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.

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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