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On thepotentialof CHRIS-PROBA for estimating
vegetationcanopy propertiesfrom space
M.J.Barnsley���, P. Lewis
�, S.O’Dwyer
�, M.I. Disney
�,
P. Hobson�, M. Cutter
�, andD. Lobb
�
February16,2000
Manuscript submitted to: RemoteSensingReviews,
SpecialIssueon the ���� InternationalWorkshopon Multi-angularMeasure-
mentandModelling (IWMMM-2).
Submitted: 16February2000
Accepted:
Revised:
EnvironmentalModellingandEarthObservationGroup,Departmentof Geography, Universityof WalesSwansea,Singleton
Park,Swansea,SA28PP, UK�Correspondingauthor. Email: m.barnsley@swansea.ac.uk�RemoteSensingUnit, Departmentof Geography, UniversityCollegeLondon,26 BedfordWay, London, WC1H 0AP, UK SiraElectro-opticsLtd, SouthHill, Chislehurst,Kent,BR75EH,UK
1
Abstract
The CompactHigh ResolutionImagingSpectrometer(CHRIS), to be launchedon boardthe PROBA
(PRojectfor On-BoardAutonomy)satellitein 2001/2002,will provide remotely-senseddatafor terres-
trial andatmosphericapplications.Themissionis intendedto demonstratethepotentialof a compact,
low-cost,imagingspectrometerwhencombinedwith a small,agilesatelliteplatform. CHRISwill pro-
vide datain 19–62user-selectablespectralchannelsin the range400nmto 1050nm(1.25nm–11nm
intervals)at a nominalspatialresolutionof either25mor 50m. SincePROBA canbepointedoff-nadir
in boththealong-trackandacross-trackdirections,it will bepossibleto useCHRISto sampletheBidi-
rectionalReflectanceDistribution Function(BRDF) of the landsurface. This combinationof an agile
satelliteanda highly configurablesensoroffers theuniquepotentialto acquirehigh spatialresolution,
spectralBRDF datasetsand,from these,to studythebiophysicalandbiochemicalpropertiesof terres-
trial vegetationcanopies.It will alsoprovide an importantmeansof validatingsimilar datasetsfrom
other, coarserspatialresolutionsensors,suchasPOLDER2,MODIS andMISR. This paperpresents
key featuresof the instrument,andexploresthepotentialof CHRISfor estimatingcanopy biophysical
parametersfrom space.
Keywords: BRDF, albedo,biophysicalparameters,modelinversion,hyperspectral,Smallsat.
2
1 Introduction
A primary aim of terrestrialremotesensingis to derive accurateestimatesof selectedstate (e.g. leaf
biochemistry, Leaf Area Index (LAI) and the fraction of AbsorbedPhotosynthetically-Active Radiation
(f APAR)) andrate (e.g.Net PrimaryProductivity (NPP))variablespertainingto vegetationcanopies.This
informationis requiredby applicationsrangingfrom precisionagricultureto modellingtheglobalcarbon
cycle (Gao1994,Defrieset al. 1995).Attemptsto derive suchvariablesfrom optical remotesensingdata
have traditionally beenfoundedon the useof vegetationindices(VIs) (Baretand Guyot 1991,Myneni
et al. 1995).AlthoughVIs canbedesignedto belesssensitive to certaininfluencessuchassoil variations
(Huete1988),terrainslopeandaspect(Holbenet al. 1986),andatmosphericeffects(KaufmanandTanré
1992,Myneni andAsrar1994),they cannotadequatelyreflectthecomplex variationsin reflectancedueto
leaf/soil biochemistry, vegetationamountandcanopy structure.Furthermore,sincethe relationshipsthey
embodyareempirical,they mustbe ‘tuned’ for differentsensors,spatialresolutions,viewing/illumination
conditions,vegetationtypesetc. (Myneni et al. 1995,Verstraeteet al. 1996).
A more powerful and genericapproachis to invert physically-basedmodelsof canopy reflectance
againstmeasuredreflectancedata(Pinty andVerstraete1991,Kuusk1991,Knyazikhinet al. 1998),using
bothangularandspectralsampling.This is particularlytruefor determiningvegetationstructuralparame-
ters(Asneret al. 1998),solving for coupledatmosphere-surfaceparameterizations(Martonchik1997),or
for calculationof integratedpropertiessuchasalbedo(Wanneret al. 1997).Inversiongivesthepossibility
for directretrievalof biophysicalvariableslikeLAI, clumping,leafangledistributionandleaf/soilbiochem-
istry, aswell asradiometricpropertiessuchasfAPAR or albedo.Thereareseverallimiting factorshowever.
Thefirst is therequirementfor sufficient measurementsof thesurfaceBRDF, with theassociatedrequire-
mentfor goodcalibrationandatmosphericcorrection.Suchpropertiescanbereliably estimatedfrom the
remotesensingdatathemselvesif multi-angleor polarizationmeasurementsareavailable,usingeithera
separateatmosphericcorrectionprocedureor a coupledatmosphere-surfacereflectancemodel(Diner and
Martonchik1985,Rahmanet al. 1993,Liang andStrahler1994,Leroy et al. 1997). A seconddifficulty
is theexistenceof spatialheterogeneity, particularlygiventhatcurrentandfuturesensorswith multi-angle
capabilitiesoperateat so-called‘moderate’spatialresolutions,typically ������� (Leroy et al. 1997,Diner
et al. 1998,Justiceet al. 1998). Sub-pixel mixing of cover typesmakes inversionof physically-based
3
canopy reflectancemodelsdifficult or unreliable. Thirdly, thereis a needfor inversionalgorithmswhich
arebothfastandrobustenoughto avoid becomingstuckin localminima.
Several practicalsolutionsto the third problemhave beensuggestedin recentyears,namely(i) lin-
earizethe modelsor (ii) usenon-linearinversionschemeswhich placethe computationalemphasison
pre-calculationof reflectancedataratherthanrepeatedforwardmodelling‘on thefly’. Thelinearizationap-
proachis exemplifiedby modelswhich arebeingusedoperationallyin connectionwith datafrom NASA’s
MODIS sensor(Wanneret al. 1997,Justiceet al. 1998). Suchmodelsaresuitablefor normalizationof
angulareffects (Roujeanet al. 1992)andto estimatepropertiesinvolving angularintegrals,suchas the
surfacealbedo(Wanneret al. 1997).They provide a fastdescriptionof BRDF shapefrom sparsesamples,
andtheir linearity permitsimplicit modellingof heterogeneity. However, themodelabstractionperformed
during linearizationmakesthe relationshipbetweenthe derived modelparametersandthe actualcanopy
parametersunreliableor at least indirect. What is required,then, is a methodby which more realistic
scatteringmodelsmight beinverted.
Two practicalexamplesof the secondapproachmentionedabove are: (i) Artificial NeuralNetworks
(ANNs) (Kimeset al. 1998)and(ii) look-uptables(LUTs) (Knyazikhinet al. 1998).Both methodstrans-
fer theburdenof intensivecomputationout of theoperationalinversionprocess.ANNs providea practical
methodof invertingcomplex modelsthroughthe repeatedtraining of an inter-connectednetwork of pro-
cessingnodes,eachwith its own non-linearresponsefunction, usinga large variety of input data. The
ANN ‘learns’how to respondunderawiderangeof inputconditionswhicharepresentedto it. ANN meth-
odsareideally suitedto developingtheir own basisfunctionsadaptively. They can,therefore,compensate
for potentialerrorsin theassumptionsmaderegardingcanopy scatteringbehaviour. A numberof studies
have shown that ANNs, oncetrained,canprovide very rapid estimationof biophysicalparametersfrom
measurementsof canopy scattering(Kimeset al. 1998,Pragnereet al. 1999).
The LUT approachrequiresconstructionof a table of pre-calculatedspectraldirectionalreflectance
dataasa functionof thekey canopy biophysicalproperties.Operationalinversionis reducedto searching
throughtheresultingLUT for thereflectancevalueswhich mostcloselyfit theobservations(a minimaof
the error functionquantifyingthedifferencebetweenmeasuredandmodelledvalues)andthe retrieval of
thecorrespondingparametervalues.If themodelusedto populatetheLUT is altered,theLUT is simply
regenerated.Ancillary knowledgeof biometypeand/orlandcover canalsobeusedto further restrictthe
4
searchspace.A significantadvantageof both the ANN andLUT approachesto model inversionis that
arbitrarily complex scatteringmodelscanbeused,astheforwardmodellingis only performedonce,prior
to inversion.
If the issuesof atmosphericscatteringandsurfaceheterogeneity, discussedabove, canbe dealtwith
appropriately, it maybepossibleto usetheserapid inversiontechniquesto derive biophysicalparameters
from satellitesensormeasurementsof directionalreflectance.This paperpresentsaninvestigationinto the
useof the LUT approachto inversionof a non-linearmodelof canopy reflectance.The approachis ap-
plied to simulatedreflectancedatawith spectralanddirectionalsamplingcharacteristicof theforthcoming
CHRIS/PROBA satellitesensor.
2 CHRIS on board PROBA
ThePROBA small satellitemission(ESTEC1999),duefor launchin 2001/2002is a technologyproving
experimentdesignedto demonstratetheuseof on-boardautonomyin respectof a small,genericplatform
suitablefor scientificor applicationmissions.A numberof Earthobservation instrumentshave beenin-
cludedin the payloadto testplatform pointing anddatamanagementcapabilities;thereoffer interesting
opportunitiesto acquirenovel datasetsfor scientificinvestigations.The instrumentpayloadincludesthe
CompactHigh ResolutionImaging Spectrometer(CHRIS) (Sira Electro-OpticsLtd. 1999), a radiation
measurementsensor(SREM),a debrismeasurementsensor(DEBIE), awide angleEarthpointingcamera,
a startracker andgyroscopes.Thecombinationof CHRISandPROBA, in particular, providesa pointable,
spectrally-configurablesensoroperatingin thevisible andnearinfrared(Table1).
2.1 CHRIS-PROBA angular sampling regime
Multi-angularsamplingof a point on the Earthsurfacewill be achieved by tilting the PROBA platform
and,hence,pointing theCHRIS instrument.This featureis programmable,with across-tracktilting of up
to������� andalong-trackpointingof up to������� (equivalentat-groundangles)envisagedfor themission.An
approximateorbital/cameramodelwascreatedby modifying theXsatview software(Barnsley et al. 1994)
to provide a generalindicationof thepotentialangularsamplingregimeof CHRIS-PROBA underthese
conditions. The orbit is assumedto be circular, and the modeldoesnot allow for drift or slewing, but
5
typically providesinformationonexpectedsamplingbehaviour to within a few degrees.Althoughtheview
zenithanglesareessentiallyprogrammable,weassumeatypicalconfigurationwith at-groundzenithangles
of ��� , ��� ��� , ������� , and ������� for thepurposeof this paper.
Figure1 shows theangularsamplingregimefor a latitudeof �!� �#" . Thenineview anglesform a trace
acrosstheplot, which rotatesin azimuthrelative to thesolarprincipalplane(theplanecontainingthesolar
directionvector)over theyear. It is closeto thesolarprincipalplanein January, but nearlyperpendicularto
it at othertimesof theyear. Figure2 showssimilar informationfor ���$� " , with all potentialsamplesovera
14-dayperiodshown. Thevarioustracesover thisperiodcombineto providemorecompletesamplingover
theviewing hemisphere.Thesampledview angletracesdonotrotateasmuchasat lowerlatitudesandthere
aremoretracesowing to orbital convergencetowardsthepoles.PROBA’s potentialacross-tracksampling
of upto ������� meansthat,overa14-daysamplingperiod,CHRIScanpotentiallyprovidetracesof upto �����
in view zenithanglein thesolarprincipalplanefor sitescloseto theequator, eventhoughindividual traces
lie in thecross-principalplane. Thesolarzenithanglechangesonly slightly for sitescloseto theequator
(Figure1), but variesconsiderablyat higherlatitudes(Figure2). Table3 providesrepresentative datafor
thevariationin solarzenithangleatdifferentlatitudesandtimesof yearfor samplesgatheredovera14-day
period.Thevariationin solarzenithanglefor asingledaytraceis typically of theorderof ��� .
The CHRIS-PROBA samplingregime seemsto have good potential for samplingin or closeto the
solarprincipal plane,particularlyat moderatelatitudes,especiallyif samplesaregatheredover a 14-day
period. Thequality of samplingtowardstheequatorcanbeexpectedto vary over theyearasthegeneral
tracedirectionrotatesbut, even in the worst case,samplingof up to ����� in the solarprincipal planecan
potentially be achieved. This discussionassumestwo factorswhich are unlikely to hold in reality for
CHRIS-PROBA: (i) no angularsamplesarelost dueto cloud cover; and(ii) all potentialsamplescanbe
downloadedfrom thesatelliteto agroundreceiving station.
2.2 Potentials of CHRIS-PROBA
CHRISdatawill providethepotentialto overcomemany of thedifficultiesassociatedwith physically-based
modelinversionfrom satellitedata.First, aroundsevenviews of a fixedpoint on theEarthsurfacecanbe
obtainedin asingleorbitalover-pass.Thisoffersthepotentialto solvethemodelsfor theeffectsof boththe
atmosphereandthesurfacedirectionalreflectance.Second,thehighspatialresolutionof theinstrumentwill
6
maketheapplicationof physically-basedmodelssimpler, sincetherewill befewerproblemswith sub-pixel
mixturesof landcover type thanfor ‘moderate’resolutionsensors.Third, CHRIS/PROBA providesboth
hyperspectralanddirectionalsampling,which canbe expectedto have greaterinformationcontentthan
eitherdomainalone(Barnsley et al. 1997).In thefollowing section,weexploretheseissuesin amodelling
study, by attemptingto invert biophysicalparametersfrom simulatedCHRIS datausinga varianton the
LUT approach.
3 MAPPING BIOPHYSICAL PARAMETERS
3.1 Experimental design
A feasibility studyhasbeenconductedof the useof CHRIS-PROBA datawith a simpleLUT inversion
scheme(O’Dwyer 1999). The main elementsof this studyandits major conclusionsareexaminedhere.
Thestudyinvestigatedtheextentto which theangularandspectralsamplingprovidedby CHRIS-PROBA
wassufficient to derive estimatesof selectedsurfacebiophysicalparameters.In addition,theapplicability
of a variationon theLUT approachwasexplored. Thestudyassumedtheavailability of datarecordedin
18 spectralbandswithin the visible/near-infrared,in a numberof differentconfigurations.The Xsatview
camera/orbitmodel,describedabove, wasemployed. No limitations wereplacedon datadownloadrates
andcloud-freeconditionswereassumed.Thestudyalsoassumedthat therewereno errorsresultingfrom
uncertaintiesin theatmosphericcorrectionof thedata,althoughthestudydid considertheimpactof random
additivenoiseon theretrievedproperties.
Vegetationcanopy reflectancewassimulatedusingthemodeldevelopedby Kuusk(1991),usingawide
rangeof valuesfor themodel(biophysical)parameters.Themodelassumesa plane-parallelhomogeneous
vegetationcanopy whoselowerboundaryrepresentsthesoil surface.Themodelparametersrelevantin this
context are(i) leaf chlorophyll concentration,(ii) leaf structure,(iii) leaf areaindex, (iv) leaf clumping,
(v) relative leaf size,and(vi) soil brightness.Leaf sizeestimationrequiresgoodangularsamplingin the
retro-reflectiondirection (i.e., the ‘hot spot’). This was not well sampledby the angularconfiguration
consideredandsois not analyzedin detail in this paper.
7
3.2 Inversion strategy
A key concernin designinga LUT approachfor modelinversionis to keepthesizeof theLUT assmallas
possible.Knyazikhinet al. (1998),for example,achieve this by limiting therangeof possiblevaluesfor
thecanopy structuralandspectralvariablesasa functionof thebiomebeingconsidered.In addition,they
formulatethe problemfor canopy transmittance,absorptanceandreflectance,allowing theapplicationof
principlesof energy conservation. The informationthey storein the LUT is reconfiguredin wavelength-
independentterms,reducingthe LUT storagerequirementsandallowing the calculationof the required
termsfor any givenspectralfunctions.Properlyused,this typeof inversionschemecanprovide informa-
tion not only on the most likely valuesof the retrieved biophysicalproperties,but alsoon their expected
variation.However, sincethemainconsiderationhereis testingtheutility of CHRIS-PROBA’s anticipated
angularandspectralsamplingregimefor deriving estimatesof selectedsurfacebiophysicalproperties,we
will not considerthesepointsany furtherin this paper.
Instead,we start from the observation that a function storedon LUT grid points will, of necessity,
producequantizedresults.A simple,naïve LUT approachcanbeconsideredwhich quantizestherequired
reflectancefunction for all observation anglesinto " samplesper biophysicalproperty. If " is large,
the resultantparameterset will be quasi-continuous,but as the inversiontime is a function of %'& for %
properties,theprocessingtime would soonbecometoo great.Operationally, it maybepossibleto restrict
the propertysearch-spaceby consideringthe likely behaviour of a given biome,asnotedabove, but we
prefernotto imposethisconstrainthere.Rather, weattemptto reduce" to aminimumsuchthatit produces
acceptableresults(in computationalterms)while seekingto mimic thebehaviour of a large-" systemby
meansof linearinterpolationbetweenLUT grid points.This approachcanbejustifiedon thebasisthatwe
expectCHRIS-PROBA to provide a reasonableangularandspectralsamplingschemeand,therefore,the
large-scalevariationswithin the error function arelikely to be relatively well-behaved. Departuresfrom
this assumptionwill, of course,translateinto error in thesolutionwhich canbeattributedto the inversion
algorithm,on topof thosedueto theCHRIS-PROBA samplingscheme.
Theapproachoutlinedabove hasobvioussimilaritiesto thelinearizationapproachto modelinversion:
effectively, we aredesigninga piecewise linear inversionstrategy basedon the LUT. We assumethat a
coarseminimizationof the error function on the definedLUT grid pointswill localizethe solutionsuffi-
ciently for a linearizedsolutionaroundthe grid pointsto be valid. Following Knyazikhin et al. (1998),
8
wecouldpermitall grid-pointsolutionswithin anacceptabletolerancevalueto bepossiblesolutionsto the
inversion. Theselocalizationpointswould thenform a setof candidatesfor applicationof the linearized
inversion. The linearizedinversionleadsto a second-orderdescriptionof theerror function asa function
of the % variables.Here,a rangeof possiblesolutionscanbeconsideredfor eachpropertyby thresholding
this function. In this context, we choseto acceptonly the absoluteminimum grid point andthe absolute
minimumof thelinearizederrorfunction. In anoperationalalgorithm,however, it mightprovemoreuseful
to considera rangeof solutions.
3.3 Errors due to CHRIS-PROBA sampling
In this study, the LUT is populatedusingspectraldirectionalreflectancedatasimulatedusingthe model
of Kuusk (1991). We first considerthe effect of angularand spectralsamplingon the retrieval of two
major biophysicalproperties,namelyLeaf Area Index (LAI) andleaf chlorophyll concentration.CHRIS
angularsamplingovera1-dayperiodwassimulatedfor 4 differenttimesof yearandasitelocatedat ��� �(" .
Spectralsamplingwasassumedto beat regularintervalsover18 wavebandsin therange450nm–1045nm.
An alternativespectralsamplingschemeis investigatedbelow. In eachcase,reflectancedataweresimulated
with andwithout noise(at a magnitudeof up to 0.1 in reflectance).A high densitytwo-dimensionalgrid
wasdefinedto spanthefull rangeof parametervaluesallowedfor in theKuusk(1991)model. Simulated
reflectancevalueswerethencomparedto eachpoint in the grid andthe root meansquareerror (RMSE)
usedto createtheerrorsurfacesin Figure3.
Theresultsshow that theerror function is well-behavedand,in eachcase,theminimumregion within
which a linearizedsolutioncanbe found is clearly defined. The error surfacesaredistinctively different
for casesof low LAI (Figure3a and3b) andhigh LAI (Figure3c and3d), respectively. Although some
deviation of theretrievedvaluefrom therealvalueis to beexpected,owing to thequantizednatureof the
LUT, mostof theuncertaintycanbeattributedto variationsin theangularsamplingschemesused(Figures
3a–d). The additionof noisefurther increasesthe uncertaintyassociatedwith the identifiedminima and
leadsto largerdeviationsbetweentheretrievedandactualpropertyvalues(O’Dwyer 1999).
9
3.4 Effect of LUT grid size
The inversionschemedevelopedabove wasusedwith four differentLUT grid sizes,to retrieve four bio-
physicalparameters,namely(i) LAI, (ii) chlorophyllconcentration,(iii) leafangledistributionand(iv) the
first of the Price(1990)soil parameters.The densitiesat which the biophysicalpropertiesweresampled
variedbetweenLUTs. In thecoarsergrids,a reduceddensityof pointswasachievedfor LAI andchloro-
phyll by usingmultiplicativeincrementsin themodelparametervalues.This is because,althoughtheeffect
of soil brightnessis essentiallylinearfor singlescatteredradiation,termssuchasLAI andchlorophyllvary
almostexponentially. CHRISangularsamplingovera1-dayperiodwasusedto simulatereflectancedataat
��� �(" for asingledate,usingavarietyof parameterconfigurations.Tenreplicatesof eachdatasetwerecre-
ated,with randomnoiseaddedat a magnitudeof up to 0.1 in reflectanceto simulatea comparatively noisy
signal. Thefour differentgridswereusedto retrieve parametervaluesfrom thesimulateddatasets.Table
3 shows the meanvaluesof all four parametersretrieved usingeachgrid, andthe corresponding‘actual’
values(i.e., thevaluesusedto parameterizethemodel).
Theresults(Table3) show theinversionmethodto berobustto randomadditivenoiseupto about0.1in
reflectance.More importantly, they indicatethatthedifferencesin retrievedand‘actual’ valuesattributable
to differentgrid densitiesareminimal. More specifically, increasingthedensityof thegrid doesnotappear
to improvesignificantlytheaccuracy of retrievedparametervalues.Thissuggeststhatthecoarsestgrid can
be usedwith minimal lossof precision,while resultingin a significantreductionin processingtime. An
identifiabledrawbackof usingacoarsegrid,however, is thattheretrieval of theLAI seemsto breakdown at
high values.It seemslikely thatthis is a consequenceof canopy reflectancebeinginsensitive to increasing
LAI beyondsomethreshold(saturation)value,ratherthanaflaw in theinversionstrategy per se.
The sensitivity of the modelparametersto spectralanddirectionalsamplingalsoholdsperformance
implicationsfor theinversion:if any givenparameteris insensitiveto suchchanges,it will notberetrievable
from hyperspectral,multiple-view-angleimagerysuchasthatprovidedby CHRIS/PROBA. Practically, if a
parametercannotberetrieved,or if it hasa low sensitivity to samplingchanges,it shouldbeexcludedfrom
the inversion,andkeptat a plausiblefixedvalue. Thesensitivity of parametersmayalsovary depending
on canopy type; for obviousreasons,thesoil parametersaremorelikely to besensitive to thespectraland
directionalsamplingin canopieswith a low LAI. An ideal implementationwould comprisean adaptive
grid, wherethemodelparametersareincludedor excludeddependingonassessmentsof theirsensitivity to
10
thesamplingscheme.
Theeffectsof variationsin the directional(angular)sampling— asa function of theday-of-yearand
latitude— on parameterretrieval usinga coarseLUT werealsoexamined(O’Dwyer 1999).Resultsshow
that,asexpected,combiningsamplesovera two-weekperiodallowsconsistentlybetterparameterretrieval
thanthatpossiblewith 1-daysampling.This is a resultof thelargernumberandbroaderrangeof viewing
andillumination anglessampled,particularlyin thesolarprincipalplane(Figure2). For thesamereason,
the angularsamplingregimescharacteristicof higher latitudessitesresult in moreaccurateretrievals of
themodelparametersthanfor sitescloserto theequator. Thedistribution of solarzenithanglesseemsto
becomeincreasinglyimportantastheoverallnumberof samplesdrop.
3.5 Effect of spectral sampling
The effect of spectralsamplingon biophysicalparameterretrieval usinga coarseLUT grid was investi-
gatedthroughuseof an alternative spectralsamplingscheme,in which 18 wavebandswere selectedat
‘critical’ pointsalongthe spectrum.Theseincludedsix bandsaroundthe peakin the visible wavelength
reflectance(500nm–570nm;controlledby leaf chlorophyllconcentration),six bandsplacedalongthe‘red
edge’(680nm–770nm),andtwo in thenear-infrared(at 990nmand1020nm,respectively). Angularsam-
pling wasassumedto beovera1-dayperiodfor asingledayandasitelocatedat ��� �(" . Randomnoise(ata
magnitudeof 0.1reflectance)wasaddedto tenreplicatedatasets.Table6 shows themeanvaluesretrieved
for eachof thefour parametersusingtheinversionstrategy describedabove.
The resultsshow that targeting the wavebandscan be beneficialto thoseparametersmost sensitive
to spectralchanges.LAI, especially, is moreaccuratelyretrieved usingthe alternative spectralsampling
scheme,althoughtheprocedurebreaksdown at high LAI values,asbefore.Similarly, retrieval of chloro-
phyll concentrationis improved,althoughthedifferencesareminor. It is thoughtthatby combiningdirec-
tional samplesover a 14-dayperiodandusingtargetedwavebandstheimprovementsin theaccuracy with
which thebiophysicalparametersareretrievedwouldbemoresignificant.
11
4 DISCUSSION AND CONCLUSIONS
Theresultsabove indicatethata relatively coarseLUT canbeusedfor inversionif interpolationbetween
LUT grid pointsis used.TheresultingLUT typically requiresonly 3–5samplesperparameterto achieve
an acceptableaccuracy andso is very fastto invert. The inversionmethodalsoproved robust to random
additivenoiseup to about0.1in reflectance.Theparameterretrieval is sensitiveto variationsin theangular
samplingschemeasanticipated.Combiningsamplesover a two weekwindow permitsthe inversionof
modelparametervaluesto a high degreeof accuracy (errorsof only a few percentfor mostparameters);
althoughasthe precisenatureof the angularsamplingvariesasa function of time of yearand latitude,
the absoluteaccuracy valueswill vary accordingly. The angularsampling,and thereforethe parameter
retrieval accuracy, provided by CHRIS/PROBA over mostof Europeis generallygood(ignoring, for the
moment,theeffectsof cloudcoveranddatadownloadlimitations),generallyimproving towardsthepoles.
Thereis evidenceto suggestthat theaccuracy of parameterretrieval canbeimprovedby carefulselection
of appropriatespectralwavebands,targetingknown featuresof soil andvegetationspectra.
The resultssuggestthat the interpolatedLUT methoddevelopedin this study hasexcellent poten-
tial for biophysicalparameterretrieval basedon the angularandspectralsamplingcharacteristicsof the
CHRIS/PROBA mission. Sincethe interpolatedLUT procedureessentiallycomprisesa piecewise linear
surface,thereare inevitable discontinuousat the boundariesbetweenthe LUT grid points. This creates
inconsistenciesin the applicationof the inversionalgorithm,so investigationsinto the potentialof using
differentfunctionsimposingcontinuitymaybe beneficial.As theLUT approachis not limited to simple
canopy reflectancemodelsit should,in future,bepossibleto populatetheLUT usingnumericalsolutions
to theradiative transportproblem.Finally, thesuccessfulapplicationof theinterpolatedLUT schemeused
in thispaperin conjunctionwith datafrom CHRIS/PROBA is reliantupon(i) satisfactoryatmosphericcor-
rectionof thosedata,(ii) accurateco-registrationof theimagesacquiredatdifferentsensorview angles,and
(iii) theability to downloadsufficient data,given the currentlimitations on dataratesandon-boardmass
memory.
12
ACKNOWLEDGEMENTS
Theauthorswish to acknowledgethesupportof variousbodiesin this work, notablytheNERCin respect
of O’Dwyer (MSc. studentshipat UCL), Disney (Ph.D.studentshipat UCL) andBarnsley andHobson
(throughgrantnumberGR3/11639).TheUniversityof LondonIntercollegiateResearchServices(ULIRS)
provide financialsupportto the UCL RSU, wherethe simulationand inversionstudieswereconducted.
CHRISwasdesignedanddevelopedbySiraElectro-OpticsLtd, andfundedbySiraandBNSC.TheCHRIS-
PROBA missionis alsosupportedby ESA.We would alsolike to thankDr. Jeff Settleof NERCESSCfor
providing informationandadvice.
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Figure Captions
Figure1 Polarplots of viewing anglesrelative to the solarprincipal planeandassociatedsolarzenith
anglesfor 1-daysamplingat a latitudeof �!��� " andfour differenttimesof year.
Figure2 Polarplots of viewing anglesrelative to the solarprincipal planeandassociatedsolarzenith
anglesfor 14-daysamplingat a latitudeof ��� �(" andfour differenttimesof year.
Figure3 Three-dimensionalerror surfacescreatedusinga high densityLUT andassociatedidentified
andactualminimashownasverticallinesfor 1-daysamplingatalatitudeof 52N.Figures(a)to
(d) show variationsin the‘shape’of thesurfacesproducedby differencesin angularsampling
andLAI.
17
Table Captions
Table1 CHRISinstrumentcharacteristics.
Table2 Rangeof solarzenithangles(degrees)overa14-daysamplingperiodat four latitudesandfour
timesof year.
Table3 Meanretrieved values(andassociatedactualvalues)of LAI, chlorophyll concentration,leaf
angledistribution andthefirst of Price’s soil parametersusingdifferentLUT grid sizesbased
on 1-daysamplingat a latitudeof ��� �(" .
Table4 Meanretrieved values(andassociatedactualvalues)of LAI, chlorophyll concentration,leaf
angledistributionandthefirst of Price’ssoil parametersusingtargetedwavebandsandasingle
coarseLUT grid, basedon 1-daysamplingat a latitudeof ��� �(" .
18
Figure1: Polarplotsof viewing anglesrelativeto thesolarprincipalplaneandassociatedsolarzenithanglesfor 1-daysamplingat a latitudeof �!� �#" andfour differenttimesof year.
(a)1 January (b) 1 April
(c) 30 June (d) 28September
19
Figure2: Polarplotsof viewing anglesrelativeto thesolarprincipalplaneandassociatedsolarzenithanglesfor 14-daysamplingat a latitudeof ��� �#" andfour differenttimesof year.
(a)1 January (b) 1 April
(c) 30 June (d) 28September
20
Figure3: Three-dimensionalerrorsurfacescreatedusingahigh densityLUT andassociatedidentifiedandactualminima shown asvertical lines for 1-daysamplingat a latitudeof ����� " . Figures(a) to (d) showvariationsin the‘shape’of thesurfacesproducedby differencesin angularsamplingandLAI.
(a) Low LAI/chlorophyll content; Januaryangularsam-pling.
(b) Low LAI/chlorophyll content;Juneangularsampling.
(c) High LAI/chlorophyll content; Januaryangularsam-pling.
(d) High LAI/chlorophyll content;Juneangularsampling.
21
Table1: CHRISinstrumentcharacteristics
PROBA Orbital CharacteristicsOrbit Virtually circular, Sun-synchronousAltitude )*��+*�,�Inclination +�).-0/ �Orbital period 101.3minutesEquatorialcrossingtime 10:30amPlatformrevisit period 14days
CHRIS Spatial CharacteristicsSwathwidth �!).-1�*�,�Typical imagearea �!+*�,�324�!+*���Spatialresolution ���$� –�����
CHRIS Spectral CharacteristicsSpectralrange 56�!��78� – �!�*����78�Spectralresolution ��-9���$7:� – ���7:�Spectralbanddisplacement User-selectableNumberof selectablespectralbands
Full swathwidth �!) bands@ ���$� spatialresolution���<;=��� bands@ �����spatialresolution
Half swathwidth �*/ bands@ ���$� spatialresolution
PROBA PointingCapabilityAcross-track ����� �Along-track �������
CHRIS Data CharacteristicsDataper �>+*�,�32?�!+*�,� scene �!�.� MbitsQuantization � � bits (CDSnoisereduction)SNR ����� (at targetalbedoof �.-0� , baselineconditions)DataI/F �@2A��� Mbits/sEffectivedatarate �!) Mbits/s(link overhead)
CHRISPower, WeightandDimensionsPowerConsumption BC�!�*D primarypowerWeight BC� ���FEDimensions /�+����G�32H�����$�G�I2A�������G�
Table3: Rangeof solarzenithangles(degrees)over a 14-daysamplingperiodat four latitudesandfourtimesof year.
Dayof Year Latitude
��� �(" 5*� �#" �!� �(" �*� �KJ1stJanuary 70-80 65-70 35-50 5-301stApril 40-55 35-45 15-30 50-6530thJune 25-40 20-35 15-30 50-65
28thSeptember 55-65 45-60 20-40 25-40
22
Table4: Meanretrievedvalues(andassociatedactualvalues)of LAI, chlorophyllconcentration,leaf angledistribution andthefirst of Price’s soil parametersusingdifferentLUT grid sizesbasedon 1-daysamplingat a latitudeof ��� �(" .
ActualValues RetrievedValuesGrid 4x3x3x3 Grid 5x4x4x4 Grid 7x6x4x4 Grid 11x10x8x8
LAI:0.5 0.24 0.44 0.40 0.360.8 0.46 0.44 0.65 0.651.5 1.46 1.41 1.33 1.302.1 1.54 1.30 1.63 1.843.2 3.32 3.40 3.20 2.995.3 6.05 6.17 5.16 5.04
Chlorophyll39 37.60 31.71 31.09 34.4954 59.10 48.68 46.63 46.9869 70.10 57.72 59.66 57.6581 74.35 71.18 69.04 68.7787 82.76 78.52 75.97 75.33
Leafangledistribution0.6 0.28 0.97 0.774.8 3.90 5.95 4.697.9 6.66 7.91 7.65
Soil parameterLAI: 0.6
0.13 0.16 0.15 0.140.22 0.23 0.22 0.230.31 0.31 0.31 0.31
LAI: 5.30.13 0.19 0.21 0.200.22 0.19 0.21 0.200.31 0.19 0.21 0.20
23
Table6: Meanretrievedvalues(andassociatedactualvalues)of LAI, chlorophyllconcentration,leaf angledistributionandthefirst of Price’s soil parametersusingtargetedwavebandsanda singlecoarseLUT grid,basedon 1-daysamplingata latitudeof ��� � " .
Property/Parameter Actual Values MeanRetrievedValuesLAI 0.5 0.37
0.8 0.921.5 2.292.1 3.403.2 7.535.3 10.42
Chlorophyllconcentration 39 30.254 49.869 76.181 85.787 89.3
Leafangledistribution (eccentricity) 0.6 1.164.8 5.997.9 9.37
First soil parameterLAI: 0.6 0.13 0.20
0.24 0.320.36 0.49
LAI: 5.3 0.13 0.370.24 0.400.36 0.44
24
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