173, Apr. 2015. [3] M. Buehner, A. Caya, L. Pogson, T. Carrieres, and P. Pestieau, “A new Environment Canada regional ice analysis system,” Atmos.Ocean, vol. 51, no. 1, pp. 18–34, Feb. 2013. [4] J.-F. Lemieux et al., “The Regional Ice Prediction System (RIPS): Verification of forecast sea ice concentration,” Q. J. R. Meteorol. Soc., vol. 142, pp. 632–643, Jan. 2016. [5] M. Buehner, A. Caya, T. Carrieres, and L. Pogson, “Assimilation of SSMIS and ASCAT data and the replacement of highly uncertain estimates in the Environment Canada Regional Ice Prediction System,” Q. J. R. Meteorol. Soc., vol. 142, pp. 562–573, Jan. 2016. [6] W. Dierking, “Sea ice monitoring by synthetic aperture radar,” Oceanography, vol. 26, no. 2, pp. 100–111, 2013. [7] Manual of Standard Procedures for Observing and Reporting Ice Conditions, MANICE, 9th ed., Canadian Ice Service, Ottawa, ON, Canada, Jun. 2005. [8] A. A. Thompson, “Overview of the RADARSAT constellation mission,” Can. J. Remote Sens., vol. 41, no. 5, pp. 401–407, Dec. 2015. [9] J. Karvonen, “Operational SAR-based sea ice drift monitoring over the Baltic Sea,” Ocean Sci., vol. 8, no. 4, pp. 473–483, 2012. [10] A. S. Komarov and D. G. Barber, “Sea ice motion tracking from sequential dual-polarization RADARSAT-2 images,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 121–136, Jan. 2014. [11] A. S. Komarov, V. Zabeline, and D. G. Barber, “Ocean surface wind speed retrieval from C-band SAR images without wind direction input,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 2, pp. 980–990, Feb. 2014. [12] F. Monaldo, C. Jackson, X. Li, and W. G. Pichel, “Preliminary evaluation of Sentinel-1A wind speed retrievals,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens, vol. 9, no. 6, pp. 2638–2642, Jun. 2016. [13] M. Hallikainen and D. P. Winebrenner, “The physical basis for sea ice remote sensing,” in Microwave Remote Sensing of Sea Ice, F. D. Carsey, Ed. Washington, DC, USA: AGU, 1992, ch. 3. [14] A. S. Komarov, D. Isleifson, D. G. Barber, and L. Shafai, “Modeling and measurement of C-band radar backscatter from snow-covered firstyear sea ice,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 7, pp. 4063–4078, Jul. 2015. [15] D. G. Barber, “Microwave remote sensing, sea ice and arctic climate processes,” Phys. Canada, vol. 61, pp. 105–111, Sep./Oct. 2005. [16] J. J. Yackel, D. G. Barber, T. N. Papakyriakou, and C. Breneman, “Firstyear sea ice spring melt transitions in the Canadian Arctic Archipelago from time-series synthetic aperture radar data, 1992–2002,” Hydrol. Process., vol. 21, no. 2, pp. 253–265, Jan. 2007. [17] H. Wakabayashi, Y. Mori, and K. Nakamura, “Sea ice detection in the sea of Okhotsk using PALSAR and MODIS data,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 3, pp. 1516–1523, Jun. 2013. [18] N. Y. Zakhvatkina, V. Y. Alexandrov, O. M. Johannessen, S. Sandven, and I. Y. Frolov, “Classification of sea ice types in ENVISAT synthetic aperture radar images,” IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp. 2587–2600, May 2013. [19] J. Karvonen, M. Similä, and M. Mäkynen, “Open water detection from Baltic Sea ice RADARSAT-1 SAR imagery,” IEEE Geosci. Remote Sens. Lett., vol. 2, no. 3, pp. 275–279, Jul. 2005. [20] M. Arkett et al., “Transitioning CIS ice operations to dual channel RADARSAT-2—A cost-benefit analysis,” Canadian Ice Service, Ottawa, ON, Canada, Internal Rep., 2009. [21] N. Zakhvatkina, A. Korosov, S. Muckenhuber, S. Sandven, and M. Babiker, “Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images,” Cryosphere, vol. 11, pp. 33–46, Jan. 2017. [22] H. Liu, H. Guo, and L. Zhang, “SVM-based sea ice classification using textural features and concentration from RADARSAT-2 dual-pol ScanSAR data,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 4, pp. 1601–1613, Apr. 2015.

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Alexander S. Komarov (S’10–M’15) received the B.Sc. (Hons.) degree in radiophysics and electronics and the M.Sc. (Hons.) degree in physics from Altai State University, Barnaul, Russia, in 2006 and 2008, respectively, and the Ph.D. degree in electrical engineering from the University of Manitoba, Winnipeg, MB, Canada, in 2015. He has conducted post-doctoral research at the Centre for Earth Observation Science, University of Manitoba. Since 2015, he has been a Research Scientist with the Meteorological Research Division, Environment and Climate Change Canada, Ottawa, ON, Canada. His research interests include synthetic aperture radar remote sensing of sea ice and the ocean surface, and measurements and modeling of electromagnetic wave scattering from sea ice.

Mark Buehner received the B.A.Sc. degree from the University of Waterloo, Waterloo, ON, Canada, in 1994, and the Ph.D. degree in physical oceanography from Dalhousie University, Halifax, NS, Canada, in 2000. He specializes in data assimilation research for both weather prediction and sea-ice applications. He is currently the Scientific Lead for the development of sea-ice data assimilation systems at Environment and Climate Change Canada, Dorval, QC, Canada. He also plays a central role in Canada for data assimilation research focused on improving operational weather prediction. He is a Senior Research Scientist with the Data Assimilation and Satellite Meteorology Research Section, Environment and Climate Change Canada.