evaluation of the modis (mod10a1) daily snow …research.bpcrc.osu.edu/~jbox/pubs/stroeve et al rse...

nt 105 (2006) 155–171www.elsevier.com/locate/rse

Remote Sensing of Environme

Evaluation of the MODIS (MOD10A1) daily snow albedo productover the Greenland ice sheet

Julienne C. Stroeve a,⁎, Jason E. Box b, Terry Haran a

a National Snow and Ice Data Center (NSIDC), Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado,Boulder, CO 80309-0449, USA

b Atmospheric Science Program, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio, USA

Received 23 January 2006; received in revised form 14 June 2006; accepted 19 June 2006

Abstract

This study evaluates the performance of the beta-test MODIS (MOD10A1) daily albedo product using in situ data collected in Greenlandduring summer 2004. Results indicate the beta-test product tracks the general seasonal variability in albedo but exhibits significant more temporalvariability than observed at the stations. This may indicate problems with the cloud detection algorithm, and/or failure of the BRDF model toadequately model the bidirectional reflectance of snow. Comparisons with in situ observations at five automatic weather stations in Greenlandindicate an overall RMSE of 0.067 for the Terra instrument and an RMSE of 0.075 on Aqua. The Terra-retrieved-albedo are slightly bettercorrelated with the in situ data than the Aqua retrievals (r=0.79 versus r=0.77). Comparisons were also made between the MODIS daily albedoproduct and the MODIS 16-day albedo product (MOD43B3). Results indicate general correspondence between the two products, with betteragreement found using the Terra-retrieved-albedo than the Aqua-retrieved albedo. The reason for the differences in albedo between the Aqua andTerra satellites remains unclear. At the stations examined, both the Terra and Aqua retrievals were made at nearly the same time of the day andtherefore the differences in albedo between the satellites cannot be explained by differences in solar illumination. Finally, the albedo derived usingMODIS data and the direct estimation algorithm (DEA) was also compared with 2004 Greenland in situ data. Results from this comparisonsuggest that the DEA performs well as long as the solar zenith angle of the observation is not greater than 70°.© 2006 Elsevier Inc. All rights reserved.

Keywords: Remote sensing; Snow and ice; MODIS; Greenland ice sheet

1. Introduction

Surface albedo is an important climate parameter as it governsthe amount of absorbed solar energy. It is especially important tomonitor albedo in polar regions where much of the year, the highalbedo of snow and ice surfaces reflects most of the incomingsolar radiation. Yet, decreases in albedo during periods of meltingallow substantially more solar energy to be absorbed, amplifyingthe sensitivity of surface melt to warming. It is believed that thispositive feedback process is currently amplifying the effects of ageneral rise in Arctic temperatures (e.g. Lindsay & Zhang, 2005;Overpeck et al., 2005; Stroeve, Serreze et al., 2005).

Routine visible satellite observations of the polar regionsbegan in 1972 with the launch of the first Landsat. Since then, a

⁎ Corresponding author.E-mail address: [email protected] (J.C. Stroeve).

0034-4257/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.rse.2006.06.009

wide range of optical-wavelength sensors have been launched thathave allowed for long-term observations of snow albedo. TheNOAA/Advanced Very High Resolution Radiometer (AVHRR)sensor provides the longest time-series of surface albedoobservations currently available. The AVHRR Polar Pathfinder(APP) (Fowler et al., 2000) product is available from the NationalSnow and IceDataCenter (http://nsidc.org), providing twice dailyobservations of surface albedo for the Arctic and Antarctic fromAVHRR spanning July 1981 to December 2000. The accuracy ofthe APP-derived albedo over snow and ice surfaces in Greenlandhas been previously estimated to be approximately 6% (Stroeveet al., 2001). In December 1999, the Moderate Imaging Spectro-radiometer (MODIS) sensor was launched on the Terra AMplatform, followed inMay 2002 by the launch of a nearly identicalMODIS sensor on the Aqua PM platform.With its higher spectralresolution (36 spectral bands compared to 5 on AVHRR), higherspatial resolution, and improved cloud detection capabilities, it

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156 J.C. Stroeve et al. / Remote Sensing of Environment 105 (2006) 155–171

was anticipated that the accuracy of snow albedo derived fromsatellite would significantly improve.

A prototype snow albedo algorithm for theMODIS instrumentwas developed by Klein and Stroeve (2002). Beginning inSeptember 2003, this algorithm was incorporated into the routineprocessing of Terra and Aqua MODIS snow products (i.e.MOD10A1 and MYD10A1, respectively, referred to herecollectively as MxD10A1). Since the product has not beenvalidated, it is considered a beta-test product and therefore maystill contain significant unsubstantiated errors. Another albedoproduct available from MODIS is the 16-day albedo product(MOD43 (Terra)/MCD43 (Aqua plus Terra) (Schaaf et al., 2002)that has previously been validated over the Greenland ice sheet(Stroeve, Box et al., 2005). This product was found to have anaverage RMS error of 0.07 in comparison with in situmeasurements collected over the Greenland ice sheet, consideringall available MOD43 albedo estimates, that is, when includingalbedo retrievals made with both the primary and secondary“backup”MOD43 albedo algorithms.MOD43 snow albedoRMSerror was determined to be 0.04 when using MOD43 albedoretrievals based on the “primary” algorithm only, that is, thehighest quality retrievals. The temporal resolution of the 16-dayproduct is however insufficient to precisely monitor importantsurface processes, such as: the onset of surface melt; rain on snowevents; wind sculpting of the surface; and surface hoar frostdeposition.

Since there is minimal validation of the MxD10A1 dailysnow albedo product, validation work is needed to access theusefulness of the product for science applications. This paperaims to give users of MODIS snow albedo products insight intothe accuracy of the MxD10A1 daily snow albedo productthrough comparisons with ground measurements of surfacealbedo available from several Greenland Climate NetworkAutomatic Weather Stations (AWS) (Steffen & Box, 2001;Steffen et al., 1996) as in previous studies (Liang et al., 2005;Stroeve et al., 2001, 2005). As an additional comparison, theMODIS MxD10A1 daily albedo product is compared with thealready validated MODIS MOD43 and MCD4316-day albedoproducts as well as the snow albedo retrieved from applying anew snow albedo algorithm, the direct estimation algorithm(DEA) of Liang et al. (2005). These comparisons improve ourunderstanding of the accuracy of the MxD10A1 albedo productand will help determine the applicability of the various MODISsnow albedo products for particular research problems.

2. Study area

The Greenland ice sheet has been used in several previousstudies for accuracy assessment of snow and ice albedo mea-surements from satellite (e.g. Greuell and Oerlemans, 2005; Knapand Oerlemans, 1996; Liang et al., 2005; Stroeve et al., 2001,2005, 1997). The ice sheet is a good target for validation studiesbecause: (1) several albedo monitoring sites are in operationaround the ice sheet in different snow zones; (2) the relativelyhomogenous surface of the ice sheet allows for comparisonsbetween the area viewed by the satellite and the much smallerAWS measurement footprint; and (3) the ice sheet has a less

cloudy environment than elsewhere in the Arctic providing moredata for comparisons. The AWS albedo measurements have beendescribed in earlier validation papers (Liang et al., 2005; Stroeveet al., 2001; Stroeve, Box et al., 2005). Therefore, a brief updatedsummary of the study area and instrumentation is given here.

The GC-Net AWS are equipped with LI-COR shortwave 200SZ pyranometers that measure the downward and upward solarenergy in a narrower spectral range (0.4–1.1 μm) than is repre-sented in the MODIS daily snow albedo product (0.3–3.0 μm). Inorder to use these data for comparisonswith theMxD10A1 albedoproduct, a correction was applied to the AWSdata to convert themto an equivalent broadband albedo. The accuracy of daily in situalbedo observations after applying this correction is estimated tobe 0.035. Therefore, differences between the MODIS and in situobservations exceeding 0.035 will be interpreted here as havingnoteworthy statistical significance.

The instrument footprint varies with instrument height.Instrument height varies in response to snow accumulation andsurface ablation. Instrument heights vary from 1.5 m to 4.5 m.Given that most of the footprint comes from ±30° from nadir, thefootprint size varies from 1.7 m2 to 5.2 m2, respectively. Theimpact of this change depends on the length scales of surfaceroughness elements, for which we have no detailed surveys.However, the importance of surface patchiness is discussed ininterpreting the AWS to satellite comparisons.

For this study, we have quality controlled surface albedomeasurements at 5 different AWS sites during spring and summer2004 available for comparison with the MODIS observations.Fig. 1 shows the location of the AWS sites used in this study. Twoof the stations (JAR1 and JAR2) are located in the so-called“ablation region”, where large seasonal variations in albedo occurfrom seasonal snow accumulation followed by intense summersurface melting. The other three sites (Crawford Point-1 (CP-1),Summit, and DYE-2) are located at higher elevations, in theaccumulation region of the ice sheet wheremelting is limited (CP-1 and DYE-2) to near zero melting (Summit) and therefore do notexhibit large seasonal albedo variations. The time-period used forcomparison in this study covers pre-melt times from 29 March(day 89) 2004 through the melt season to the autumn freeze thatoccurs before 30 September (day 274) 2004. The coordinates ofeach AWS are known to within 100 m. The coordinates of sitesthat move significantly with glacier flow (e.g. JAR1 and JAR2)(<100m/year) were updated to reflect positions in 2004. Thus, thegeolocation accuracy of all sites is better than 100 m.

Hourly mean downward shortwave irradiance measured at theAWS are compared with clear-sky irradiance calculated using aradiative transfer model as described in Box (1997a) and Stroeve,Box et al. (2005) to determinewhich days are essentially cloud free.Clear-sky conditions are defined by an effective sky transmission(TE) value (ratio of the measured to modeled clear-sky downwardsolar radiation) greater than 0.8. This threshold serves to removeoptically thick cloudy cases. As the TE parameter does not discri-minate between clear and cloudy conditions in cases of extremediffuse sky light, frommultiple scattering between high albedo dry-snow surfaces and clouds, visual inspection of image data is thenused to identify cloudy cases based on image texture information.The parameter TE performs best in regions where melting takes

Fig. 1. Map of the Greenland ice sheet showing the five automatic weather stations used to assess the accuracy of the MODIS albedo products.

157J.C. Stroeve et al. / Remote Sensing of Environment 105 (2006) 155–171

place, that is, where melt produces significant albedo reductions,minimizing the increase in downward shortwave irradiance frommultiple scattering between the surface and diffuse clouds.

For the following comparisons, daily integrated albedo (Ai)is used instead of selecting the hourly-mean albedo measuredclosest in time to the satellite overpass. Ai is computed as:

Ai ¼X

Sz=X

SA

where S↑ is the upward shortwave irradiance and S↓ is thedownward shortwave irradiance, both summed daily using 24hourly averages. Ai is less sensitive to instrument level and cosineresponse errors since it integrates errors that partly cancel eachother. We have reviewed information on instrument levelingrecorded during annual site visits and only use AWS-observedalbedo values for time-periods when the leveling errors are lessthan 3°. Errors are further minimized by theAi integration. Hourlyalbedo ismore susceptible to the daily and seasonal albedo “smile”that is more the result in cosine response error than an actual daily

cycle in snow metamorphosis effects. We assume that the diurnalvariability in albedo is represented by the daily integrated valueand sub-diurnal variability is otherwise insignificant. However, Aimay only partially represent sub-diurnal variability, such as onsetof melt or otherwise when large diurnal albedo variability occurs.In any case, the instruments usually lack the leveling certainty toreliably gauge sub-diurnal albedo variability.

3. MODIS snow albedo products

3.1. Daily product (MxD10A1)

This study makes use of the MODIS Terra and Aqua snowcover daily L3 global 500mgridded products [MOD10A1 (Terra)andMYD10A1(Aqua)] available from the National Snow and IceData Center (NSIDC) (Hall et al., 2002). These data are in HDF-EOS format and gridded in a sinusoidal map projection. Albedo isstored as integer type array with values ranging from 0 to 100 (%)and is accompanied by quality control flags.

Table 1Input products used in the production of the MODIS (MOD10A1 andMYD10A1) daily snow albedo products

Input product Description/comments

MOD09GHK/MYD09GHK

MODIS/Terra/Aqua Surface Reflectance Daily L2GGlobal 500 m SIN Grid — Atmospherically correctedMODIS surface reflectances for 7 reflectance channelsfor each observation. These data are the primary input tothe albedo algorithm. However, since MODIS Aqua band6 had many inoperable detectors the snow albedo iscomputed using bands 1–5 and band 7.

MOD10L2G/MYD10L2G

MODIS/Terra/Aqua Snow Cover Daily L2G Global500 m SIN Grid — Surface type, including snow cover,bare land, ocean, cloud, etc., for each observation. Notethat the MOD10L2G and MYD10L2G products are notarchived and are not available for distribution. They existonly as temporary files within the MODIS DataProcessing System (MODAPS).

MODMGGAD/MYDMGGAD

MODIS/Terra/Aqua Geolocation Angles Daily L2GGlobal 1 km SIN Grid Day — Solar and sensor zenithand azimuth angles for each observation. These data areused to adjust the MODIS surface reflectance values foranisotropic scattering effects, and to determine whichsnow covered observation in MOD10L2G/MYD10L2Gis the best to use for computing the albedo.

GTOPO30 The Global 30-Arc Second Digital Elevation Data Set(GTOPO30) — This information provides slope andaspect information to correct for the tilt of the surface.

MOD12Q1 MODIS/Terra Land Cover Type 96-Day L3 Global —Land cover information produced by MODIS Terrain 2000.

MODPTHKM/MYDPTHKM

MODIS/Terra/Aqua Observation Pointers Daily L2GGlobal 500 m SIN Grid — Contains information used tolocate individual observations in the correspondingMOD09GHK/MYD09GHK, MOD10L2G/MYD10L2G,and MODMGGAD/MYDMGGAD product files.

MOD35 MODIS cloud mask used to flag which pixels are clearand which are cloudy. A value for the surface albedo isonly given in the pixel is determined to be clear by theMOD35 cloud mask.

MODIS BRDFLook-up tables

Used to compute a BRDF correction factor from the solarzenith, sensor zenith, and relative azimuth angles togetherwith the slope and aspect values.


The Prototype MODIS snow albedo algorithm builds uponprevious efforts to derive snow albedo from satellites (e.g. Knap,1997; Knap & Oerlemans, 1996; Stroeve et al., 1997). Initialdevelopment of the algorithm was accomplished using MODISAirborne Simulator (MAS) data acquired in Wisconsin as part ofthe Winter Cloud Experiment (WINCE). After the MODIS databecame available, some limited validation of the algorithm wasperformed using upwelling and downwelling radiation data fromthe SURFRAD site in Fort Peck, Montana. Data acquired duringNovember 2000 showed that the prototype daily MODISalgorithm produced reasonable broadband albedo estimateswith maximum daily differences of 15% (Klein & Stroeve,2002). Since these initial development and validation efforts, nofurther studies to examine the performance of the algorithm asapplied to MODIS imagery have been performed.

TheMODIS daily snow albedo algorithm computes the albedoonly for areas identified as cloud-free by the MODIS cloud mask(MOD35) and as snow-covered by the MODIS snow algorithm.When a pixel meets these criteria, atmospherically correctedsurface reflectances are retrieved from the MODIS/Terra SurfaceReflectance Daily L2GGlobal 500m SINGrid product, availablefrom the Land Processes (LP) Distributive Active Archive Center(DAAC). Table 1 summarizes the various input products used inthe production of the MODIS daily snow product including theMODIS daily snow albedo parameter.

Since snow albedo can change substantially from day to dayin the ablation region of the Greenland ice sheet, comparisonsare made only on days for which we have coincident clear-skysatellite and in situ observations. The AWS data are used toindicate whether or not an observation is clear, as discussed inthe previous section. MxD10A1 pixels determined to be cloudyhave a flag value of 150. Any impossible albedo values, such asalbedo=100%, are discarded from the analysis. Snow albedorarely exceeds 90%. However, during times of high solar zenithangles (SZAs) and in conditions of optically-thin clouds, snowsurface albedo in excess of 90% has been measured. Toconserve as much of the daily albedo retrievals, we did notdiscard albedo data less than 100%, rather we allow comparisonstatistics with ground observations to determine overall errors.

Instances of albedo values equal to 100% were found for theAqua (MYD10A1) data 2.4% out of 163 observations and 1.3%out of 158 observations in the case of Terra (MOD10A1) data. Itis important to keep in mind that the MODIS daily albedoproduct is not a daily-averaged albedo. Rather, it is the “best”single observation in a day. It is termed “daily” in that an albedoestimate is produced once per day and represents a synopticsnapshot. The “best” pixel is selected based on having the bestscore which is based on clouds, and viewing and illuminationangles. Determining the time of acquisition of each MOD10A1or MYD10A1 pixel is complicated and unfortunately is not partof the information stored in the albedo grid.

Fig. 2 shows an example of the surface albedo fromMOD10A1 on 21 June 2004 that includes the Greenland icesheet. Snow free land is shown in brown while clouds are shownin white. Melt has already started along the coastal regions asindicated by the areas with relatively low albedo values. Regionsof high surface albedo are visible in southern and central-west

Greenland, with albedo values that approach or exceed 90%. Inthe comparisons that follow, we scaled the albedo values providedin the albedo array from 0–1.0.

3.2. 16-day product (MxD43)

The 16-day MODIS albedo product has been described indetail Schaaf et al. (2002) and was used in Stroeve, Box et al.(2005) to assess its performance over the Greenland ice sheet. It isclassified as “Validated Stage-1 Accuracy” which implies thatalthough the product accuracy has been assessed by comparisonstudies, users are still urged to use the band specific quality flagsto isolate the highest quality full inversion results for their ownscience applications. Here we use the MOD43B3 (Terra) andMCD43B3 (combined Terra and Aqua) albedo products(collectively referred to here as MxD43) that provide both thedirectional hemispherical reflectance (“black-sky albedo”) andthe bihemispherical reflectance (“white-sky albedo”). The black-sky albedo (BSA) is defined as albedo in the absence of a diffuse

Fig. 2. MODIS image from MOD10A1 on 21 June, 2004 in the sinusoidal map projection. The scale of the albedo from 0–100% is included. Values in pale greyrepresent open water. Brown corresponds to snow-free land, darker gray indicates areas where there is a landmask mismatch (found along the coastlines) caused bydisagreement between the slope and aspect data used and the land/water mask carried in the sequence of MODIS products, white are clouds and pale blue are no data(tiles outside Greenland ice sheet not included).


component and is a function of solar zenith angle whereas thewhite-sky albedo (WSA) is the albedo in the absence of a directsolar beam component when the diffuse component is isotropic.The actual albedo is a linear combination of the black- and white-sky albedo and thus depends on the particular atmosphericconditions under which the observation is made. However, at thelocal solar noon zenith angles typically encountered duringsummer in Greenland, the two albedo are nearly identical over theGreenland ice sheet (they are identical at approximately 50°(Stroeve, Box et al., 2005).

The standard MODIS 16-day albedo algorithmmakes use of a“kernel-driven” linear Bidirectional Reflectance DistributionFunction (BRDF) model to describe the anisotropy of surfacereflectance (see Schaaf et al. (2002) for more detail). For locationswhere a full BRDF model cannot be accurately retrieved (e.g.when there is lack of sufficient MODIS Terra and/or Terra plusAqua observations available), a “backup algorithm” that uses adatabase of archetypal BRDF models is used. The backupalgorithm is based on a land cover classification and a data base ofhigh quality MODIS full inversion BRDF retrievals from aprevious year to estimate the surface BRDF. Retrievals using thebackup algorithm are flagged as ‘low quality’ results compared tofull inversion retrievals, but there are instances where the backupalgorithm performs as well as the main (e.g. full inversion)algorithm (e.g. Jin, Schaaf, Gao et al., 2003; Jin, Schaaf,Woodcock et al., 2003; Stroeve, Box et al., 2005).

Based on previous validation efforts (e.g. Jin, Schaaf, Gao etal., 2003; Jin, Schaaf, Woodcock et al., 2003; Liang et al., 2002;Stroeve, Box et al., 2005; Wang et al., 2004), the MxD43B3

albedo product has been assigned a Validated Stage-1 accuracy.Using the full inversion (i.e. the main albedo algorithm) resultsin snow albedo accuracies within 5% while the majority of thebackup magnitude inversions have a lower accuracy, between8–11%.

3.3. Direct estimation algorithm

Recently, an improved version of the direct estimationalgorithm (DEA) for computation of snow albedo from MODISwas developed by Liang et al. (2005). Instead of performingseparate computations for atmospheric correction of the satelliteradiances, correction for the anisotropic reflection of the snowsurface and conversion from satellite narrowband reflectances to abroadband albedo, as is done in the standardMODIS snow albedoproducts, the direct estimation algorithm combines all threeprocedures in a single calculation. DEA links the top-of-the-atmosphere (TOA) observations from MODIS, which containsinformation on both the surface reflectance and the atmosphericoptical properties directly to the surface albedo through statisticalregression. Since the standard MODIS albedo algorithms arebased on a physical understanding of atmospheric and surfaceprocesses, the albedo depends on the performance of all theprocedures that characterize the known processes. Thus, inac-curacies of the atmospheric correction and of the angular modelused to describe the directional distribution of the reflectance canlead to large errors in the retrieved surface albedo (although theerrors in each procedure may also at times cancel). DEA attemptsto simplify the procedure and hopefully reduce errors by


estimating only the surface broadband albedo rather than all theindividual variables involved (e.g. aerosol, surface BRDF, etc.).DEA was applied to MODIS Level 1B swath data. The Terraorbits that typically cover the entire Greenland ice sheet occurbetween 1300 and 1800 GMT. Comparisons with a limited set ofGreenland AWS measurements showed that this algorithmproduces accurate daily snow/ice albedo with mean biases ofless than 0.02 and a residual standard error of 0.04 (Liang et al.,2005). This study provides more stations and observations foraccuracy assessment.

4. Results

4.1. Daily albedo

4.1.1. High elevation dry snow regionAt Summit, the AWS-observed albedo is nearly constant

over time, with a mean daily albedo of 0.84±0.013 (Fig. 3). TheMODIS Terra (MOD10A1) and Aqua (MYD10A1) dailyproducts exhibit more albedo variability than the AWS dataand at times return albedo values exceeding 0.9, particularlyearly in the year when sun angles are low. For example, onMarch 30th (day 90; SZA=72°), the Terra albedo was 0.95 andthe Aqua albedo was 0.99. The same problem was previouslynoted when evaluating algorithm performance of the MODIS16-day albedo product (Stroeve, Box et al., 2005). When theSZA is high (e.g. greater than 70°), the inputs into the both thedaily and 16-day MODIS albedo algorithms tends to be of lowerquality (see Stroeve, Box et al., 2005) because as zenith angleincreases, the plane-parallel assumption used in the atmosphericcorrection is increasingly compromised. In the case of theMxD43 albedo products, this assumption results in morefrequent use of the backup algorithm, leading to less accuratealbedo retrievals. In the case of the MxD10A1 albedo products,errors in the atmospheric radiative transfer code and the BRDFsincrease with increasing SZA, reducing the accuracy of theinput data into the algorithm. Nevertheless, on average, theMODIS daily albedo retrievals are in reasonable agreement withthose from the AWS. The mean Terra MODIS albedo is 0.85

Fig. 3. Surface albedo at Summit (72.5794° N, 38.5042° W) f

±0.036, and the RMSE is 0.035. For Aqua, the mean albedo is0.84±0.038 and the RMSE is 0.037.

The 16-day albedo product (MxD43) tracks the dailyvariability in the AWS albedo (except early in the year whenhigher SZAs result in large errors in the retrieved albedo). Note, inFig. 3 we only show the results for the BSA from the combinedTerra and Aqua instruments (i.e. from the MCD43 product), butresults do not differ much for the white-sky albedo (e.g. meanBSA andWSA are 0.83 during this time-period) nor for retrievalsfrom the Terra instrument (i.e. from the MOD43 product) alone.More detailed comparisons between the MxD10A1 and MxD43albedo products will be discussed in the next section.

Results from the DEA show a systematic underestimation ofthe in situ albedo by approximately 0.08 (mean DEA albedo is0.80±0.030). This underestimation is dominated by low albedovalues duringApril and likely reflects problemswith the ability ofthe algorithm to accurately retrieve albedo for observations madeunder relatively high SZAs. Liang et al. (2005) excluded datafromApril. Ifwe exclude values duringApril, themean differencebetween the AWS and DEA-retrieved albedo is −0.05 (RMSE of0.028). Liang et al. (2005) reported anRMSEof 0.036 and ameandifference of less than 0.01 at Summit using data obtained in2002, in which comparisons were made between hourly AWSmeasurements closest in time to the Terra orbit (typically aMODIS Terra orbit at around 1500 GMT).

Both CP-1 and DYE-2 are located near 2000 m a.s.l. Resultsat these two stations are shown in Figs. 4 and 5, respectively. AtCP-1, the daily MODIS albedo product tends to match the AWSdata early in the year (after day 105 and before day 130).However, both the MODIS and AWS retrievals exhibit a slightlypositive bias in April. Mean April albedo from the AWS is 0.86±0.016, whereas that from MOD10A1 and MYD10A1 is 0.88±0.034 and 0.89±0.040, respectively. We expect that dry snowhas an albedo of around 0.84 (Konzelmann & Ohmura, 1995). Itmay be possible to slightly exceed an albedo of 0.90 duringcloudy sky conditions, not because of changing surfaceproperties, but owing to a change in the spectral distribution ofenergy and a non-uniform spectral reflectance of snow, i.e., lownear-infrared snow reflectance. However, since we are limiting

rom 29 March (day 89) through 14 June (day 166) 2004.

Fig. 4. Surface albedo at CP1 (69.8819°N, 46.97358°W) from 2 April (day 93) through 15 August (day 228) 2004.


our analysis to clear-sky observations, we expect possibleresidual AWS instrument errors.

After day 130 (May 9th), there is large variability in theMxD10A1 albedo, particularly for the Aqua albedo retrievals,while the AWS albedo remains relatively constant at an albedoof around 0.83±0.011. Between day 130 and day 155 (June3rd), the average Terra-retrieved daily albedo is nearly thesame as that measured at the AWS (0.84), but the MODIS dataexhibit more variability (one standard deviation=0.045). Forthe Aqua retrievals, the mean albedo during the same time-period is 0.86 with one standard deviation=0.088. Furtherexamination of the MODIS data during this time-intervalreveals that the variability is partially a result of clouds thatwere not accurately detected by the MODIS cloud algorithmor by the use of the TE=0.8 threshold on the AWS data. Forexample, on day 136 (May 15th), visual inspection of MODISimagery near CP-1 clearly show clouds that resulted in a Terraalbedo of 0.77 and an Aqua albedo of 0.65.

Misidentified clear-sky conditions are not the only reason forthe large fluctuations in the MODIS daily albedo during thistime-period. On days 134 and 135, the MODIS-derived dailyalbedo are 0.85 and 0.90 for the Terra instrument and 0.82 and

Fig. 5. Surface albedo at DYE-2 (66.48096° N, 46.27995° W

0.94 for the Aqua instrument, respectively. Visual inspectionreveals clear skies on both these days and yet the albedoincreases by more than 14% for the Aqua instrument (5% forTerra) whereas the in situ albedo showed nearly constant albedofrom days 134–136 (0.84 and 0.83). This could indicate aproblem with invisible clouds or the BRDFs used to correct forthe anisotropic reflectance of the snow surface. If the observa-tions from the 2 days are obtained under different illuminationand viewing angles, they will have different correction factorsapplied and errors in these could induce large changes in theretrieved albedo. This is discussed in more detail in the errorssection.

During the summer months (e.g. mid June through midAugust), there is more variability in the in situ observations atCP-1. In general, melt does not occur at elevations above2000 m. However, the extent of melt in Greenland has increasedin recent years (Abdalati & Steffen, 2001) and melt is morefrequently observed at stations such as CP-1 than it was in earlieryears. Thus, we see a decrease in the AWS measured albedowhen the snow melts and also increases as a result of newsnowfall from mid June through mid August and in general, theMxD10A1 albedo tracks these changes in albedo. Overall, the

) from 1 April (day 92) through 19 May (day 140) 2004.


mean RMSE for the Terra (MOD10A1) retrievals is 0.055 and0.070 for the Aqua (MYD10A1) retrievals.

DEA retrievals exhibit largest errors during the early season(day<105, April), underestimating the AWS albedo by 0.08 onaverage. During summer, DEA performs better, with a meanbias of −0.05. The overall residual standard error between theDEA results and those from the AWS is 0.047, similar to thefindings of Liang et al. (2005).

At DYE-2, mean AWS albedo from 1 April through 19 May2004 is 0.86±0.018, again, slightly higher than we expect fordry snow (see Fig. 5). The mean daily albedo from Terra is 0.83±0.029 and from Aqua it is 0.85±0.056. These results aresimilar to what we observe at CP-1 where the Aqua-retrieveddaily albedo are slightly higher and exhibit more variability thanthose from Terra. The overall RMSE is 0.029 for the Terra(MOD10A1) retrievals and 0.064 for the Aqua (MYD10A1)retrievals. Since the RMSE of the in situ data is 0.035 the Terra0.029 accuracy does not have any real statistical significance.

The 16-day albedo product (MxD43) appears to underestimatethe albedo at DYE-2, whereas at Summit and CP-1 the 16-day

Fig. 6. a. Surface albedo at JAR1 (69.42000° N, 50.05750° W) from 29 May (day 1(69.42000°N, 50.05750°W) from 29 May (day 150) through 7 August (day 220) 200open triangles represent the albedo from Aqua.

albedo product tracks the AWS albedo reasonably well. Stroeveet al. (2005) previously reported poor agreement between theAWS data and the 16-day MODIS albedo product at DYE-2.They noted that many of the retrievals came from the backupalgorithm and therefore it was not possible to clearly assess theaccuracy of theMxD43 16-day albedo product at this station. Thealbedo retrieved using the DEA also results in lower overallalbedo than those retrieved from the MxD10A1 and MxD43MODIS products. The mean albedo at DYE-2 using the directestimation algorithm is 0.80+0.014 with an RMSE of 0.017when compared with the in situ data.

4.1.2. Ablation regionTwo ablation zone AWS, JAR1 and JAR2, record relatively

large seasonal albedo fluctuations associated with meltingduring summer months. Thus, any image pixel geolocationerrors at these stations may result in large differences betweensurface observations and satellite-retrievals. Local albedovariability is expected depending on whether the AWS issituated over snow, bare ice, melt puddles, clean ice or dusty ice,

50) through 7 August (day 220) 2004. b. Scatter plot of surface albedo at JAR14. Black squares correspond to albedo retrieved from the Terra satellite and the


etc. In the following comparisons it is important to rememberthat this micro-scale variability may contribute to disagreementsbetween the satellite and station measurements in the ablationregion of the ice sheet. However, the satellite data should ingeneral track the seasonal variability in albedo at these stations.

Fig. 6a shows the albedo at JAR1 in a format similar to thatshown in Figs. 3–5. Fig. 6b illustrates the scatter of the in situversus the MODIS MxD10A1 daily albedo products. Note thatat JAR1, there was a severe (40°) leveling error until the sitemaintenance was accomplished May 29 (day 150), 2004.Comparisons with MODIS albedo values are based on data afterthe site maintenance. After 7 August (day 220) the data againsuffered from leveling drift near the end of the melt season,verified in the data and by leveling measurements made in the2005 site visit. Thus, again, no comparisons are made after day220, 2004. At JAR1 there is large seasonal variability in thesurface albedo, with values around 0.8 in May, indicative of drywinter snow accumulation before seasonal melt onset. Albedothen decreases to a bare melting ice value of 0.5, occurring inJuly. After the first week of July, the albedo fluctuates as newsnow raises the surface albedo and subsequent melting eventslower it again.

There is overall poor agreement between the daily MODISalbedo products and the JAR1 AWS data. Visual inspection oftheMODIS data near JAR1 indicates misidentification of cloudsmay have occurred for only one of the days during June and July.Thus, cloud contamination does not appear to be a significantfactor for the discrepancies noted during those months. Theagreement is worst in June and July as the snow melts and thebare ice is exposed. Liang et al. (2005) also found pooragreement between AWS and MODIS DEA albedo at JAR1.The JAR1 AWS site is adjacent to a basin where a perennial meltlake of approximately 500 m in diameter forms and drains after7–10 days. The lake existed 9 days in 1996 (192–200) and4 days (days 177–180) in 1997 (Box, 1997b). Lake contami-

Fig. 7. Aerial photo of the JAR1 AWS taken approximately 30 m above surface showand dust, August 10th, 2005. The radiometer footprint is to the left of the wooden b

nation of the MODIS pixel could explain the consistent negativedeparture for all methods for the 10 day period (170–180) in2004. The linear correlation coefficient (r) between the AWS andTerra (MOD10A1) albedo is 0.54, and for Aqua (MYD10A1) itis 0.65. This correlation is poorer than expected despite thespatial variability of the surface and poorer than that observed inprevious validation studies (0.8< r<0.9) at this site (e.g. Liang etal., 2005; Stroeve et al., 2001; Stroeve, Box et al., 2005). Thecorrelation of the AWS albedo with that derived using the DEAis lowest (r=0.48). It could be that the small melt puddles thatform in the vicinity of JAR1 (Fig. 7) occasionally result in anunderestimation of the station albedo once melt has started. Fig.7 shows an example of the surface, including melt puddles (notlakes), snow patches, and dust accumulations, at the JAR1 AWSon 10 August 2005. If we assume that 30% of the MODIS pixelcontains melt ponds and 70% bare ice while the AWS is seeing100% bare ice, the error in albedo would be −0.16 assuming analbedo of 0.65 for bare ice and an albedo of 0.3 for the meltponds. Thus, the presence of melt ponds near JAR1 is likely oneof the reasons why the agreement is so poor at JAR1 duringsummer. Another reason would be tower and battery boxinterference with the measurements (Fig. 7). Furthermore, sincethe nearby lake is not always in the same spot, it could explainvariable errors with respect to satellite data from year to year.

We limit our comparisons with JAR2 AWS data (Fig. 8a, b)to dates after the 24 May (day 145) 2004 maintenance visit,since leveling errors affect the data prior to this date. At JAR2,the satellite-retrieved albedo values are closer to the AWS-observed values than at JAR1, though there remains consider-able scatter and outliers in the satellite data (Fig. 8b). Thecorrelation between the MxD10A1 MODIS albedo and theAWS albedo at JAR2 is 0.71 from Terra and 0.73 from Aqua.The DEA method shows better agreement with the in situ data(r=0.85). Overall, the residual error using the Terra(MOD10A1) daily snow albedo product is 0.074, while it is

ing high spatial variability in albedo owing to melt water puddles, snow patches,attery box resting on the ice surface. The tower height is 5 m.

Fig. 8. a. Surface albedo at JAR2 (69.42000° N, 50.05750° W) from 24 May (day 144) through 21 September (day 265) 2004. Symbols are the same as those in Fig. 2.b. Scatter plot of surface albedo at JAR2 (69.42000° N, 50.05750° W) from 24 May (day 145) through 21 September (day 265) 2004. Black squares correspond toalbedo retrieved from the Terra satellite and the open triangles represent the albedo from Aqua.

Fig. 9. Scatter plot of the MOD10A1 (Terra) averaged 16-day albedo with theMCD43B3 (Aqua+Terra) 16-day albedo product. Black diamonds correspondto the MCD43B3 black-sky albedo (BSA) and white triangles are the MCD43B3white-sky albedo (WSA). In general the BSA and WSA are nearly identical.


0.087 for Aqua (MYD10A1). The RMSE using the DEA is0.063. At JAR1, the RMS errors were 0.082 and 0.094 for theTerra (MOD10A1) and Aqua (MYD10A1) retrievals, respec-tively. It is important to remember however that the MODISalbedo products may perform better than reported here at JAR1and JAR2 because of surface heterogeneity not well captured bythe small∼5m2 AWS footprint. This hypothesis is supported bylower overall RMS errors at higher elevation sites.

4.2. Comparison between the MxD10 and MxD43 products

This section compares the two standard MODIS snow albedoproducts. In order to make this comparison, the MOD10A1 andMYD10A1 daily albedo are averaged over the same 16-dayperiods that are used to make the MxD43 products. To remainconsistent with the previous comparisons, only data that havebeen cloud-cleared according to the AWS data are used for thiscomparison. Data from all AWS sites are combined into one plot,with separate plots for the BSA and WSA.

The 16-day averaged albedo derived from the MxD10A1products is greater than that from the MxD43 dataset. Thedifferences are on the order of 0.035 to 0.039 between theMOD10A1 (Terra)-derived 16-day albedo and the BSA and

WSA albedo, respectively, provided by the MCD43 (e.g. fromboth the Aqua and Terra satellites) product (see Fig. 9 forexample). The differences are even larger for the MYD10A1

Fig. 10. Scatter plot of the MYD10A1 (Aqua) averaged 16-day albedo with theMCD43B3 (Aqua+Terra) 16-day albedo product. Black diamonds correspondto the MCD43B3 black-sky albedo (BSA) and white triangles are theMCD43B3 white-sky albedo (WSA). In general the BSA and WSA are nearlyidentical.


data (Fig. 10), with mean differences of 0.085 betweenMYD10A1 (Aqua)-derived 16-day albedo and the BSA albedofrom MCD43 and 0.053 for the WSA albedo. The bias isinfluenced by the larger offsets observed for lower albedo (i.e.albedo less than 0.7) where the daily MODIS albedo productsconsistently predict higher albedo than that obtained from the16-day MODIS albedo product. At Summit and JAR2 theMOD43 and MCD43 albedo products match the 16-dayaveraged AWS values better than either the MOD10A1 orthe MYD10A1 products. At the other 3 stations theMOD10A1- and MYD10A1-derived 16-day mean albedoagrees better with the AWS observations than does the MxD43albedo. However, it is believed that the AWS data from DYE-2 are biased high by approximately 0.02 and therefore itremains inconclusive at this site whether the MODIS dailyalbedo products out-perform the 16-day products. For dates inApril, it is known that the MxD43 16-day albedo product issubject to large errors because of algorithm problems underhigh SZAs (Stroeve, Box et al., 2005). Therefore, includingdata from high SZA cases in the averaging stations lowers theoverall mean albedo, as noted for DEA-derived 16-day meanalbedo at the higher elevation stations (Table 2).

Table 2Mean albedo and standard deviation (in parenthesis) of the 16-day averaged albedo

Station AWSalbedo

MODIS MOD10A1albedo (Terra)

MODIS MYD10A1albedo (Aqua)

BSA MCD4albedo (Aqu

CP-1 0.83 (0.033) 0.83 (0.066) 0.85 (0.044) 0.80 (0.018)Summit 0.84 (0.012) 0.86 (0.042) 0.87 (0.068) 0.83 (0.010)DYE-2 0.88 (0.012 0.84 (0.023) 0.86 (0.028) 0.81 (0.025)JAR1 0.79 (0.112) 0.72 (0.097) 0.73 (0.078) 0.64 (0.052)JAR2 0.51 (0.069 0.63 (0.083) 0.66 (0.112) 0.58 (0.045)

Results are shown for the AWS 16-day averaged daily albedo, the MOD10A1 (Terra)the MOD43B3 (Terra) and MCD43B3 (Aqua+Terra) 16-day albedo product. Note thwhite-sky albedo (WSA) albedo only. The actual albedo falls between these two vaestimation algorithm as applied to the MODIS level 1B data.

5. Error sources

5.1. BRDF correction

An important source of errors in satellite albedo retrievals is therepresentation of anisotropic surface reflectance in the compli-cated context of differing viewing and solar illuminationgeometries (e.g. Greuell & Oerlemans, 2005; Stroeve et al.,2001, 1997; Warren, 1982). Snow scatters sunlight in the forwarddirection (e.g. Warren, 1982) and failure to account for the BRDFof snow can result in large errors in the satellite-derived surfacealbedo. Fig. 11 shows an example of the BRDF measured oversnow at 0.55 and 1.03 μm at a SZA of 49° (obtained from Painterand Dozier, 2004). Fig. 11 illustrates the forward scattering natureof snow (forward direction is represented by a relative azimuthangle of 180°), particularly under oblique viewing angles. Thesemeasurements were obtained for snow with a grain size ofapproximately 284 μm, which is similar to the grain size used tomodel the BRDF for snow in the MxD10A1 albedo algorithm(e.g. grain size is set to 250 μm).

The DIScrete Ordinate Radiative Transfer model (DISORT)(Stamnes et al., 1988) is used to model the snow BRDF andresults from these model runs provide the individual MODISchannel anisotropic reflectance factors (ARFs) that are used to“correct” the MODIS satellite measurements made under aspecific illumination and viewing geometry to represent ahemispheric albedo. The ARF is defined as the ratio of thesatellite measured reflectance (or the BRDF) to the spectralalbedo (as):

ARFðhs; hv;/Þ ¼ kdBRDFðhs; hv;/Þ=asðhsÞ

where θs is the SZA, θv is the sensor zenith angle and ϕ is therelative azimuth angle (where 180° represents the forwarddirection). The ARFs in the MxD10A1 product were modeledassuming the sky to be completely composed of direct radiation(ignoring diffuse components). The ARFs under real skyconditions will be slightly different depending on the ratio ofdirect to diffuse sky radiation and is a potential error source in theARF values used in the processing of the MODIS daily snowalbedo. Another potential error source is the fact that snow grainsize is variable, and different ARFs are needed depending onhow fine or coarse the snow grain size is. Recently Painter and

for clear-sky cases only

3a+Terra)

BSA MOD43albedo (Terra)

WSA MCD43albedo (Aqua+Terra)

WSA MOD43albedo (Terra)

DEA albedo(Terra)

0.79 (0.021) 0.80 (0.020) 0.79 (0.020) 0.77 (0.024)0.82 (0.028) 0.82 (0.007) 0.81 (0.045) 0.80 (0.023)0.80 (0.034) 0.80 (0.038) 0.79 (0.050) 0.80 (0.004)0.61 (0.061) 0.62 (0.072) 0.61 (0.059) 0.70 (0.090)0.55 (0.057) 0.58 (0.043) 0.55 (0.066) 0.55 (0.064)

and MYD10A1 (Aqua) daily albedo averaged over a 16-day time-period and fore MOD43B3 and MCD43B3 albedo products provide the black-sky (BSA) andlues. The last column gives the mean 16-day averaged albedo using the direct

Fig. 11. Hemispherical-directional reflectance factor measurements of snow at 0.55 μm and 1.03 μm made with the Automated Spectro-Goniometer (ASG) [Painterand Dozier, 2004]. Solar zenith angle was 49°, high elevation skies were clear, and snow surface grain radius from stereology was 284 μm.

Table 3Solar and sensor zenith angles, relative azimuth angle and albedo for the Terra(MOD10A1) and Aqua (MYD10A) pixels at 4 AWS spanning 13 May through18 May 2004

Dayof year

Solar ZenithAngle (degrees)Terra/Aqua

Sensor ZenithAngle (degrees)Terra/Aqua

Relative AzimuthAngle (degrees)Terra/Aqua

Albedo(%) Terra/Aqua

CP-1133 51.61/51.74 6.30/19.23 107.0/62.57 86/90135 51.07/51.14 3.21/9.94 68.25/60.27 90/94136 51.28/51.18 27.15/29.22 110.06/112.43 77/65137 50.63/50.61 12.80/0.47 66.88/101.15 80/78138 50.62/50.90 19.52/32.06 110.54/61.74 80/95

DYE-2133 48.23/48.42 23.37/10.69 111.01/60.79 150/150135 47.68/47.77 13.34/0.84 110.10/84.63 150/82136 48.15/48.49 34.72/35.91 62.86/59.25 86/83137 47.23/47.22 2.31/11.82 119.49/115.16 79/93138 47.33/47.66 35.84/26.76 109.41/59.69 82/99

JAR1133 51.17/51.25 0.78/9.33 58.75/64.99 94/100135 50.71/50.70 10.54/0.44 61.10/129.83 100/100136 50.70/51.00 22.00/31.88 109.48/61.25 86/88137 50.34/50.23 20.09/10.02 64.73/112.50 150/150138 50.10/50.30 13.43/23.45 111.11/61.03 79/86

JAR2133 51.09/51.15 1.33/8.06 73.79/62.15 79/100135 50.64/50.61 11.19/1.75 63.24/101.23 87/93136 50.61/50.89 21.63/30.95 110.25/60.80 82/67137 50.27/50.16 20.64/11.19 65.17/114.07 62/62138 50.01/50.20 12.97/22.43 112.36/62.25 150/150

Relative azimuth angle in the MxD10A1 products is defined as the absolutevalue of the solar azimuth minus the sensor azimuth.


Dozier (2004) showed that fine-grain snow has a smallbackscattering peak at θv=50°, whereas medium snow grainsizes do not.

We suspect that some of the large variability in albedo at CP-1after day 130 may indicate problems with the BRDF adjustmentin the MxD10A1 albedo product. To test this, we obtainedMxDGGAD–MODIS/Terra/Aqua Geolocation Angles DailyL2G Global 1 km SIN Grid data that provides the illuminationand viewing angles under which the MOD10A1 and MYD10A1products retrieved the albedo for our station pixel. Table 3summarizes the angles and albedo at all stations except Summiton days 133, 135–138. Days 133, 135 and 138 are confirmed tobe completely clear by visual inspection of the satellite data,whereas clouds appear to cover the stations on day 136 eventhough both the MOD10A1 and MYD10A1 retrieve an albedo(i.e. the cloud mask failed to detect the clouds). Some scatteredcloudsmay also be present on day 137 near the sites, and this mayhave been picked up by theMODIS algorithm as evidenced by thevalues of 150 at the JAR1 site. Note that at DYE-2, bothMOD10A1 and MYD10A1 say it's cloudy on day 133 andMOD10A1 additionally says it's cloudy on day 135.

Several important results follow. First, the MYD10A1 Aqua-retrieved daily albedo is consistently higher than those from Terra.An exception to this is on days 136 and 137 at JAR2 which mightbe because one sensor saw the nearby clouds and/or cloud shadowsand the other one did not (although both the Aqua and Terra pixelsat this site are flagged as cloud-free). Second, both theMOD10A1andMYD10A1 retrievals for these stations occur at about the sametime of day (similar SZAs for both the Terra and Aqua retrievals).Thus, differences in theMOD10A1 andMYD10A1 retrievals on aparticular day are not because the retrievals were made underdifferent illumination or surface states. In addition, the SZAs of theTerra and Aqua observations between days 133 and 138 at thestations do not differ by more than a degree. This implies thatdifferences in times of day are not a factor in the observed satellite-retrieved albedo variability between these days. Thus, since thealbedo as measured by the AWS at CP-1 was essentially constanton these days, the variability in the MxD10A1 daily albedoretrievals is apparently due to some other factor.

In general, differences in sensor zenith angle between the Terraand Aqua retrievals are similar with differences less than 10°. Inaddition, all the sensor zenith angles are less than 36° for the days

listed in Table 3, and thus none of the observations were madeunder particularly oblique viewing angles. Nevertheless, the ARFdoes vary considerably with sensor viewing angle even for zenithangles less than 40°. In Fig. 12 we showDISORT-modeled ARFsfor MODIS channel 1 as a function relative azimuth and viewingzenith angle. In these model simulations we also assumed a snowgrain size of 250 μm (as used by the MxD10A1 product) and setthe forward direction to be at ϕ=180° to be consistent with therelative azimuth defined in the MxD10A1 data. Thus, for

Fig. 12. Modeled dependence of the anisotropic correction factor (ARF) at 50°SZA on sensor viewing zenith (18° to 42°) and relative azimuth angles (0° to180°:ϕ=180° represents the forward direction).

Fig. 13. MODIS channel 1 (0.62–0.67 μm) ARFs provided by the MxD10A1albedo algorithm. Solar zenith angle is 49°, snow grain size is 250 μm and theforward direction is given by ϕ=180°.


observations in the forward direction theARFwould act to reducethe satellite reflectance observations, whereas the ARF wouldincrease reflectances made in the aft direction. The mostnoticeable angular differences listed in Table 3 occur in therelative azimuth angles, with differences as large as 60° betweenthe Terra and Aqua observations and also between days. Forexample, on day 133 (May 12th) at CP-1, the difference in relativeazimuth angles between the Terra and Aqua observations isϕdiff=44.43°. Using the modeled ARFs shown in Fig. 12, wewould obtain a MODIS channel 1 ARF for Terra of∼1.0247 andfor Aqua of ∼0.9959. These values of the ARFs would act toincrease the Aqua channel 1 reflectance by about 0.4% whereasthe Terra reflectance would be decreased by 2.4%. Thus, perhapsat CP-1 on day 133, the 4% absolute albedo difference in albedobetween the Terra and Aqua retrieval is because the ARFs werenot adequately modeled to correctly adjust the satellite measure-ments for the difference in relative azimuth angle between the twoobservations.

To further investigate the accuracy of the ARFs used in theMxD10A1 processing, we extracted out the ARFs from the look-up-tables (LUTs) providedwith the operational albedo processingcode. Fig. 13 shows the ARFs used for MODIS channel 1 at aSZA of 49° (the same SZA as the observational BRDFs shown inFig. 11). If the BRDFwasmodeled correctly, wewould expect theARFs to show a pattern similar to the observed BRDFs shown inFig. 11 from Painter and Dozier (2004).What we notice is that thelocationwhere themodeledBRDF reaches itsmaximum is shiftedto slightly smaller viewing angles than what was measured byPainter and Dozier (2004) and that the area of lower reflectanceextends further out towards the forward direction at high viewingzenith angles. In addition, results suggest that the magnitude ofthe forward scattering peak is overestimated. Painter and Dozier(2004) made some comparisons between observational BRDFsand DISORT-modeled BRDFs and further found that DISORTunderestimated reflectance for wavelengths greater than 1.03 μmand exhibited large errors in the perpendicular plane. Some of thereasons whyDISORTmay not adequatelymodel the snowBRDF

are that the phase function does not accurately characterize theforward scattering peak and surface micro-topography is notaccounted for in the model. Regardless, Fig. 13 suggests that themodeled BRDFs used in the MxD10A1 daily albedo productsdeviate from what is observed in nature, which may result insignificant errors in the MODIS-derived daily albedo product,depending on the illumination and viewing angles.

However, we acknowledge that not all of the variability inthe MODIS-derived albedo can be attributed to problems withthe algorithm-prescribed snow BRDFs. Sometimes the illumi-nation and viewing angles do not differ by much between theTerra and Aqua observations, and yet the MOD10A1 andMYD10A1 albedo still differ substantially. For example, atJAR2 on day 133, the difference in albedo is 21% between theMOD10A1 and MYD10A1 retrieved albedo. However, Table 3shows the illumination and viewing angles are similar for boththe Terra and Aqua observations (ϕdiff=11.64°; θs_diff=0.06°;θv_diff=6.73°) and thus, theoretically, similar ARFs should havebeen applied (especially when viewing near nadir). If the ARFswere indeed about the same (suggested both by the DISORT-modeled and observed BRDFs), the large discrepancies in theMOD10A1 and MYD10A1 retrieved albedo for this examplemay be a result of a different error source besides potentialerrors in the prescribed ARFs.

5.2. Slope effects

In general, the surface of the Greenland ice sheet exhibitsgentle slopes and therefore slope-effects on the satellite derivedalbedo are small. The slope and aspect files derived fromGTOPO30 elevation data have a nominal horizontal resolution of500m.GTOPO30 has a nominal resolution of 30″ (approximately1 km), but an actual resolution over the Greenland ice sheet ofprobably no better than 5 km, sufficient to capture the large-scaleslope of the ice sheet, but inadequate to resolve local-scale surface

Table 4Frequency of the number of times that the MOD10A1 product did not indicateclouds when clouds were present and the number of times the MOD10A1product indicated cloudy conditions when no clouds were present out of 122images inspected

Station Number of times MODISdid not detect clouds

Number of times MODISdid not detect clear-sky

CP-1 13 or 11% 10 or 8%Summit 5 or 4% 9 or 7%DYE-2 4 or 3% 5 or 4%JAR1 6 or 5% 14 or 11%JAR2 6 or 5% 14 or 11%


undulations. The magnitude of the slope errors is probably smallbecause the variation in slope of the undulation field on the icesheet is small. Not capturing the undulation field in the DEMprobably results in an apparent albedo error of about 3–5% at theindividual station locations. Using a DEM with a true resolutionapproaching 500 m (e.g. the 625 m photoclinometrically-enhanced Greenland DEM; Scambos & Haran, 2002), wouldreduce this error to near zero (assuming that the slope and aspectdata are computed correctly and used in the albedo calculationcorrectly). Recent evaluation of the GTOPO30 topography overGreenland (including land) showed an overall bias of −193 m(Box & Rinke, 2003), which would be corrected using a modernDEM, such as that available from Scambos and Haran (2002).However, the overall effect of a negative elevation bias on thealbedo retrievals should be minimal, assuming the surface slopesand directions remain roughly the same.

5.3. Cloud detection

It is apparent that there are problems with cloud identificationusing both TE calculated from AWS data and in the MODIS (e.g.MOD35) cloud detection algorithm. At the AWS sites, severalfactors, such as pyranometers calibration, instrument leveling andmultiple scattering between the surface and the clouds degrade theaccuracy TE. Variations in the intensity of multiple scattering alsocause variations in TE. If the cloud cover is optically-thin, theAWS may not ‘see’ the clouds, because of an increase in diffuseirradiance counteracting the decrease in direct beam radiance. Athigher elevations where the albedo is usually higher and theclouds are often thinner, more multiple scattering should bias theeffectiveness of cloud identification using TE. Thus, the algorithmshould perform best for lower albedo surfaces and for opticallythick clouds (i.e. less multiple scattering).

The use of TE for cloud identification suggests a higherfrequency of clear conditions than found in the MOD35 cloudmask. For example, at CP-1, the MOD10A1 albedo arrayindicates cloudy conditions 34 out of 39 AWS-flagged clear-skydays. At lower elevations, a similar fraction of discrepancies arefound. For example, at JAR2, MOD10A1 and MYD10A1indicate only 3 and 6 clear days, respectively, out of 37 dayssuggested to be clear by the AWS retrieval. These comparisonsmay indicate the MODIS cloud mask is too conservative oversnow.

We can evaluate the MODIS cloud detection algorithm furtherby visual inspection of the MODIS images. We thus visuallyinspected 122 MODIS images to see if MOD10A1, which usesthe MOD35 cloud mask, accurately detected cloudy conditions.We found several instances when the product returns a surfacealbedo value when it should have been set to the cloud-flag value.In general, the cloud mask algorithm results in more cloudobscuration than is observed by visual inspection. Table 4 lists thenumber of times that theMOD10A1 product did not detect cloudswhen visual inspection indicates clouds present as well as thenumber of times the scenewas clear when theMOD10A1 productsaid it was cloudy for the dates when we had coincident TerraMODIS and AWS data. Table 4 confirms that in general theMODIS cloud mask more frequently detects clouds when clouds

are not present (i.e. the cloud mask is too conservative). Theexception in this limited comparison is at CP-1where theMODIScloud mask missed several instances where visual inspectionindicated it was cloudy.

5.4. Atmospheric correction

There are no studies evaluating the performance of theMODIS(MOD09) atmospheric correction over snow- and ice-coveredsurfaces. Thus, it is not possible to easily quantify what sorts oferrors may be a result of errors in the MODIS atmosphericcorrection algorithm. The atmospheric correction uses band 26(1.36–1.39 μm) to detect cirrus clouds, water vapor fromMOD05, aerosols from MOD04 and ozone from MOD07.Climatologies are used if the values of these atmosphericvariables are unavailable (e.g. aerosols, water vapor, and ozone).

Some limited validation studies of the MODIS-retrieved watervapor have been performed at the DOE ARM site in Barrow,Alaska and are summarized at http://modis-atmos.gsfc.nasa.gov/validation.html. The seasonal variations of the MODIS-retrievedwater vapor appeared to be realistic. However, because themicrowave radiometer used at the site had calibration problems, itwas not possible to reach a conclusion on the accuracy of theMODISwater vapor product over snow- and ice-covered surfaces.

It has been impossible to retrieve aerosols over bright surfacessuch as snow and ice, compromising the climatology of aerosols inthe polar regions. In the atmospheric correction for snow- and ice-covered regions, the aerosol optical depth at 550 nm is set at 0.05.Measurements made over Greenland at a surface elevation ofapproximately 1100 m suggest a mean aerosol optical depth of0.065 during summer (Stroeve et al., 1997). Thus, at theseelevations and lower, the aerosol optical depth prescribed in theMOD09 product is slightly underestimated (note: aerosol opticaldepth decreases with surface elevation). Additionally, there areprobably seasonal aerosol variations that are not being consideredby prescribing the optical depth to be constant throughout the year.

Previous sensitivity studies have looked at the effects of errorsin variables such as aerosol, water vapor and ozone optical depthon satellite-retrieved snow albedo from AVHRR and MODIS(e.g. Greuell & Oerlemans, 2005; Stroeve et al., 1997). Bothstudies show that out of the three major atmospheric variables(water vapor, ozone and aerosols), uncertainties in aerosol opticaldepth have the largest impact on satellite-retrieved albedo. Greuelland Oerlemans (2005) suggest that for pixels with viewing angles

http://modistmos.gsfc.nasa.gov/validation.html

http://modistmos.gsfc.nasa.gov/validation.html


less than 50°, variation in aerosol optical depth impacts the albedoby less than 0.01. For viewing angles greater than 50°, theysuggest that the impact will be larger, though they don't specifyhow large. These assessments were performed using the 6Sradiative transfer model, the same model used in producing theMOD09 surface reflectance product. Shortcoming in thisradiative transfer model may also lead to large errors particularlyfor surfaces that exhibit strong anisotropy (e.g. bare ice)(Lyapustin, 2002).

5.5. Other error sources

Accurate instrument calibration is essential in order toprovide high-quality scientific data sets. Greuell and Oerlemans(2005) suggest uncertainties in MODIS albedo as a result ofcalibration problems around 2% for the Terra instrument basedon comparisons made using data in Greenland, which is similarto that reported by Justice et al. (2002).

Geolocation accuracy is also very important and certainly sucherrors may be amplified in the ablation region and elsewhere thatheterogeneous surfaces reduce the applicability of point-measure-ments. The current geolocation accuracy (Level 1A geolocationdata) for Terra MODIS is ∼50 m whereas that for the AquaMODIS instrument is ∼65 m (Wolfe et al., 2002). This isconsiderably better than the ground-equivalent nadir pixel size forthe MODIS bands (∼250 m) and much better than thegeolocation accuracy for AVHRR.

Several tests of the geolocation accuracy and precision havebeen conducted by Scambos et al. (in press) using known surfacesites (e.g. South Pole Station, Vostok Station, Dome Concordiacamp, Siple Dome camp and its north-south GPS traverse trail)and areas of well-mapped coastline (e.g., Ross Island, northernAntarctic Peninsula). Results showed that the geolocationdiscrepancy never exceeded 125 m (i.e. one grid cell) in theprojected location of a fixed object among the 260 scenes, orrelative to well-mapped coastline positions. Further, overlappedareas of separate images showed identical feature locations on thegrid to within one grid cell. Given these results, positional errorsin the low-resolution MODIS albedo products are unlikely to beany larger that 125 m, particularly over the Greenland ice sheetwhere the local relief is relatively flat.

The spatial variability of the surface in the ablation region ofthe ice sheet makes it difficult to compare the larger satellitefootprint (spatial resolution of 500 m for the MxD10A1product) with the ∼5 m2 footprint of the AWS measurement.In the high elevation regions of the ice sheet where the surface ismore homogenous, the comparison between in situ and satellitedata becomes less ambiguous.

6. Conclusions

This paper provides an evaluation of the performance of thebeta-test MODIS daily albedo product (e.g. MOD10A1 andMYD10A1) using in situ data collected in Greenland during2004. Results indicate an overall RMS error of 0.067 for theTerra instrument (MOD10A1) and an RMS error of 0.075 onAqua (MYD10A1) through comparisons with in situ observa-

tions at five automatic weather stations in Greenland. The Terra-derived daily albedo have a correlation coefficient of 0.79 whilethe Aqua daily albedo observations are slightly less correlatedwith the automatic weather station (AWS) data (r=0.77).

We noticed frequent satellite-retrieved daily clear-sky albedovalues that were unrealistically high. For the 158 coincidentTerra-derived daily albedo retrievals at five individual AWSsites, albedo greater than 0.90 occurred 6.3% of the time whilevalues of 1.0 occurred 1.3% of the time. The Aqua daily albedoretrievals showed more frequent albedo values greater than0.90: 12.8% of the 163 point measurements had an albedogreater than 0.90 and 2.4% of the measurements had an albedoof 1.0. Both the Terra and Aqua retrievals at the sites were madeat nearly the same time of day. Therefore, differences in the timeof day when the surface was viewed would not explain theobserved differences in albedo between the two satellites. Inaddition, both the Terra and Aqua daily albedo algorithmsshould be using the same MODIS bands to derive the albedo(i.e. using MODIS band 7 instead of MODIS band 6 which isunreliable for MODIS on the Aqua satellite). Thus, differencesin daily albedo between Terra and Aqua cannot be explained bydifferences in the MODIS bands used to derive the albedo.

Results here suggest that the Bidirectional ReflectanceDistribution Function (BRDF) is poorly modeled and thereforethe conversion from the satellite observation to an albedo is inerror. Examination of the BRDF/ARF look-up-tables (LUTs) foreach MODIS channel (channels 1–7) in the MxD10A1 proces-sing code revealed that the modeled BRDF exhibit differentspatial patterns (and magnitudes) than what is observed in natureaswell as completely different BRDF spatial patterns for channels1–5 than for channels 6–7 (an apparent error in the code).

Despite apparent problems in the modeled BRDFs, itremains inconclusive as to why the Aqua (e.g. MYD10A1)retrievals are biased high relative to those from Terra (e.g.MOD10A1) and users are cautioned of the potential errors in thedata. However, in general, both the MOD10A1 and MYD10A1daily albedo products track the seasonal variability in albedodespite occasional problems with cloud detection.

The processing of the daily (MOD10A1/MYD10A1) and the16-day (MOD43B3/MCD43B3) albedo products are verydifferent and provide albedo at different temporal scales as wellas provide different albedo measurements: MOD43B3/MCD43B3 gives the black- and white-sky albedo whereas theMOD10A1/MYD10A1 products give the albedo that is a linearcombination of the BSA and WSA. Nevertheless, we made acomparison between the two MODIS standard snow albedoproducts by averaging the MOD10A1 and MYD10A1 over thesame 16-day intervals that the MOD43B3/MCD43B3 productrepresents. Since the BSA andWSA are nearly identical for manyof the observations, we should expect general correspondence ofthe 16-daymean albedo derived from theMOD10A1/MYD10A1products compared with the MOD43 and MCD43 BSA andWSA. This is indeed what we find. Mean biases between theMOD10A1 (Terra) and the combined Terra and Aqua(MCD43B3) 16-day albedo are 0.04 for the BSA and 0.08 forthe WSA albedo. Correlation between them was generally high,with a correlation coefficient of 0.89 for the BSA and 0.87 for the


WSA. In this comparison, as in the comparisons between the dailyalbedo retrievals from Aqua and the in situ data, results are worsefor the MYD10A1 product. Mean differences increase to 0.05(correlation coefficient of 0.82) between theMYD10A1 and BSAand increase to 0.10 (correlation coefficient of 0.77) for theWSA.

Albedos from the direct estimation algorithm (DEA) ofLiang et al. (2005) appear to match the in situ data better thanthe albedo from the MxD10A1 MODIS products, with anoverall RMSE of 0.052 and a correlation coefficient of 0.88.However, it is apparent that retrievals during spring (e.g. April)show a systematic underestimation of the surface albedo(−0.08). During April solar zenith angles (SZAs) at the sitesare often above 70° and the accuracy of the algorithm degrades.The DEA method retrieves albedo values greater than 0.90(3.1% of the time based on 254 point measurements) and valuesof 1.0 occurred 1.2% of the time. However, all but one of theseinstances occurred under conditions where the SZA exceeded70°. Thus, from these comparisons as well as those shown inLiang et al. (2005) it is recommended that the algorithm only beapplied only when SZA is less than 70°.

Accurate retrieval of snow and ice albedo from satellitesremains an active research area. Since the methods to retrievesurface albedo from satellite require several processing steps,propagation of errors in each step can lead to large errors in theretrieved albedo. All algorithms that rely on a single observationto make a full retrieval of both the atmospheric and surface effectswill have largest errors in anomalous conditions. However,several studies have shown that although the magnitudes of thealbedo may not exactly match those observed in situ, particularlyon a daily basis, satellites provide good qualitative estimates of theseasonal and spatial variability of snow albedo. Continuedimprovement in cloud detection over snow- and ice-coveredsurfaces and accurate description of the BRDF will lead toimproved accuracy of the various snow albedo algorithms. In theupcomingV005 reprocessing, a refined snow/cloud detectionwillbe applied to theMxD10A1 andMxD43 albedo products that willhopefully lead to improved albedo retrievals (e.g. less cloudcontamination). Additionally, the V005 MCD43 product willhave an increased time-step of every 8 days based on the last16 days at 500 m spatial resolution.

Acknowledgements

This study was supported by a NASA grant NNG04GO51G.The authors wish to thank Dr. Hall and Dr. Schaaf and twoanonymous reviewers for their valuable comments on themanuscript, and those involved in collection of the Greenlandice sheetAutomaticWeather Station (AWS) data led byK. Steffen.

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