NASA Technical Reports Server (NTRS) 20120009500: Use of In Situ and Airborne Multiangle Data to Assess MODIS- and Landsat-based Estimates of Surface Albedo

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by  NASA Technical Reports Server (NTRS)

1 Use ofin situ andairborne multiangle data toassess
2 MODIS- and Landsat-based estimates ofsurface albedo
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5 MiguelO. Romána,*, Charles K. Gatebea,b, YanminShuaia,c,
6 Zhuosen Wangd,e, Feng Gaof, JeffMaseka, and CrystalB. Schaafg
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8 aNASAGoddardSpaceFlightCenter,Greenbelt,Maryland,USA
9 bUniversitiesSpaceResearchAssociation(USRA),Columbia,Maryland,USA
10 cEarthResourcesTechnologyInc.,Laurel,Maryland,USA
11 dCenterforRemoteSensing,DepartmentofGeographyandEnvironment,BostonUniversity,Boston,MA,USA
12 eStateKeyLaboratoryofRemoteSensingScience,CenterforRemoteSensingandGISofGeographyCollege,
13 BeijingKeyLaboratoryforRemoteSensingofEnvironmentandDigitalCities,BeijingNormalUniversity,Beijing,China
14 fUSDA-ARSHydrologyandRemoteSensingLaboratory,Beltsville,Maryland,USA
15 gEEOSDepartment,UniversityofMassachusettsBoston,Boston,Massachusetts,USA
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20 * Corresponding author.Tel.: [phone];fax: [phone]
21 E-mail address: [email protected]
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1
23 Abstract –
24 The quantificationofuncertaintyofglobalsurface albedo data and products is a criticalpart
25 ofproducing complete, physicallyconsistent,and decadalland propertydata records for studying
26 ecosystemchange.Acurrent challenge in validating satellite retrievals ofsurface albedo is the
27 abilityto overcome the spatialscaling errorsthat cancontribute onthe orderof20% disagree-
28 ment betweensatellite and field-measured values. Here, we present the results fromanuncertain-
29 tyanalysis ofMODerate ResolutionImaging Spectroradiometer (MODIS)and Landsat albedo
30 retrievals, based oncollocated comparisons withtower and airborne multiangular measurements
31 collected attheAtmospheric RadiationMeasurement Program’s (ARM) Cloud and Radiation
32 Testbed (CART) site during the 2007 Cloud and Land Surface InteractionCampaign(CLAS-
33 IC’07). Using standard error propagationtechniques, airborne measurements obtained by
34 NASA’s CloudAbsorptionRadiometer (CAR) were used to quantifythe uncertainties associated
35 withMODIS and Landsat albedos across a broad range ofmixed vegetationand structuraltypes.
36 Initialfocus was onevaluating inter-sensor consistencythroughassessments oftemporalstabili-
37 ty, as wellas examining the overallperformance ofsatellite-derived albedos obtained at alldiur-
38 nalsolar zenithangles. Ingeneral, the accuracyofthe MODIS and Landsat albedos remained
39 under a 10% marginoferrorinthe SW(0.3 -5.0µm) domain. However, results reveala high
40 degree ofvariability inthe RMSE (root meansquare error) and bias ofalbedos inboththe visible
41 (0.3- 0.7 µm) and near-infrared (0.3 -5.0µm) broadband channels;where, insome cases, re-
42 trievaluncertainties were found to be inexcess of20%. Forthe periodofCLASIC’07, the prima-
43 ryfactorsthat contributedto uncertainties inthe satellite-derived albedo values include: (1)the
44 assumptionoftemporalstabilityin the retrievalof500 mMODIS BRDF values over extended
45 periods ofcloud-contaminatedobservations;and (2)the assumptionofspatialand structuralun-
46 iformityat the Landsat (30m) pixelscale.
47 1. Background
48 Amajor goalofinternationalEarthobservationeffortsis the long termmonitoring ofterre-
49 strialessentialclimate variables and the productionofconsistent land surface radiationparame-
50 ters forrigorous modeling studies.Withthe advent ofa new generationofmulti-sensor data and
51 products forLand science applications, recent effortshave explored the “MODISization” ofna-
52 dir-looking satellite sensors toobtain high-resolution(30 m) MODIS-driven land surface para-
53 meters (Gao et al. 2006;Royet al. 2008).Forexample, (Shuaiet al. 2011) combined bothLand-
54 sat reflectance (Masek et al. 2006) and highquality500 mMODIS BRDF(BidirectionalReflec-
55 tance DistributionFunction) parameters (Lucht etal. 2000;Schaafet al. 2002;Schaafet al.
56 2008)to retrieve 30 mresolutionestimates ofsurface albedo. Bycapturing seasonaltrends at the
57 characteristic scale ofvegetationchange (~1 ha),these approaches have the potentialto improve
58 ourunderstanding ofthe climate consequences ofgloballand cover change and ecosystemdis-
59 turbance (Barnes and Roy2008;Masek et al. 2008)..
60 The quantificationofuncertaintyofglobalsurface albedo data and products fromboth
61 MODIS and Landsat satellites is a criticalpart ofproducing complete, physicallyconsistent,
62 global, and decadal land propertydata records.The MODIS BRDF/albedo standard product,
63 available globallysince 2000 at resolutions from0.5to 5 km, has been validatedto Committee
64 onEarthObservationSatellites (CEOS) Stage 2(i.e.,over a widelydistributed set oflocations
65 and time periods via severalground-truthand validationefforts).This validationstage is a pre-
66 requisite for anydata productthat is used for monitoring change over time (Morisetteet al.
67 2002).The high-qualityprimaryalgorithmfor the MODIS standard albedo product (MCD43)
68 has also beenshownto produce consistent globalquantities over a varietyofland surface types
69 and snow-covered conditions (Jinet al. 2003a;Jin et al. 2003b;Salomonet al. 2006;Liu et al.
70 2009;Románet al. 2009;Románet al. 2010;Wang et al. 2011). Onthe other hand, the combined
71 MODIS/Landsat albedo product (herebytermed ‘Landsat albedo’), which is based onper-class
72 MODIS BRDFshapes based onuniformland cover characteristics, has beenshownto provide a
73 more detailed landscape texture and achieve goodagreement with in-situ dataover a limited
74 number offield stations (Shuaiet al. 2011).Additionalassessments over a wide range ofspatial
75 (from10s ofmeters to 5-30 km) and temporalscales (fromdailyto monthly) are nonetheless re-
76 quired to accuratelyprovide end users witha pixel-specific measure ofproduct uncertainty–
77 bothinterms ofretrievalquality(e.g. givena limited number ofcloud-free satellite observations)
78 and their abilityto capture albedo trends under conditions ofseasonaland/orrapid surface
79 change.
80 Acurrent challenge in validating satellite albedo retrievals is the abilityto overcome the spa-
81 tialscaling errorsthat contribute disagreement betweensatellite and field-measured values,
82 whichcan be ontheorderof20% (Jinet al. 2003b;Salomonet al. 2006;Liu et al. 2009;Román
83 et al. 2010).Recent studies have acquired measurements atoptall(> 400 m) towersto properly
84 “scale-up” to satellite measurements (Augustine et al. 2005;Románet al. 2009).Other efforts
85 have used highresolution imageryto consider the spatialrepresentativeness ofthe towerobser-
86 vation footprint tothe MODIS pixel(Susakiet al. 2007;Románet al. 2009).While these me-
87 thods provide a goodmeans bywhichdirect “point-to-pixel” assessments can be performed with
88 highconfidence;theypresent their ownset challenges (e.g., inthe United States, instruments
89 atoptalltowers cannot be left operating year-round, dueto heavy icing and bad weather). Onac-
90 count ofthe uncertainties arising fromdirect comparison betweensparselysampled in situ mea-
91 surements and their corresponding satellite products, a formalassessment has yet to be carried
92 outto characterize the abilityofthe MODIS and Landsat datato capture diurnaltrends inalbedo
93 across spatiallyheterogeneous environments. To address these issues, we present the results from
94 anuncertaintyquantificationofMODIS and Landsat albedo retrievals based oncollocated com-
95 parisons withtower and airborne measurements. Forthe airborne datasets, we have employed the
96 retrievalscheme presented inRománet al. (2011a), which follows the operationalsequence used
97 to retrieve the MODIS surface reflectance and BRDF/albedo products, based on high-quality
98 multiangular reflectance measurements obtained byNASA’s CloudAbsorptionRadiometer
99 (CAR) (King et al. 1986;Gatebe et al. 2003).This studyfocuses onCAR retrievals obtained
100 overtheAtmospheric RadiationMeasurement Program’s (ARM) Cloud and RadiationTestbed
101 (CART) site during the 2007 Cloud and Land Surface InteractionCampaign(CLASIC’07) (Bin-
102 dlishet al. 2009;Heathmanet al. 2009).
103 2. Albedo retrieval strategy
104 Inthis section, we brieflyreview the albedo retrievalmethods used bythe MODIS, Landsat,
105 and CAR instruments, and assess the calibrationperformance ofthe CAR spectralchannels dur-
106 ing the periodofCLASIC’07. Readers are referred to Sections 2 and 3 inRománet al. (2011a)
107 for detailed descriptions ofthe CLASIC’07 experiment (including retrievalofCAR and MODIS
108 BRDF/albedo datasets);and Section2 inShuaiet al. (2011) for acomplete descriptionofthe
109 Landsat albedo retrievalstrategy.
110 2.1 Instantaneous albedos from CAR, MODIS, and Landsat
111 The CAR, MODIS, and Landsat albedo retrievalschemes employthe BRDFkernelmodel
112 parameters fromthe reciprocalversionofthe semiempiricalRossThick-LiSparse model
113 (RTLSR) (Wanner et al. 1995;1997;Lucht et al. 2000):
114 R ( , ) f  f K ( , ) f K ( , ) (1)
 v s iso, vol, vol v s geo, geo v s
115 Here,  and arethe viewing and solar geometries, whichare eachdefined byzenithand azi-
v s
116 muthalangles (,). K is the coefficient forthe RossThick volume scattering kernel(Ross
vol
117 1981);K is the coefficient ofthe LiSparse-Reciprocalgeometric scattering kernel(Liand
geo
118 Strahler 1992);and f arethe RTLSR kernelweights x inwaveband  with limits [ , ]
x, min max
119 (Wanner et al. 1995;Lucht et al. 2000).The RTLSR kernelweights are thenused to compute in-
120 trinsic surface albedos (i.e., black skyalbedo for direct beamat localsolar noonand white sky
121 albedo for isotropic diffuse radiation) (Martonchik et al. 2000;Schaepman-Strubet al. 2006):
1 2 1
R ( )   dR ( , )d
122  i  v  v i v v (2)
0 0
 f  f K ( ) f K ( )
iso vol vol s geo geo s
123
1 2 1
R   dR ( )d
124   i  i i i (3)
0 0
 f  f K  f K
iso vol vol geo geo
125 where, R  , BRDF (unitless), is the ratio ofthe surface BRDF tothat ofa perfect
 v i 
126 Lambertianreflector,whichcan be approximated bymeasurement over some (small) finite angle
127 withdiffuse illuminationand multiple interactioneffects accounted foror assumed zero (Lyapus-
128 tinand Privette 1999).Subscripts v and i denotethe upper ‘viewing’and ‘incident’hemispheres.
129 R ( )is the black-skyalbedo, R is white-skyalbedo, K   and K   arethe direc-
 s  vol v geo v
130 tional-hemisphericalintegrals, and K   and K   arethe bihemisphericalintegrals of
vol v geo v
131 K and K . Otherterms inEq. (2)and (3) are:
vol geo
 
132  cos ; y v or i (4)
y y
133 To accuratelycompare these intrinsic quantities against ground-based albedos,the black-skyand
134 white-skyalbedos must be combined as a function ofsolar geometryand atmospheric stateto
135 compute instantaneous albedo under assumptions ofisotropic diffuse illumination:
f (1D )f K ( ) f K ( )
iso 0  vol vol s geo geo s 
136 A ( ) (5)
Iso s  
D f K ( ) f K ( )
0  vol vol s geo geo s 
137 where D (unitless) is the proportionofdiffuse illumination for anabsorbing lower boundary
0
138 (Lewis and Barnsley1994;Lucht et al. 2000).TheMODIS BRDFshape derived fromclear-sky
139 observations canthen be used to derive albedo values inallskyconditions (Liu et al. 2009).
140 Most recently, the computationofMODIS instantaneous albedos was updatedto account forthe
141 effects ofmultiple scattering and anisotropic diffuse illumination(Románet al. 2010):
142 A ( ) f  f K ( ) f K ( ) (6)
 s iso vol vol s geo geo s
143 K ( )(1D )K ( )D K (7)
x s 0 x s 0 x
1 2 1
144 K   dK ( )N ( )d (8)
x  i x i sky i i i
0 0
1 2 1
145 K ( )  dK ( , )d (9)
x v  i x v i i i
0 0
146 where, N ( ) is the normalized skyradiance distributionunder anabsorbing lower boundary
sky i
147 and K is the N -weighted bihemisphericalintegral of K ( , ) (where x= vol or geo).
x sky x v s
148 Intrinsic albedo quantities derived fromRTLSR BRDF modelinversions canthen be combined
149 with in-situ estimates ofcloud fraction(< 0.6),550nmaerosolopticaldepth(AOD), solar zenith
150 angle (SZA), and D to compute clear-skyinstantaneous albedos fromMODIS, Landsat, and
0
151 CAR data.
152 The kernel-driven models employed bythe MODIS and Landsat albedo products are also
153 identified as partofthe heritage algorithms used to generatetheVisible Infrared Imager Radi-
154 ometer Suite’s (VIIRS) Land EnvironmentalDataRecords (EDRs);whichaimto ensure continu-
155 ityforAVHRR and MODIS observations byproviding hightemporalresolutionand wide area
156 coverage (Lee et al. 2010).TheVIIRS Land EDRs are currentlybeing evaluated byNASAand
157 NOAAto assess their suitability for operationalweather forecasting and long-termclimate moni-
158 toring applications (Románet al. 2011b).
159 2.2 Narrowband to Broadband Conversion
160 Since field-measured albedos are commonly measured as broadband quantities, anequiva-
161 lent setofbroadband albedos for MODIS and Landsat were generatedforthe UV-Visible (0.3 -
162 0.7 µm), NIR (0.7- 5.0µm), and the entire spectrumofsolar radiation([SW] 0.3- 5.0 µm),
163 based onempiricalrelations betweenground-based albedo measurements and satellite observa-
164 tions – cf.,Eqs.(11) and (15)inLiang (2001). Broadband albedos were also derived for CAR
165 measurements bycalculating the ratio ofbroadband upwelling radiative fluxto broadband
166 downwelling flux(Liang 2001;Liang et al. 2003):
max
A D ,d
s s
167 F  min  c A ,  (10)
s max i s i
D ,d i
s
min
168 Then, CAR narrowband-to-broadband spectralalbedo coefficients, c, were generated for each
i
169 spectralband bydetermining the downward fluxes (i.e. direct and diffuse) using an libraryof30
170 reflectance spectraofrepresentative land covers intheARM SouthernGreat Plains (SGP) region
171 (Trishenko et al. 2003):
172 A 0.160 0.291 0.243 0.116 0.112 0.081 0.0015 (11)
short 1 2 3 4 5 7
173 A 0.039 0.504 0.071 0.105 0.252 0.069 0.101 (12)
NIR 1 2 3 4 5 6 7
174 A 0.331 0.424 0.246 (13)
visible 1 3 4
175 The upward fluxes were directlyobtained fromthe libraryof30 SGPreflectance spectra;while
176 the downward fluxes were obtained byperforming multiple MODTRAN®5.1 (Berk et al. 2004)
177 runs for a broadrange ofsnow-free conditions (i.e., 21atmospheric visibility values for different
178 aerosolloadings, 2 atmospheric profiles, and solar zenithangles ranging from0°- 80°withthe
179 increment of1°).
180 2.3 CAR instrument performance during CLASIC’07
181 During the CLASIC’07 experiment, radiometric calibrationofthe CAR spectralchannels
182 was made at the NASAGoddardSpace Flight CenterRadiometric CalibrationFacility(GSFC-
183 RCF) (Butler and Barnes 2003).Adescriptionofthe calibrationscheme, using a series ofinte-
184 grating spheres withdiameters of1.83 m, 1.22 m, and 0.51 m, covering allofthe CAR’s spectral
185 channels, can be found inGatebe et al. (2007).The conversion fromDigitalNumbers (DNs)to
186 Level1 at-sensorradiances is determined fromthe instrument’s response for at least two known
187 radiance levels and thendetermining the instrument gain(slope) and offset (intercept)for each
188 wavelengthacrossthe sensor band pass.The estimated errors associated withthis radiometric
189 conversion varyfrom±1% to ±3%for allspectralchannels (Gatebe et al. 2003;Gatebe et al.
190 2007). Radiometric calibrationwas performed priorto and after CLASIC’07. Inthe past,to de-
191 termine a suitable calibration for a given flight during the experiment, a linear change between
192 the preflight and postflight calibrationwas assumed as a functionofonlythe number offlights
193 flownduring anentire campaign. Forthe CLASIC’07 experiment, however, boththe pre- and
194 post-calibrationcoefficients were averaged.This was found to be representative ofeach flight
195 scenario, and made it easier to account foruncertainties relatedto calibration, stability, and wa-