Comparison of NASA Team2 and AES-York Ice Concentration ... · Comparison of NASA Team2 and AES-York Ice Concentration Algorithms Against Operational Ice Charts From the Canadian

2164 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 8, AUGUST 2006

Comparison of NASA Team2 and AES-York IceConcentration Algorithms Against Operational

Ice Charts From the Canadian Ice ServiceMohammed Shokr, Member, IEEE, and Thorsten Markus, Member, IEEE

Abstract—Ice concentration retrieved from spaceborne pas-sive-microwave observations is a prime input to operationalsea-ice-monitoring programs, numerical weather prediction mod-els, and global climate models. Atmospheric Environment Service(AES)-York and the Enhanced National Aeronautics and SpaceAdministration Team (NT2) are two algorithms that calculate iceconcentration from Special Sensor Microwave/Imager observa-tions. This paper furnishes a comparison between ice concentra-tions (total, thin, and thick types) output from NT2 and AES-Yorkalgorithms against the corresponding estimates from the opera-tional analysis of Radarsat images in the Canadian Ice Service(CIS). A new data fusion technique, which incorporates the actualsensor’s footprint, was developed to facilitate this study. Resultshave shown that the NT2 and AES-York algorithms underesti-mate total ice concentration by 18.35% and 9.66% concentrationcounts on average, with 16.8% and 15.35% standard deviation,respectively. However, the retrieved concentrations of thin andthick ice are in much more discrepancy with the operationalCIS estimates when either one of these two types dominates theviewing area. This is more likely to occur when the total iceconcentration approaches 100%. If thin and thick ice types coexistin comparable concentrations, the algorithms’ estimates agreewith CIS’s estimates. In terms of ice concentration retrieval, thinice is more problematic than thick ice. The concept of using a singletie point to represent a thin ice surface is not realistic and providesthe largest error source for retrieval accuracy. While AES-Yorkprovides total ice concentration in slightly more agreement withCIS’s estimates, NT2 provides better agreement in retrieving thinand thick ice concentrations.

Index Terms—Atmospheric Environment Service (AES)-Yorkalgorithm, data fusion, Enhanced NASA Team (NT2) algorithm,ice retrieval algorithms, operational ice monitoring, passive mi-crowave for ice, Radarsat, sea ice concentration.

I. INTRODUCTION

I CE CONCENTRATION is an established parameter re-trievable from passive-microwave satellite observations. Its

continuous spatial and temporal coverage makes it an importantdeliverable for an operational ice-monitoring programs and aprime input to weather and climate modeling. Multichannelsatellite passive-microwave sensors have been used to retrieveice concentration through different algorithms for over 25 years(see [1] for an overview). The first sensor was the Scanning

Manuscript received April 15, 2005; revised December 15, 2005. The datafusion technique was developed with funding by the Space TechnologiesDivision of the Canadian Space Agency under MOA 02M0A26002.

M. Shokr is with the Meteorological Service of Canada, EnvironmentCanada, Toronto, ON M3H 5T4, Canada (e-mail: [email protected]).

T. Markus is with the Hydrospheric and Biospheric Sciences LaboratoryNASA Goddard Space Flight Center, Greenbelt, MD 20771 USA.

Digital Object Identifier 10.1109/TGRS.2006.872077

Multichannel Microwave Radiometer (SMMR) on the NationalOceanic and Atmospheric Administration’s (NOAA) Nimbus 7satellite from 1979 to 1987, followed by the Special SensorMicrowave/Imager (SSM/I) on a series of Defense Meteorolog-ical Satellite Program (DMSP) satellites from 1987 to present.Since May 2002, the Advanced Microwave Scanning Radiome-ter (AMSR-E) onboard the Enhanced National Aeronautics andSpace Administration (NASA) Earth Observing System (EOS)Aqua satellite provides daily data of both polar regions.

Several algorithms have been developed to retrieve ice con-centration from passive-microwave observations. Among thosealgorithms are NASA Team (NT) [2], [3] and its enhancedversion (NT2) [4]. These are basically empirical algorithms,which make use of more than one radiometric parameter spaceto separate ice from water; bare ice surface from snow-layeredsurface; and thin ice from thicker ice. Ice concentrations areproduced operationally using the NT2 algorithm at the U.S.National Ice Center and the National Snow and Ice Data Center(NSIDC) in Boulder, CO. NT2 is also the primary algorithmfor the standard AMSR-E concentration products. Another al-gorithm, called Atmospheric Environment Service (AES)-York[5], employs a combination of a simple version of the radiativetransfer model along with empirical modules. This algorithmis being used in the operational ice and weather programs inCanada.

Several studies were undertaken to validate the retrievedice concentration algorithms [2], [4], [6]–[10]. The valida-tion approaches so far used gridded (i.e., a drop-in-the-bucketapproach) satellite data. This spatial and temporal averagingapproach does not allow relating satellite observations directlyto the surface conditions that engendered them. To solve thisproblem, orbit data, instead of gridded data, have to be used. Forthis purpose, a new data fusion approach has been developedto overlay the footprint from a coarse-resolution imagery data(e.g., SSM/I) onto a fine-resolution image (e.g., Radarsat).Hence, radiometric measurements or derived parameters froma single pixel of the coarse-resolution data can be related to thesubpixel information available from the coarse-resolution data.

This approach facilitates, for the first time, comparison ofthe retrieved ice type concentrations (thin, thick, and total ice),which are derived from SSM/I observations against correspond-ing estimates from the operational analysis of Radarsat imagesat the Canadian Ice Service (CIS). This is important becauseevaluation of sea-ice-concentration algorithms based on theirperformance in retrieving total ice concentration only may bemisleading. It is possible that an algorithm outputs correctvalue for total ice concentration, but fails to resolve the thinand thick ice concentrations. An accurate estimate of thin ice

0196-2892/$20.00 © 2006 IEEE

SHOKR AND MARKUS: COMPARISON OF NT2 AND AES-YORK ICE CONCENTRATION ALGORITHMS 2165

concentration is particularly important for climate and weatherapplications as it affects the energy balance and heat exchangebetween ocean and atmosphere.

This paper offers quantitative comparison between ice con-centration estimates from AES-York and NT2 algorithms onone hand against estimates from a major operational ice-monitoring program (the CIS national program). The com-parison is performed at the SSM/I footprint level. It shouldbe emphasized that this paper is about a comparison of thetwo data sets; not an evaluation of one set against the other.That is because the CIS’s results, thought fairly accurate forthe operational use, still cannot be considered as truth dataas much as any other data set (e.g., field observations, othersatellite data) also deviates from the truth. The CIS’s data,however, provides extensive temporal and spatial coverage andincorporates human interpretation and integration of severaldata sources [11], [12].

II. DATA SETS

The data sets used in this paper cover the Gulf of St.Lawrence, Eastern Canada, during the winter of 2003 (January5 to March 25). Three data sets were used: 1) Radarsat imagesthat were used in the CIS operational analysis; 2) the digitalversion of the Radarsat image analysis from CIS; and 3) theice concentration retrieved from the coincident SSM/I datausing the NT2 and AES-York algorithms. The time differ-ence between the Radarsat and SSM/I acquisitions is typicallyunder 1 h.

A. Radarsat Images

Radarsat images were obtained from the ScanSAR widemode, which covers 500 × 500 km2 per scene, with 50-m pixelspacing (100-m spatial resolution). After the completion ofvisual analysis at CIS, images are archived on CD-ROMs. Thearchive version is a 2 × 2 block average of the original image[13], hence the pixel spacing is reduced to 100 m. Archiveimages were used in this paper after calibrating each image into16-bit backscatter coefficient values following the ScanSARcalibration scheme described in [14]. A total of 27 scenes wereused in this paper.

B. CIS Analysis

Radarsat image analysis at CIS is performed visually bytrained ice analysts. The analysis involves outlining a contouraround each area that, in the analyst’s view, encloses uniformice type distribution and concentrations. These areas are calledice analysis polygons (IAPs). The analyst encodes total ice con-centration, predominant ice types and ice type concentrationsfor each polygon into the standard WMO egg code. The outputis called “image-analysis chart.” In this paper, the retrievedice concentrations from AES-York and NT2 algorithms werecompared against output from those charts.

Fig. 1 shows a Radarsat image of the southeast area of theGulf of St. Lawrence, acquired on February 17, 2000, with thecorresponding image-analysis chart. The chart was producedfrom two successive image frames from the same orbit. The firstframe only is shown in the figure. The CIS image-analysis poly-gons and their relevant egg codes are included in the chart. The

Fig. 1. Radarsat image of (top) the southern area part of the Gulf of St.Lawrence, acquired on February 17, 2000 and (bottom) the corresponding CIS“image-analysis chart.” The chart was created from the above image and thefollowing image (not shown) along the same orbit. The “egg codes” at the rightside include the following information (from top to bottom of each “egg”):total ice concentration, ice type codes, concentration of each type, code forpredominant form of ice.

polygons color and egg codes contents are described in [15].The color code represents sea ice thicker than 15 cm withdifferent concentrations (blue, green, yellow, orange, and redcolors represent concentration values of less than one tenth,between one to three tenths, between four and six tenths,between seven and eight tenths, and finally between nine andten tenths, respectively). The black color represents fast ice,i.e., ten tenths of ice concentration. For the purpose of thisstudy, the IAP contours and the associated analysis resultswere reproduced in digital format. Details of this product areincluded in [16].

In comparing the results of CIS analysis against the al-gorithms’ output, the following factors that contribute to thediscrepancies should not be overlooked. First, visual analysisof Radarsat images is subjective; hence, there may be incon-sistencies in ice parameter estimates due to biases by differentanalysts. The assumption made in this paper is that thoseinconsistencies are averaged out as the number of analyzedimages increases. Second, for operational safety, the analysistends to produce conservative estimates in terms of total iceconcentration and thickness-based ice type [16]. Third, the iceconcentrations are produced at steps of one tenth, whereas the


Fig. 2. Scanning geometry of SSM/I showing footprints of a radiometricchannel (not to scale).

algorithms calculate the concentrations as floating point values.This factor alone accounts for 5% of the discrepancy. Fourth,ice analysts in CIS usually ignore small-scale features of typicalsizes equivalent to a few Radarsat pixels. This includes cracksin the ice cover, streaks of new ice, and melted and frozen bud-dle. Accumulation of such features, however, over a small IAPmay amount to a nontrivial area. Finally, an average IAP sizeis typically 5 to 15 times the SSM/I 37-GHz footprints. Whilethe entire IAP is assumed to be homogeneous with a certaincombination of ice types and their partial concentrations, theretrieval algorithm usually captures the heterogeneity withinthe IAP.

C. SSM/I Data

The SSM/I measures the radiation from the surface andthe atmosphere at three microwave frequencies: 19, 37, and85 GHz. It is a conically scanning radiometer with a 53◦

incidence angle looking backward. Hence, the scan lines arenonconcentric arcs, which tend to converge more toward theedge of the swath as the distance from the ground trackof the subsatellite point increases. Accordingly, more overlapof the footprints occurs near the end of each scan line (Fig. 2).The footprint from each radiometric channel is approximatedas an ellipse with minor and major axes, based on 3-dBbeamwidth size, as shown in Table I. Details of the geometriccharacteristics of the SSM/I radiometers and the method ofradiation integration over the footprint can be found in [17]. Theoutput ice concentration is assigned to the 37-GHz footprint.Justification for this choice is presented in Section V-D.

The sensor samples data at two different spatial samplingintervals the first is at 25 km (for the 19- and 37-GHz channels),and the second is at 12.5 km (for the 85-GHz channel). Atthese intervals a footprint from the 37-GHz channel is typicallyoverlapped by four or five neighboring footprints, while afootprint from the 19-GHz channel is overlapped by seven to

TABLE IGROUND DIMENSIONS OF THE FIELD OF VIEW

OF SSM/I SPECTRAL CHANNELS

nine neighboring footprints. Hence, in gridded SSM/I data suchas the 25 km × 25 km grid used in the NSIDC data product,each observation is actually an average of a number of neigh-boring pixels in the raw data. On the other hand, the samplingspacing in case of the 85-GHz channel is almost equal to thefootprint dimensions (13 km × 15 km), and therefore onlyslight overlap will occur between adjacent footprints in griddeddata. In this paper, nongridded SSM/I swath data, obtained fromthe Canadian Meteorological Center (CMC), were used, whichavoids uncertainties due to averaging and resampling. However,the different sizes of the footprints from different channelswere still ignored in this paper. Ice concentrations from theAES-York algorithm are incorporated in the SSM/I CMC datastream. Details of the SSM/I CMC data format and processingare given in [16].

III. SEA-ICE-CONCENTRATION ALGORITHMS

The NT2 algorithm [4] is similar to the original NT algorithm[2] in that both are empirical methods based on selection ofparameter space(s) in which ice is separated from water, andalso ice types are separable. Those parameters are ratios of theSSM/I observed brightness temperatures, which are largely in-dependent of ice-temperature variations, and thus eliminate theneed to account for ice-temperature variability both temporally(e.g., day to day and seasonal) and spatially (e.g., temperaturegradients across the examined region).

The algorithm also provides an improved sea-ice-concentration product through the resolution of a thirdice type needed to overcome the difficulty of complex surfaceeffects [7] and [18] on the emissivity of the horizontallypolarized component. This is called C-type ice. It is rather anice surface condition characterized by surface glaze, layering,and snow metamorphism. Under this surface condition, theoriginal NT algorithm underestimates ice concentration [7]. Tothis end, the NT2 algorithm makes use of the 85-GHz channels,in addition to the 19- and 37-GHz channels. That is mainlybecause the horizontally polarized 85-GHz data are much lessaffected by surface glaze and layering than the horizontallypolarized lower frequency data [19].

Five ratios are used in the algorithm: two polarization ra-tios (PR19, and PR85) and three gradient ratios (GR37V 19V ,GR85H19H , and GR85V 19V ). The polarization ratio at fre-quency f is defined as

PR(f) =TB(fV ) − TB(fH)TB(fV ) + TB(fH)

(1)

where TB is the observed brightness temperature and V and Hrefer to vertical and horizontal polarization, respectively.


A gradient ratio for frequencies f1 and f2 at any givenpolarization p is defined as

GRf1pf2p =TB

(f1p

)− TB

(f2p

)

TB

(f1p

)+ TB

(f2p

) . (2)

From the above five ratios, the algorithm uses three derivedparameters, defined as follows:

PRR(19) = − GR37V 19V sinφ19 + PR(19) cos φ19 (3)PRR(85) = − GR37V 19V sinφ85 + PR(85) cos φ85 (4)

∆GR = GR85H19H − GR85V 19V (5)

where PRR(19) and PRR(85) are called rotated polarizationratios with the angles φ19 and φ85 chosen so that the rotatedPRs are independent of first-year and multiyear ice [4]. Theparameter ∆GR is called gradient difference.

The use of the 85-GHz observations demand incorporationof a radiative transfer model to provide a weather-corrected iceconcentration product because of the sensitivity of the 85-GHzchannels to atmospheric effects. The forward atmospheric ra-diative transfer model developed by Kummerov [20] is used forthis purpose. Modeled brightness temperatures are calculatedusing different combinations of concentrations for the three icesurface types and open water in addition to 12 different at-mospheric conditions ranging from clear skies to fully cumuluscongestus clouds.

For each SSM/I observation point, the three parametersin (3)–(5) are calculated. GR37V 19V is used as an indicatorof presence of either bare thin ice or C-ice surface type. IfGR37V 19V > −0.01, then the SSM/I footprint is consideredto be composed of thick ice, thin ice, and open water. In thiscase, the search parameter space comprises PRR(19) andPRR(85), and GR37V 19V . On the other hand, if GR37V 19V <−0.01, then the SSM/I footprint is considered to be composedof thick ice, C-type ice, and open water. In this case, the searchparameter space comprises PRR(19), PRR(85), and ∆GR.Search is then conducted in the relevant parameter space todefine the model data vector, which is closest to the observationdata vector. The ice concentrations that engender that modelvector are the retrieved concentrations. Details of the calcula-tion procedures are included in [4] and [16]. The nonlinearityand weather correction scheme of the NT2 algorithm make itadvantageous to calculate ice concentrations from individualswath footprints.

Ice concentrations from the AES-York algorithm are cal-culated from the solution of a simple algebraic form of theradiative transfer equation

TB = εTse−τ + Tup + (1 − ε)Tdowne−τ + (1 − ε)Tspe−2τ

(6)

where TB is the observed brightness temperature; Ts and ε arethe physical temperature and emissivity, respectively, of theradiating layer; Tup and Tdown are the upwelling and down-welling atmospheric radiation, respectively; Tsp is the reflectedspace radiation; and τ is the atmospheric attenuation (opacity)coefficient. In the above equation, the space radiation term isneglected.

The algorithm uses two observations selected from the fourobservations (19 and 37 GHz, vertical and horizontal polar-

ization channels). It employs the assumption that the observedbrightness temperature can be decomposed into a linear combi-nation of brightness temperature engendered by two dominantice types “a” and “b” with concentrations Ca and Cb within theSSM/I footprint

TB = CaTB,a + CbTB,b + (1 − C)TB,w (7)

where TB,a, TB,b, and TB,w are typical brightness temperatures(tie point) from ice types “a” and “b” and open water “w,”respectively, and C is the total ice concentration (Ca + Cb).

Equations (6) and (7) are solved to determine the partialconcentrations of the two ice types. The technique is describedin more detail in [5] and [16]. It calls on different modules tocalculate ice concentrations under different weather conditions(wind and precipitation) as well as different spatial andregional variability of ice signature. Logical decisions, basedon empirical and theoretical results, invoke calculations ofeither one of two sets of ice types: first-year and thin ice,or first-year and multiyear ice. The algorithm also accountsfor the spatial and seasonal variability of typical brightnesstemperature from ice types (through selection of appropriatetie points), meteorological conditions that affect radiometricobservations, heterogeneity of ice types within a single SSM/Ifootprint, and presence of multiyear ice within the footprint.

IV. APPROACH

A novel data fusion technique is used to ensure optimumcomparison of SSM/I ice concentrations with the Radarsat dataand CIS analysis. Each elliptic SSM/I footprint is approximatedas a dodecagon, whose vertices are defined in terms of theirlatitude/longitude coordinates. The coordinates are calculatedfrom the SSM/I footprint center coordinates and the physicaldimensions of the footprint. A simple spherical earth surfacemodel was used for this purpose [21]. The SSM/I footprints andthe CIS analysis polygons are mapped onto the correspondingRadarsat image using a geomapping technique described in[16]. An example of the output from this technique is shownin Fig. 3, where SSM/I footprints are coded in colors thatrepresent the ice concentration output from NT2 (see theattached color bar), and overlaid on the coincident Radarsatimage. The figure shows one full CIS image-analysis polygon(IAP) with ten SSM/I footprints. Only footprints that were fullylocated within the IAPs were considered in the analysis. Theirtotal number of SSM/I pixels was 613. The time differencebetween the Radarsat and SSM/I overpasses was 30 min. TheNT2 and AES-York estimates of total, thick, and thin ice con-centrations for the footprints in Fig. 3 are included in Table IIalong with their deviation from CIS’s estimates of thin and thickice types (defined as ice of thickness less and greater than 15cm, respectively). Table II is a sample of the actual data set thatwas used in the quantitative analysis.

V. RESULTS AND DISCUSSIONS

A. Qualitative Observations From the Data Fusion Technique

A typical output of the data fusion of the retrieved iceconcentration is shown in Fig. 4. The figure shows part of aRadarsat image of the Gulf of St. Lawrence, acquired on March18, 2003, with CIS IAPs outlined in yellow contours. Only two


Fig. 3. Example of a CIS IAP (yellow boundary) with the SSM/I footprintsthat are fully located within the polygon (labeled by numbers). Radarsat andSSM/I data were acquired on February 12, 2003. Footprint colors represent iceconcentration from NT2, following the shown color bar. This subscene area isroughly 100 × 100 km2. The polygon area is typical of the CIS IAPs.

TABLE IIICE CONCENTRATION ESTIMATES FROM NT2 AND AES-YORK

ALGORITHMS FOR EACH SSM/I FOOTPRINT IN FIG. 3.THE ALGORITHM’S DEVIATIONS FROM CIS

ESTIMATES ARE ALSO INCLUDED

polygons (8 and 9), which are located at the ice edge, containthin ice types (codes 1 and 4). The rest, with exception ofpolygon 1, contain first-year ice types (thick, medium, and thin:codes 4, 1, and 7, respectively). The time difference betweenthe Radarsat and SSM/I acquisitions was only 5 min. Resultsof ice concentrations (total, thick, and thin) from the NT2 andAES-York algorithms are presented in the form of coloredSSM/I footprints (pertaining to the 37-GHz channel), wherewhite color means either zero ice concentration or the algorithmskipped the calculations (in case of AES-York algorithm only).The corresponding concentrations from CIS Radarsat analysisare presented in the form of colored polygons, where whitecolor represents fast ice. Thin and thick ice categories aredefined as ice of thickness less and greater than 15 cm, re-spectively (see justification in the next section). In areas where

SSM/I and CIS concentration have exactly the same value, thefootprint contour has the same color as the background CISconcentration so that it seems to disappear. This is the reasonfor the “disappearance” of the footprints in parts of the imagewhere both CIS’s and algorithm’s concentrations show 100%[Fig. 4(a)].

It can be seen from [Fig. 4(a)] that NT2 underestimates totalice concentration by 20% to 40% inside the pack ice and moreso near the ice edge (polygons 8, 9 and 10, and 11) relative toCIS estimates. AES-York estimates of total ice concentrationare in more agreement with the CIS estimates. When bothalgorithms underestimate the total ice concentration (as in caseof polygon 10) with respect to the CIS estimate, it is possiblethat the CIS estimate is exaggerated. This concern is addressedin more detail in [16].

Qualitatively speaking, the derived concentrations of thickand thin ice from the NT2 algorithm [Fig. 4(b)] are relativelyaccurate, especially within the pack ice (polygons 1–4). Incontrast, AES-York overestimates thin ice and underestimatesthick ice concentrations significantly. The total ice concentra-tion from the NT2 algorithm does not seem to be affected byland spillover as demonstrated by the uninterrupted distributionof the ice concentration values around Madeleine Island [whiteobject in upper left quarter of the colored image in Fig. 4(a)].The NT2 usually returns nonzero ice concentration values forwhat appears to be open water near the coastline or in thevicinity of the ice edge (see the lower left and the middleright parts of the image). Therefore, it appears that the iceedge definition from NT2 tend to be displaced toward theopen ocean. Explanation of this observation in terms of landspillover is presented in Section V-D. The AES-York algorithmignores ice concentration calculation if the footprint containsany land pixels. This explains the white footprints that includeland pixels from Madeleine Island. It has also been noticedthat the ice edge definition from the AES-York algorithm isdisplaced more into the pack ice. All of the above observationsare persistent throughout the entire data set.

B. Quantitative Analysis Results

All SSM/I pixels that had their footprints from the 37-GHzchannel fully located within an IAP in any of the 27 examinedimages were identified. Their total number was 613. The firststep was to plot the algorithms’ total ice concentration out-put versus the CIS image-analysis output (Fig. 5). The plotsshow that both algorithms underestimate total ice concentra-tion although AES-York demonstrates better agreement withCIS results. Both algorithms demonstrate high-scattered outputvalues (Table III). Part of this scattering is due to the fact thatthe SSM/I footprint can be significantly smaller than a CIS’sIAP area (Fig. 3) so that ice concentrations from individualSSM/I footprints may be different than the average ice concen-trations from the relevant polygon.

The definition of thin ice is not unequivocally settled inthe algorithms. Hence, two definitions were examined throughplots similar to Fig. 5; thin ice is less than 10-cm thickness(which comprises ice types 1 and 2 in the CIS egg code), andless than 15-cm thickness (which comprises ice types 1, 2,and 4). The two definitions were examined against all IAPsthat feature zero thin ice. When the first definition was adopted,


Fig. 4. (a) Radarsat image of the Gulf of St. Lawrence, acquired on March 18, 2003 (top). CIS image-analysis polygons and SSM/I 37-GHz footprints areoverlaid in colors that represent total ice concentration. White color footprints indicate no ice. White areas in the left images are land-fast ice. The table showsthe CIS’s egg code information for selected polygons. (b) Radarsat image with CIS image analysis and SSM/I footprints in colors that represent thick and thin iceconcentration. White cells mean open water or rejection of calculations due to presence of land (in case of AES-York algorithm only). AES-York and NT2 outputsare shown at the top and bottom images, respectively.


Fig. 5. CIS Radarsat analysis estimates versus the algorithms’ estimates oftotal, thin, and thick ice concentrations. Trend lines of AES-York and NT2are shown as dashed and dotted lines, respectively. The solid line denotesoptimal fit. Values of NT2 and AES-York have been shifted by ±1% relativeto the original value for better visibility. Thin ice is defined as ice of thickness< 15 cm.

each algorithm returned nonzero thin ice concentration in about400 footprints. When the second definition was used the num-ber dropped to roughly 60 cases. Hence, the threshold of15-cm thickness was found to be more appropriate for thethin ice definition in both algorithms as it brought the resultscloser to the CIS’s estimate when the latter is zero. It is worthnoting that this is not a physical-based definition; it is rather adefinition to suite the output from the examined algorithms.

Both algorithms fail to reproduce thin ice estimates thatagree with the CIS’s estimates. The failure is demonstratedby the negative slope of the trend line (AES-York) and thelarge scattering of the data (AES-York and NT2) in Fig. 5. Itappears that both algorithms have a problem in estimating thinor thick ice.

Results from this first phase analysis highlight the need toexamine the dependence of the retrieved ice concentration ofeach type on the ice type composition of the observed area.The rest of this section is designed to address this concern andto explain the large scattering of the data in Fig. 5. The fol-

TABLE IIIMEAN AND STANDARD DEVIATION OF THE ALGORITHMS’ DEVIATION

FROM CIS ESTIMATES (ALGORITHM MINUS CIS ESTIMATE)OBTAINED FROM ALL SSM/I OBSERVATIONS THAT HAVE

THEIR 37-GHz FOOTPRINTS FULLY LOCATED

WITHIN A SINGLE IAP BOUNDARY

lowing discussions are geared toward answering the followingquestions.

1) Is the algorithm’s output affected by the actual total iceconcentration?

2) Is the algorithm’s output affected by the relative concen-trations of thin and thick ice within the sensor’s footprint?And if so, then how is it affected?

3) What are the physical complexities of thin and thickice that hinder the retrieval of ice concentration frommicrowave data?

4) What is the effect of the larger 19-GHz footprint (whichis incorporated in the ice concentration calculations fromboth algorithms) on the presented results?

1) Effect of Total Ice Concentration on the Algorithms’Performance: Fig. 6 shows that the total ice concentrationfrom both algorithms is not affected by the actual total iceconcentration in the observed area. However, the accuracy ofthe retrieved thin ice concentrations deteriorates, in terms ofits average and variance, as the actual total ice concentrationincreases. For example, at 100% actual total ice concentration,NT2 underestimates thin ice concentration by 12% concentra-tion counts on average with ±35% variability. The AES-Yorkalgorithm overestimates thin ice concentration but with higherscattering. It should be noted that the ice composition (ratioof thick to thin ice) is not accounted for in these results (thiseffect is discussed in the next section). The retrieved thick iceconcentration, on the other hand, shows weak dependence onthe actual total ice concentration although higher scattering ofthe results are observed as the total ice concentration increases.It can be seen that the scattering of the results from NT2 is muchsmaller compared to AES-York results.2) Influence of the Relative Concentrations of Thin and

Thick Ice on the Algorithms’ Performance: The effect of icetype composition (relative fraction of thin and thick ice) on thealgorithms’ performances was examined using two approaches.In the first, the deviation of the algorithms’ estimates from theCIS estimates of concentration of each ice type is examined inrelation to the dominant ice type in the SSM/I footprint (thinor thick ice), taken from CIS estimates. The goal is to identifyconditions of thin and thick ice concentrations under which thealgorithm succeeds or fails. This approach uses statistics of thedeviation. The second approach utilizes analysis of variance(ANOVA) to determine the contribution of each ice type (thin,thick and their total concentration, as obtained from CIS data)to the variability of the derived concentration of each ice type.For example, it determines the contribution of the “true” thickice concentration on the retrieval of thin ice concentration.

Fig. 7 shows the deviation of the algorithms’ estimates ofthin ice concentrations from the corresponding CIS’s estimates


Fig. 6. Algorithm’s estimate minus CIS’s image-analysis estimate of total,thin, and thick ice concentrations versus the “true” total ice concentration fromCIS analysis.

(the algorithm’s estimate minus the CIS estimate) against therelevant SSM/I footprint number. The data are sorted by thetotal ice concentrations, then thin then thick ice concentrations.Footprints 1 to 362, for example, have 100% total ice concentra-tion, and within this group the data are sorted by thin then thickice concentrations. The CIS estimate of thin ice concentrationfrom each footprint is shown on the secondary vertical axis.

It can be seen that the deviation of the retrieved thin iceconcentration from the CIS estimates depends primarily onthe relative composition of thin and thick ice concentrationsin the observation area, and partially on total ice concentra-tion. Within the data points of 100% total ice concentrationboth algorithms underestimate thin ice when the true (CIS)thin ice concentration is higher than 50% (footprints 1–176).AES-York returns zero thin ice values in a few cases whenthe corresponding CIS estimate is 100%. When the CIS thinice concentration is between 40% and 60% both algorithmsreproduce more or less the same thin ice concentration as CISestimates. As thin ice concentration falls below 30% (footprintnumbers 240–360), both algorithms overestimate thin ice. Theoverestimation can reach 100% from the AES-York algorithmassigns 100% thin ice concentration to footprints that have nothin ice (footprints 300–360).

The above trend is repeated for the data from 90%total ice concentration (footprints 361–438) and 80% total iceconcentration (footprints 439–502) as indicated by the trendlines (solid black) in the figure. This cyclic trend indicatesthat thin ice concentration from each algorithm maintains thesame dependence on the difference between the actual thin andthick ice concentrations. The higher the difference between therelative concentrations of the two ice types, the more deviationof the algorithm’s estimate of thin ice from the “true” value.This situation is more likely to be encountered when the totalice concentration is high. That explains the gradual decay of thecyclic trend in the figure as the total ice concentration decreases.It can then be concluded that the algorithm’s estimate of thinice concentration is indirectly dependent on the actual total iceconcentration. The same observations, explanation, and conclu-sion apply to the retrieved thick ice concentration (the figureis not included) with one exception, i.e., the NT2 producesbetter estimates of thick ice than AES-York, particularly at thetwo extreme conditions when thin or thick ice dominates thefootprint area.

Fig. 8 shows the deviation of the retrieved ice concentrationfrom the corresponding CIS estimates against the differencebetween the actual concentration of thick and thin ice (from theCIS estimates). It can be seen that the retrieval of thick ice ismore successful than thin ice. The most successful retrieval forboth types is achieved when neither thin nor thick ice dominatesthe observation area. Once again, NT2 outperforms AES-Yorkalgorithm, particularly in retrieving thick ice concentration.

Statistics and trends of the algorithms’ deviation in deter-mining total, thin, and thick ice concentrations are presentedin Table IV. Thin ice is more problematic than thick ice. Whenthin ice predominate the scene, NT2 underestimates it by 69.4%percentage counts, overestimates thick ice by 31.4%, and un-derestimates total ice concentration by almost 38% percentagecounts. On the other hand, when thick ice predominate thescene, NT2 underestimates thick ice by 47.4%, overestimatesthin ice by 37.8%, but outputs a fairly correct estimate of thetotal ice concentration. Similar results are obtained from theAES-York algorithm (Table IV). This is an indication thatthe problem with thin ice resides in its radiometric behavior,which partly resembles open water (hence, the underestimationof total ice concentration when thin ice prevails). On theother hand, the problem with thick ice resides in either itsradiometric signature that resembles that of thin ice or thepresence of thin ice streaks within a matrix of thick ice ina relatively large SSM/I footprint (hence, the underestimationof thick ice concentration when it prevails). In conclusion,the predominance of thin ice deteriorates the retrieval of thin,thick, and total ice concentrations, while the predominance ofthick ice deteriorates only the retrieval of thin and thick iceconcentrations.

The mean values and the standard deviation of the algo-rithms’ deviation from CIS estimates (Table III) should ideallybe zero. In reality, large values are observed, especially of thestandard deviation of the retrieved thin and thick ice concen-trations. ANOVA was conducted to explain those large values.ANOVA quantifies the contribution of the actual concentrationof each ice type (thin, thick, and total) to the variability of theretrieved concentration of a given ice type. The independentvariables in this analysis are ice type concentrations from the


Fig. 7. Deviation of the algorithms’ estimate of thin ice concentration from the CIS estimates (left axis), and the CIS estimates of thin, thick, and total iceconcentration (right axis) both versus SSM/I footprint number (613 footprints). The CIS estimates are shown in color lines. The solid black lines are arbitrarytrend lines for the data within 100%, 90%, and 80% total ice concentrations.

Fig. 8. Algorithms’ deviation from CIS estimates of (a) thin ice and (b) thickice, plotted against the difference between CIS estimates of thick minus thinice concentrations. Values of NT2 and AES-York have been shifted by ±1%relative to the original value for better visibility.

CIS Radarsat image analysis, while the dependent variables arethe differences between the CIS estimate and the retrieved iceconcentration for each type. The ANOVA R-squared results(Table V) represent the percentage of the standard deviation(included in Table III) that can be accounted for by the presence

TABLE IVDEVIATION OF ALGORITHMS’ OUTPUT OF THIN, THICK, AND TOTAL ICE

CONCENTRATION FROM THE CORRESPONDING CIS RADARSAT

IMAGE-ANALYSIS ESTIMATES. DATA ARE FROM CASES OF

100% TOTAL ICE CONCENTRATION

TABLE VANOVA R-SQUARED VALUES THAT REPRESENT THE PERCENTAGE

PORTION OF THE VARIATION OF THE RETRIEVED ICE CONCENTRATION

DEVIATION (FROM CIS ESTIMATES), WHICH CAN BE ACCOUNTED

FOR BY THE PRESENCE OF TOTAL, THIN, AND THICK ICE. THE

VARIATIONS TO BE EXPLAINED ARE PRESENTED IN

TABLE III IN THE FORM OF STANDARD DEVIATION

of thin, thick, and total ice in an SSM/I footprint (i.e., by eachone of independent variable). The low R-squared values inthe first row of Table V suggest that total ice concentration inthe observed area (or equivalently the presence of open water)does not contribute to the variability of any of the retrieved


ice type concentration. On the other hand, the presence of thinice accounts for more variability in almost all of the retrievedice concentration (second row) than the presence of thick ice.This confirms that thin ice is more problematic than thick ice.Finally, thin and thick ice combined account for 62% and 31%of the variability in the retrieved thin and thick ice concentra-tion from NT2, respectively. A lower contribution means thatthe variability is caused by factors other than the examinedindependent variables. The most likely factors in this case arethe inconsistent estimates of ice concentrations from the CISanalysis, and the relatively small SSM/I footprint, compared tothe typical area of an IAP.

C. Physical Characteristics of Thin Ice and Thick Ice ThatHinder the Retrieval of Ice Concentration

The above discussions underline complexities of thin andthick sea-ice-concentration retrieval from microwave data. Thecomplex behavior of the radiometric emission from particularlythin sea ice was reported in previous studies [22]–[24]. The ob-served difficulty in retrieving thin ice concentration from SSM/Ican mainly be attributed to the difficulty of characterizing a thinice surface with a single radiometric value (a tie point).

Effects of thin ice surface conditions on microwave emis-sion were examined through an outdoor experiment of artifi-cial sea ice grown in a circular tank with a 7.5-m diameter[25]. Microwave emission was measured using a surface-basedradiometer operating at the same frequencies as of SSM/I.The ice thickness grew from 0 cm on December 22, 2001 to15 cm on January 15, 2002. Surface conditions (dry snow cover,freezing rain, wet and metamorphosed snow) were monitoredcontinuously as the microwave emission was sampled.

Fig. 9 shows plots of the GR37V 19V versus PR(19) andPR(85) from thin ice data (between 0–8 cm thickness). Ideally,the data points should form a cluster. The polarization ratiofrom thin ice is typically between 0.04 and 0.08. These valuesoccupy a range between the open water values (typical > 0.20)and thick ice (near zero). However, the data points in thefigure are widely scattered over the entire range of polarizationand gradient ratios. It is evident that more problems arisefrom using PR(19). Similar results were reported in [26]. Thisdemonstrates that microwave emission from thin ice cannot becharacterized by a single tie point, mainly due to the rapidand intensive change of the physical properties and surfacecomposition of thin ice. The radiometric evolution of the datadoes not show a direct correlation with either ice thicknessor surface temperature (not shown). Initial investigations haveshown that the observed variations in microwave signatures aremainly caused by the surface composition, which changes inresponse to atmospheric temperature and precipitation. We planto investigate this dependence in details in a future study. Nev-ertheless, the data in Fig. 9 demonstrate the complex nature ofsea ice at its early formation stage, and how this can lead to un-derestimation of the ice concentration from the NT2 algorithm.Also, the penetration depth of the lower frequency channels (19and 37 GHz) is relatively large so that subcentimeter-thick icebecomes indistinguishable from open water.

Thick ice is typically less saline, less sensitive to variationsin atmospheric temperatures, and hence more radiometricallystable than thin ice (mainly because the temperature gradient

Fig. 9. Distribution of radiometric parameters: PR(19) and PR(85) versusGR37V 19V obtained from artificial sea ice grown in an outdoor ice tank. Thedata were obtained from the onset of ice growth on December 22, 2001 untilice was 9 cm thick on December 31, 2001.

through its depth is much milder compared to thin ice). Duringwinter, rainfall or surface melt is less likely to occur over athick ice surface because this ice usually exists in regions ofcolder temperature. Therefore, the concept of using tie pointsto represent thick ice microwave behavior should be applicable,and the chance for successful retrieval of its concentration isbetter than that of thin ice (as long as the ice surface does notdevelop glaze due to freezing rain). This situation is different,though, from summer, when essentially the entire ice cover canexperience melting including the development of melt ponds,which results in highly variable complex microwave signatures.The apparent underestimation of thick ice concentration bythe two examined algorithms (when thick ice dominates thefootprint area) could be attributed to the presence of thin iceor open water patches within what appears to be 100% thickice cover in Radarsat images. Fig. 3 demonstrates this situation.The dark signature within footprints 4, 6, and 10, for example,represents either open or newly refrozen leads, which are notaccounted for in the CIS analysis. In conclusion, it shouldbe mentioned that when thin and thick ice coexist in equalconcentrations, the errors that are caused by domination of eachice type (as described above) cancel each other, rendering theretrieval of thin and thick ice concentrations successful.

D. Effect of the 19-GHz Footprints on the Derived Statistics

The above discussions and conclusions are based on theassumption that the output ice concentration from the algo-rithm can be assigned to the SSM/I 37-GHz footprint. Bothalgorithms however, use also observations from the 19-GHz


Fig. 10. SSM/I footprints from 19-GHz channel. Colors represent total iceconcentration from NT2. The nonzero ice concentration in the vicinity of thecoastline is not likely to be caused by land spillover.

channel, which has much larger footprint (see Table I). There-fore, the “true” resolution of the SSM/I-derived ice concen-trations is more a mix of these resolutions. In the following,we investigate how the larger footprint of the 19-GHz channelaffects the results.

Obviously, the effect can be significant for footprints in thevicinity of ice edge, coastline, or boarders between two IAPsthat feature different ice composition. Fig. 10 shows a fewfootprints of the 19-GHz channel, in colors that represent NT2ice concentration output, in the vicinity of the northern coastlineof Prince Edward Island in the Gulf of St. Lawrence. Thenonzero concentration values are not in agreement with thebackground signature of Radarsat, which suggests open water.Therefore, it can only be assumed that if these values are causedby land spillover, then it must have been generated from theantenna footprint beyond the 3-dB boundary. This conclusionis based on many similar observations in the current data set,either near coastline or in the vicinity of ice edge. It should benoted, however, that this assumption cannot be verified beyondthe above observations. The other possibility that land spilloverhas no effect on the results (Section V-A) is equally acceptable.

Recall that the statistics in Section V-B are obtained frompixels that have their 37-GHz footprints fully located withina given IAP. In order to asses the impact of the 19-GHzfootprints on these statistics, the subset of pixels of which the19-GHz footprint covers more than one IAP was identified.Ice concentration statistics from this subset are presented inTable VI. Comparison of the results in this table against resultsfrom the entire set (Table III) shows that the differences aregenerally insignificant for the total ice concentration from bothalgorithms and for thin and thick ice concentrations from NT2only. However, the difference in thin and thick ice concentrationestimates from AES-York algorithm is more affected by the ob-servations from the 19-GHz footprint (fourth and sixth columnsin Tables III and VI).

VI. CONCLUSION

Ice concentration results from the operational analysis ofRadarsat images (obtained from the CIS) were compared tothe ice concentration output from two retrieval algorithms: the

TABLE VIMEAN AND STANDARD DEVIATION OF THE ALGORITHMS’ DEVIATION

FROM CIS ESTIMATES (ALGORITHM MINUS CIS ESTIMATE) OBTAINED

FROM A SUBSET OF SSM/I OBSERVATIONS THAT HAVE THEIR 19-GHzFOOTPRINTS EXTENDING OVER MORE THAN ONE CIS IAP

NT2 and the AES-York. A novel data fusion technique was usedto perform the comparison at the individual SSM/I pixel level,taking into account the actual footprint of the sensor. Twenty-seven Radarsat images were used along with their coincidentSSM/I overpasses during the winter of 2003, which resulted ina total of 613 SSM/I footprints available for this paper.

The NT2 algorithm underestimates total ice concentrationby an average of 18% concentration percentage differencewith respect to the CIS estimates with a standard deviationof 17%. The AES-York algorithm produces better agreement(underestimation by 13%). The retrieval of thin and thick iceconcentration is less accurate from both algorithms, althoughthe NT2 shows better performance in this regard.

The retrieved ice concentration from each algorithm dependson the actual ice composition within the SSM/I footprint. Ifthin ice dominates the footprint area, then both algorithmsunderestimate thin ice concentrations with respect to the CISestimates and accordingly overestimate thick ice concentration.On the other hand, if thick ice dominates the footprint area,then both algorithms underestimate thick ice concentration andaccordingly overestimate thin ice concentration. The actualthin and thick ice concentrations account for 62% and 31%of the variability of thick and thin ice estimates from theNT2 algorithm, respectively, and for 63% and 53% for theAES-York algorithm, respectively. The most accurate retrievalof thin and thick ice concentrations was observed when thefootprint comprised a balanced mixture of thin and thick iceregardless of the total ice concentration. This is more likelyto happen at lower total ice concentrations because, except forlarge polynyas, consolidated sea-ice areas consist normally ofmostly thicker ice (greater than about 15 cm).

Under prevalence of thick ice, both algorithms fail to esti-mate thick and thin ice concentrations correctly; yet the totalice concentration estimate remains fairly accurate. On the otherhand, under prevalence of thin ice, the algorithms fail to esti-mate thin, thick, and total ice concentrations.

Thin ice is more problematic than thick ice. Both algorithmsdo not account for variations in thin ice surface emissivity andits sensitivity to ambient temperature or precipitation. Hence,they produce highly scattered estimates of thin ice concentra-tions. The concept of using predetermined tie points of a thinice surface is not realistic due to the wide range of microwaveemission from that surface.

For operational purposes, AES-York algorithm may be usedto estimate total ice concentrations, albeit its tendency to dis-place the ice edge more into the pack ice. On the other hand,NT2 should be used to determine both total ice and thick iceconcentrations. None of these two algorithms is appropriate fordetermining thin ice concentration.


These conclusions are based on the limited data set used inthis paper. Conclusions may be different in different seasons orregions that may be typified by different ice surface conditions.The CIS analysis results, though comprehensive about iceconditions under different weather situations, do not necessarilyfurnish a “truth” data set.

It is recommended to extend this paper to cover differentareas, algorithms, and microwave sensors of finer resolution. Anew concept for thin ice concentration retrieval, which accountsfor the wide range of surface-engendered microwave emission,should be developed. This comparison serves to improve re-sults from the operational ice analysis as well as the retrievalalgorithms.

ACKNOWLEDGMENT

The author wishes to acknowledge the contribution ofA. Tavakoli, a coop student from the University of Waterloo, incoding the technique into a Windows application. The supportof the CIS in developing and providing the digital version ofthe Radarsat image analysis is also appreciated. The CMCfacilitated access to the SSM/I data and assisted in solving a fewprogramming issues. The excellent comments by anonymousreviewers are also gratefully acknowledged.

REFERENCES

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[25] M. E. Shokr and K. Asmus, “Microwave emission from artificial sea ice:The ice tank 1998/1999 experiment,” Can. Ice Services, Ottawa, ON,Canada, Tech. Rep. CIS-0201, 2002.

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Mohammed Shokr (M’99) received the Ph.D. de-gree in aeronautical engineering from Cairo Univer-sity, Cairo, Egypt, in 1980.

Since 1982, he has been with EnvironmentCanada, Toronto, ON, Canada. In 1989, he startedhis research career on remote sensing of sea iceto support the national operational ice monitoringprogram in Canada. He has participated in severalfield campaigns to study sea ice physical and electri-cal properties in relation to its radiometric signaturefrom different sensors. His current research interest

includes remote sensing data fusion to enhance quantitative retrieval of iceparameters, algorithm development from passive and active remote sensingdata, and data assimilation for sea ice.

Thorsten Markus (M’05) received the M.S. andPh.D. degrees in physics from the University ofBremen, Bremen, Germany, in 1992 and 1995,respectively.

He was a National Research Council ResidentResearch Associate with the NASA Goddard SpaceFlight Center (GSFC) from 1995 to 1996 beforejoining the NASA–UMBC Joint Center for EarthSystems Technology, where he worked until 2002.He is currently a Research Scientist with the GSFC,Greenbelt, MD. His research interests include satel-

lite microwave remote sensing primarily of ice and the utilization of satellitedata to study oceanic and atmospheric processes.

Dr. Markus is member of the American Geophysical Union.

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