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CLASSIFYING VINEYARDS FROM SATELLITE IMAGES: A CASE STUDY ON BURGUNDY’S CÔTE D’OR

JorgeR.DUCATI1,2,4,*,MagnoG.BOMBASSARO1 andJandyraM.G.FACHEL3 1:CentroEstadualdePesquisasemSensoriamentoRemotoeMeteorologia,UniversidadeFederaldoRioGrande doSul,Av.BentoGoncalves9500,CEP91501-970,PortoAlegre,Brazil 2:DepartamentodeAstronomia,InstitutodeFísica,UniversidadeFederaldoRioGrandedoSul, Av.BentoGoncalves9500,CEP91501-970,PortoAlegre,Brazil 3:DepartamentodeEstatística,InstitutodeMatemática,UniversidadeFederaldoRioGrandedoSul, Av.BentoGoncalves9500,CEP91501-970,PortoAlegre,Brazil 4:Visitingprofessor(2011),ÉcoleSupérieured‘Agricultured’Angers,GroupeESA,55rueRabelais, 49007Angers,

Abstract Résumé Aim:TouseRemoteSensingimageryandtechniquesto Objectif :Différencierlescatégoriesdeparcellesdu differentiatecategoriesofBurgundianvineyards. vignoblebourguignonparl’utilisationd’imagessatellites. Methods and results :Asampleof201vineplotsor Méthodes et résultats:Unéchantillonde201parcellesou “climats”fromtheCôted’OrregioninBurgundywas “climats”delaCôted’OrenBourgogneaétésélectionné, selected,consistingofthreevineyardcategories(28Grand formépartroiscatégoriesdevignobles(28GrandCru, Cru,74PremierCru,and99Communale)andtwogrape 74 PremierCruet99Communale)etdeuxcépages(Pinot varieties(PinotnoirandChardonnay).Amaskformedby noiretChardonnay).Unmasquecomposéparles thepolygonsofthesevineplotswasmadeandprojectedon polygonesdecesparcellesaétéconstruitetensuiteprojeté foursatelliteimagesacquiredbytheASTERsensor, surquatreimagessatellitesdelarégion,collectéesparle coveringtheCôted’Orregioninyears2002,2003(winter senseurASTERen2002,2003(enhiver),2004et2006. image),2004and2006.Meanreflectanceswereextracted Lesréflectancesmoyennespourlespixelsàl’intérieurde frompixelswithineachpolygonforeachofthenine chaquepolygoneontétécalculéespourchaqueannéeet spectralbands(visibleandinfrared)coveredbyASTER. pourchacunedesneufbandesspectrales(visibleet Thedatabasehadatotalof797reflectancespectra infrarouge).Labasededonnéesestforméepar797spectres assembledoverthefourimages.Statisticaldiscriminant deréflectance.Desanalysesdiscriminantesdupourcentage analysisofpercentageclassificationaccuracywasmade d'exactitudeduclassementontétéfaitesséparémentpour separatelyforCôtedeNuitsandCôtede,andfor CôtedeNuitsetCôtedeBeauneetpourchaqueannée.Les eachyear.Resultsshowedthatforindividualyearsand résultatsmontrentquelaclassificationauniveaudes Côtes,classificationaccuracyforvineyardcategorywasas catégoriesdevignobleaétépréciseentre66,7 %(Beaune highas73.7 %(Beaune2002)andaslowas66.7 % 2003)et73,7 %(Beaune2002).Aucunedifférence (Beaune2003).Therewerenosignificantdifferencesin significativedanslaprécisionn'aététrouvéeentreles accuracybetweenspring,summerandwinterimages. saisonsdel’annéedesimages(printemps,étéethiver). ClassificationaccuracyforgrapevarietyinCôtedeBeaune PourlaséparationdescépagessurlaCôtedeBeaune,la overthefourstudyyearswasbetween73.5 %forPinot précisiondel’analysediscriminanteaétéentre73,5 %pour noirclimatsin2004and91.9 %forChardonnayclimatsin lesparcellesdePinotnoiren2004et91,9 %pourles 2006,includingthewinterimage.Concerningthe parcellesdeChardonnayen2006,comprisel’image vegetationindexNDVI,therewerenosignificant d’hiver.Pourl’indicedevégétationNDVI,aucunes differencesbetweenvineyardcategories. différencessignificativesentrelescatégoriesn’ontété Conclusions:Satellitedataisshowntobefunctionalto trouvées. revealvineyardquality.Spectraldifferencesbetween Conclusion:L’analysespectraledesdonnéessatellitaires categoriesofBurgundianvineyardsareatleastpartially peutdonneruneindicationdelaqualitéd’unvignoble duetoterroircharacteristics,whicharetransmittedtovine bourguignon.Lesdifférencesspectralesentrelescatégories andvinecanopy. devignoblessontduesaumoinsenpartieauxpropriétésdu Significance and impact of the study :Thiswork terroirtransmisesàlavigne. indicatesthatRemoteSensingtechniquescanbeusedasan Signification et impact de l’étude:Cetravailmontreque auxiliarytoolforthemonitoringofvineyardqualityin latélédétectionpeutêtreunoutilsupplémentairepour establishedviticulturalregionsandforthestudyofquality l’observationderégionsviticolesétablies,etaussipour potentialinnewregions. l'étudedelaqualitépotentielleattenduedansdenouvelles Key words :Burgundianclimats,RemoteSensing, régions. vineyardsspectra,leafreflectance,satelliteimages Mots clés:climatbourguignon,télédétection,spectredes vignobles,réflectancedesfeuilles,imagessatellites

manuscript received 9th September 2014 - revised manuscript received 19th July 2014

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INTRODUCTION techniquesallowsnotonlytheseparationof vineyardsfromothervegetation,butalso,toacertain TheobservationofEarthfromremoteplatformslike degree,theidentificationofgrapevarieties(Cemin airplanesorsatelliteshasprovedtobeapowerful andDucati,2011).Thesepossibilitieswerealready resourceforlandstudies,withapplicationsto perceivedfromlaboratorymeasurements(Lacaret geology,agriculture,environmentalsciences,urban al.,2001),butnowitbecomesclearthatsatellite andmarinemonitoring,andmanyotherfields. imageshavetheirownpotentialinviticultural Presently, most of these Remote Sensing studies.Afterusingsatelliteimagestostudy investigationsareperformedusingdigitalimages vineyardsinFrance(Bordeaux,Champagne,Loire), collectedfromsatellites,whichprovidelow-cost ChileandBrazil,wenowfocusourstudieson data,withtheadvantageofbeingre-acquiredatnew Burgundy´sCôted’Or.Thischoiceisjustifiedby over-flights.ThetypicalRemoteSensingprocess threebasicfactors: involvescamerasandsensorsaboardthesatellite, whichcollectsunlightreflectedfromtheEarth’s a)ThehierarchicaldivisionoftheBurgundian surface;duringreflectionbytypesorclassesof vineyardishistoricalandemblematical,havingbeen surfacecover,likesoilorvegetation,thesolar theobjectofcountlessstudies,butuptothepresent spectrumundergoesmodifications.Theresulting dayfewpapers,ifany,haveusedobservationsfrom reflectancespectracarrycharacteristicfeaturesofthe space; classes present in the imaged surface and identificationoftheseclassesispossible.For b)ThetypicalsizeofvineparcelsinBurgundyisof example,reflectancespectrafromplantsare theorderoffewhectares,beingadequatelyresolved characterizedbylowreflectanceinvisiblelight,with bymultispectralimageslikethosefromASTER apeakat550nmduetochlorophyll,whichisthe sensor; reasonforthegreencolorofvegetation;atnear- infrared(NIR)wavelengthsthereisanabrupt c)TheCôted’Orregionisgenerallyorientedfacing transitiontowardsstrongerreflectances(theRed east(PitiotandServant2010;Atkinson2011),andso Edge);andatlongerwavelengths(theShort-Wave mostvineyardsreceivethemorningsunlightinfairly Infrared,SWIR)reflectancefalls,carryingthetypical equalinclinationsofsolarrays.Thisfactisrelevant featuresofabsorptionbywaterat1,400and sincetheASTERimagergetsdatainthemorning 1,900 nm.Thespectralsignaturesofmineralsor (around10h30AM).Theilluminationofparcels, waterarequitedifferent,andthisallowsthe whichingeneralareongentleslopes,tendstobe identificationofclassesofsoilandlandcoverin homogeneous;thisperceptionwasgainedduring RemoteSensingimageswhichhavetheadequate severalfieldtripstotheregioninthelastyearsbythe spectralsensitivity;forcomprehensivereviewson firstauthor. theapplicationsofRemoteSensingimagerytoland InBurgundy,thehierarchyofGrandCru,Premier monitoring,seeJensen(2007)orCampbelland Cruandmoregenericappellations(Côtes,Villages, Wynne(2011). Communales,etc.)seemstobelinkedtosoil Thisworkdealswithreflectancespectraofvineyards characteristics,whichareattheveryrootofthe asaparticularclassofvegetation.Applicationsof terroirconcept(VanLeeuwenandSeguin,2006). RemoteSensingtechniquestovineyardstudiesare Fromaprincipalcomponentanalysis,Wittendal stillintheirinfancy.Upuntilnow,themajorityof (2004)gaveweighttoawidespreadperception, studieshavefocusedonprecisionviticulture indicatingthatmostGrandCrusoilshaveaparticular managementinprivatepropertiesoflimitedsurface structurethatissignificantlydifferentfromthesoils and,forthisreason,arebasedonairbornesensors, ofothercategories.Therefore,theobjectiveofthis eithermultispectralorhyperspectral(Bramleyand investigationwastoverifyifthesequalitycategories, Proffitt,1999;Zarco-Tejadaet al.,2005).Remote whicharetransmittedfromsoiltowine,arealso Sensingimageryfromsatellitescoversmuchlarger transmittedfromsoiltovineleavesandiftheycanbe areasandissuitableforregionalsurveysand detectedinthespectralinformationcontainedinthe monitoring.Thisfieldofresearchisnewandmuch images.Thisisbecausetheobservationparameterin groundbreakingworkhastobedone.Inthisaspect, digitalimages,thereflectance,originatesmainlyfrom wereportedinaseriesofpapersstudiesperformed vineleavesreflectingsunlight,ifweareusingnon- overseveralviticulturalareasinEuropeandSouth winterdata.Atthehighplantdensityusedin America(DaSilvaandDucati,2009;Blauthand Burgundy(upto10,000vines/hectare),thesoilis Ducati, 2010 ; Ducati et al., 2014). It was almostentirelycoveredbytheplantcanopy;besides, demonstrated that Remote Sensing data and atthemomentofimageacquisition(10h30AM),

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thereisanimportantprojectionofshadowbetween than40pixelsof225 m2 each),withadequate vinerowsandlittlesunlightisreflectedfromthe geometry(themoresquare,thebetter),andevenly shadowedsoil.Thispointwillbeanalysedingreater distributedovertheCôted’Orregion.Themapsof detailintheDiscussionsection,butfornow,itcanbe CôtedeNuitsandCôtedeBeaunebyPitiotand stated,forpracticalpurposes,thatthereflectedlight Poupon(2009)wereusedfortheselectionofvine inimagesacquiredduringthevegetativecyclecomes parcels.Thefinalsamplewasformedby201plots: essentiallyfromvineleaves. 28GrandCru(10inBeaune,18inNuits), 74 PremierCru(51inBeaune,23inNuits),and MATERIALS AND METHODS 99 Communales(53inBeaune,46inNuits),thislast 1. Image acquisition categorycorrespondingtovineyardswhicharelieu- ditsandwhichareneitherGrandCrunorPremier ImagesfromtheASTERsensor,whichisaboardthe Cru.Thecompletelistofthesevineyardsisgivenin Terrasatellite,wereacquiredthroughtheNASA AppendixA. websitereverb.echo.nasa.gov/reverb,inthecontext ofaresearchprojectsubmittedbytheauthorsand A maskcontainingthepolygonsofthesevineyards approvedbyNASA.Extensiveinformationonthe wasgeneratedbasedonthemapsoftheregion, ASTERsensorcanbefoundinAbramset al. (2002). whichweregeoreferencedinthesameframeof Thespatialresolution,orpixelsizeoftheimages,is referenceasthesatelliteimages.Thismaskwasthen 15metersatthefirstthreespectralbandsinthe superposedontheASTERimages,onwhichthe visibleandnear-infrared(VNIR)subsystem,and selectedparcelscouldbeidentified.Foreachclimat 30 metersatthesixbandsintheSWIRsubsystem. orlieu-dit,themeanreflectanceofallpixelsinsidea Thesecombinedfeaturesallowdeeperanalysisof polygonwascalculatedforeachoneofthenine reflectancecomparedwiththoseofotherorbital spectralbands.Greatcarewastakenindoingthe sensorslikeLandsat,CBERSorALOS.Imagedates masktoensurethateachpolygonwasentirelyinside wereSeptember19,2002 ;June9,2004 ;and theclimat,avoidingcontaminationfromother September6,2006,correspondingtolatespringor spectralclasses,likeroads,buildingsorother latesummerintheNorthernhemisphere.Tolookfor vegetationclasses.Theselectedpolygonshadfrom puresoileffects,wealsoanalysedawinterimage 40to140pixels,correspondingtoareaswithin (February15,2003),wherenograpeleaveswere climatsfrom0.9toabout3hectares,sinceapixelhas coveringthesoilandhidingitfromsatelliteview. 15 mx15 mor225 m2. Theusualtreatmentswereapplied :atmospheric correction(Berket al.,2006)andcompensationfor Thefinalsamplehad797reflectancespectraof thecrosstalkeffect(IwasakiandTonooka2005).An 201 climatsofthethreevineyardcategories.The additionaltreatmentwasmadeconcerningpixelsize; samplewasdividedineightfiles,onefileforeach toruntheatmosphericcorrectionalgorithmitis year(2002,2003,2004,and2006)andforeachCôte. necessarythatallninebandsbeatthesamespatial Theexactnumberofeachcategoryineach resolution(either15metersor30meters).When year/imagevariedslightlybecausevariationsin studyingvineparcelsofafewhectares,thespatial imagequalitysometimesmadeitimpossibleto resolutionhastobeashighaspossible,andtotake accuratelyvisualizecertainclimats.Thesamplealso fulladvantageofthethreehigherresolution containedinformationongrapevariety.InCôtede (15 meters)bands,weresampledthesix30-meter Nuits,only2outofthe87selectedparcelswere bandstomatchthe15-meterpixels,aprocedure Chardonnay,expressingthemassivedominanceof alreadyusedanddiscussedelsewhere(Mather,1999; PinotnoirinthenorthernpartofCôted’Or.Onthe Altaweel,2005). otherhand,inCôtedeBeaunemanyparcelsofthe CommunalecategoryandsomeofPremierCruwere 2. Vineyard selection plantedwithbothgrapevarieties.Intheeightfiles formingthedatabase,eachlinecontainedtheclimat TheCôted’Orregionhasabout1,200vineparcels numberandname,thecategorycode,thegrape whichareclimatsorlieu-dits.Thisisalargenumber, variety,andallninemeanreflectancevalues. andtostudythemallwouldbeamajorconstrainttoa projectwhoseprimeobjectiveistotestthespectral Figure 1showshowsomeclimatsappearinthe recognitionofvineyardcategories.Thus,welooked satelliteimage. forasamplewhichwouldberepresentativeofthe 3. Statistical analysis threecategories(GrandCru,PremierCruand Communales),havingasselectioncriteriaparcels Astatisticaldiscriminantanalysiswithrespectto withadequateareas(nottoosmall,i.e.,withmore vineyardcategorywasperformedusingthenine

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Figure 1. Examples of polygons superposed on an ASTER satellite image and the corresponding maps. Some selected climats in two areas of Côte de Nuits are shown.

spectralbandsofreflectancespectraforeachyear DifferenceVegetationIndex(NDVI),definedas andeachCôte,sinceapreliminaryanalysisusingthe (Tucker,1979): wholesamplerevealedthatseasonalandspatial (1) differencesincreasednoiselevel.

Anadditionalanalysisusingone-wayANOVAwith whereRIR isthereflectanceatthenearinfrared, Tukeypost-hocmultiplecomparisonstestwasmade, which in ASTER subsystems is band 3 lookingforwhichspectralbandcouldbemore (0.760–0.860 µm),andRVIS isthereflectanceatred relevanttoseparateonevineyardcategoryfromthe (0.630–0.690µm),whichisband2.NDVIvalues others. werecalculatedforall797spectraandanANOVA analysiswasmadecomparingthemeanvaluesof Anotherdiscriminantanalysiswasperformedwith NDVIwithrespecttovineyardcategory. respecttograpevarietyfortheCôtedeBeaunefiles. Sinceeachimagewasstudiedseparately,we RESULTS includedthewinterdataintheanalysis,althoughin wintertherearenovineleavestoreflectsunlight.We Withrespecttovineyardcategory,theapplicationof alsoperformedtwoseparateanalysesforgrape discriminantanalysistoCôtedeBeaune(Table1) varieties.ThefirstonewaswithPinotnoir andCôtedeNuits(Table2)showedthatfor (49 parcels)andChardonnay(37parcels);all28 individualyearsandCôtes,classificationaccuracy mixedparcelswereexcludedfromthisanalysis.The wasashighas73.7 %(Beaune2002)andaslowas secondanalysisconsideredallthethreegrape 66.7 %(Beaune2003). groups : Pinot noir, Chardonnay and Pinot/ ResultsfromtheANOVAtestwhichinvestigatedthe Chardonnay.Preliminarytestsshowedthatthe discriminantanalysisperformedbetterforthepure morerelevantspectralbandsforvineyardcategory groups.Thiswasexpectedbecauseinparcelswith discriminationwereasfollows:GrandCruclimats spectralmixtureatpixellevel,theexactvariety withPinotnoirgrapeshadhighermeanvaluesof proportionsareunknown.Therefore,werestricted reflectanceinbandsB2,B4,B5,B6,B7andB8;that theanalysistothemono-varietalvineyards. is,inthesesixbands,GrandCruplotsweredifferent frombothPremierCruandCommunaleplots. Anadditionalanalysiswasperformedconcerninga NumbersarelessclearforChardonnayGrandCrus. vegetationindex,inthiscasetheNormalized Nosystematicdifferenceswereobserved,andin

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Table 1. Discriminant analysis (vineyard category) for Côte de Beaune

Percentage of original grouped cases correctly classified: 2002, 73.7 %; 2003, 66.7 %; 2004, 71.2 %; 2006, 70.5 %.

somebands,CommunalewasseparatedfromGrand higherforbothlatesummerimages(Sept.2002and CruandPremierCruandinothers,GrandCruwas 2006),withmeanvaluesbetween0.62and0.64. separated,withhigherorlowervalues,fromtheother However,therewerenosignificantvariationsof classes.ItseemsthatforChardonnay,theGrandCru NDVIcomparingvineyardcategory. categoryisseparatedinamorecomplexway. DISCUSSION GrapevarietyresultsforCôtedeBeaune,overthe fouryears,arepresentedinTable3.Accuracywasas 1. Factors building up reflectance lowas73.5 %forPinotnoirparcels(2004)andas Aninitialpointtobediscussedisifreflectancedata highas91.9 %forChardonnayparcels(2006).Itis fromavineyard,acquiredbyasatellite,contains worthofnotethattheseparationofPinotnoirfrom informationonlyfromvineleavesorifthis Chardonnayvineplotswasfairlywelldoneeven using2003winterdata(79.6 %forPinotnoirand informationiscontaminatedbyotherradiantsources, 89.2 %forChardonnay),performingbetterthanJune likesoil,roads,orbuildings.Asforpixelslocated 2004dataandwithanaccuracysimilartolate outsidethevineparcels,wehavealreadymadeit summerdatafor2002and2006. clearthatgreatcarewastakentopreciselyextract datawellinsidetheselectedparcels.Thequestionis ResultsforNDVIareshowninTable4.Not ifwithin-vineyardfeatureslikesoil,terrainslope,and surprisingly,NDVIvaluesforallvineyardcategories roworientationplayrelevantrolesinreflectance weresmallerforthe2003winterimage(meanvalues responses.Inapreviouspaperwestudiedthese around0.25),beingtypicalofclasseslikesoil,which influencesinadifferentregion,theLoireValley isexposedatthisseason.Theindexincreasedasthe (Ducatiet al.,2014).Wewillnowpresentashort vegetativecycleprogressedandwas0.46fortheJune summaryofthatlengthystudy.Wearguedthatthe 2004imageinthetworegions.TheNDVIindexwas soilcontributiontoreflectanceisstronglydependent

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Table 2. Discriminant analysis (vineyard category) for Côte de Nuits

Percentage of original grouped cases correctly classified: 2002, 69.4 %; 2003, 69.0 %; 2004, 70.1 %; 2006, 73.6 %.

Table 3. Discriminant analysis (grape variety) for Côte de Beaune

Percentage of original grouped cases correctly classified: 2002, 88.4 %; 2003, 83.7 %; 2004, 74.1 %; 2006, 89.5 %.

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ontheamountofilluminatedsoil,whichdependson (2) plantdensity,roworientation,andthehourandepoch ofimageacquisition.Asforplantdensity,the whereh istherowheight,d isthedistancebetween standarddensityinCôted’Orisabout10,000plants rows,z istheSun’szenithaldistanceorelevationata perhectare;suchahighdensityensures,duringthe givenmoment,anda isthelateralilluminationangle vegetativecycle,arelativelysmallsoilvisibility. atthesametime,thatis,a istheanglebetweenthe However,somesoilwouldstillbeseenfromthe orientationofagivenrowandthesolarazimuth. vantagepointofasatellite,andwehavetoconsider Here,theanglea modulatestheshadow’slength itsradiancerelativetoplantleaves,whichcanbe becausewhena = 0º,therowisorientedtowardsthe reducedbyshadowsprojectedbythevines. Sunandtheinter-rowsoilisentirelyilluminated.In equation(1),theproportionP assumesvalues Forthisgeometricalshadoweffect,theproportionP between0and1,thevalue1correspondingtothe oftheinter-rowsurfacethatisshadowedbya extremesituationwherethewholeareabetweenvine continuouswallofvinesis: rowsisintotalshadow.

Table 4. Vegetation index (NDVI) for Côte de Beaune and Côte de Nuits

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InDucatiet al. (2014)weshowedthatashadowed themselvestomaximizesunlightabsorptionanddo soilhaslittlereflectancecomparedtofully- notfollowterrainslope;therefore,leaforientation, illuminatedleaves.Butitisseenthattheamountof andsothepartofpixelreflectancethatisdueto illuminatedsoiliscriticallydependentonrow vegetation,tendstobeindependentofslope. orientation ;ifinLoireweperceivedthatrow orientationsinvineyardsarealmostrandom,inCôte Asafinalremarkonthepossiblerelevanceofslope d’Orwesawduringourfieldtrips,furthersupported andsolarorientationtovineyardqualityinCôte byvisualinspectionofhigh-resolutionsatellite d’Or,wenotethatthesevariableswerealready imagesavailableviaInternetservices,thatthereisa includedinthestudybyWittendal(2004),withno certaindominanceofdown-hillrows.Theexact positiveresults. geographicaldirectionofthesedown-hillrows, 2. Our results however,variesdependingonthelocalslope,which canfavoraneastexposureaswellasnorth-east, AspresentedintheResultssection,category south-eastandothermoreextremeexposure.The discriminationwasfairlygoodoverallfourimages. Sun’spositionatthemomentofimageacquisition Itcouldbeexpectedthatdiscriminationaccuracy (10 h 30AM)waseast,andtheSun’szenithal wouldbepoorerforthewinterdata,sincethe distanceinourimagesneverexceeded65°. reflectedlightcomesfromthesoil,withlittle Therefore,forthevineyardswithdown-hillrows, contribution from vegetation ; however, the thereisacertainamountofsunlightilluminatingthe differenceofwinterdatawithrespecttospringor soilbetweenrowsanditscontributionmaybe summerdatawassmall.Thissuggeststhatspectral considered. differencesbetweencategoriescomeprimarilyfrom However,forpracticalpurposes,wewillnotdiscuss thesoilandwhenthesoiliscoveredbyvineleaves thisissuefurther.Thisisduetothefactthatduring thesedifferencespersistandevenseemtobe ourfieldtripswedidnotdetectanycorrelation reinforcedbywhatthesoilcommunicatestoplant betweenroworientationandvineyardcategory.In leaves.Anotherresultthatmaydeservedeeper fact,weevenobservedthatinthoseGrandCru investigationistheparcelsthatwereplacedina vineyardsthatarenot“monopoles”theorientations differentcategoryindiscriminantanalysis.Overall, ofvinerowsvaryconsideringtheplotsinsidethe theresultsindicatethatabout30 %ofallparcels appellation;thisisobserved,forexample,inthe presentedspectralfeaturestypicalofothercategories. -andÉchezeauxterrainsand,ofcourse, Forexample,someclimats,whichareformally inthetwoothercategoriesofthisstudy,PremierCru classifiedasbeinggenericappellations,carried andCommunale.Therefore,evenifacontributionto spectralfeaturesderivedfromsoil/leafreflectance pixel reflectance from the soil exists, this thatputtheminhighercategories;theoppositealso contributionispronetobecommontoallthree happened.Foreachregionwehadfourimages,and studiedcategoriesandwouldnotbeadifferentiation sowehadfourspectratreatedseparatelyin factorinourstudy.Thisconclusionisnotsurprising, discriminantanalysis.Uncertaintiesinreflectance sinceifroworientationhadbeenrelevanttoquality determinationcouldeventuallychangeaspectrumto discriminationinBurgundy,thecategorieswould thepointofputtingitinanothercategory,butsome havebeenseparatedbytheirrespectiverow systematicchangesdidappear.Thesecaseswereas orientationsalongtimeago,whichisnotthecase. follows:oneclimatoftheCommunaleclasswas consideredtobeGrandCruinallfourimages;seven Anotherconsideration,asstatedatthebeginningof CommunaleclimatswereclassifiedasPremierCru thissection,isterrainslope.Thelessprestigious fourtimes;theopposite,PremierCruclassifiedas vineyardsinBurgundyarelocatedovertheplainson Communale,happenedfourtimes;andtwoPremier theeastsideofRN74,butinourstudywehave CruclimatswereclassifiedasGrandCruinallfour selectedonlyafewplotsinthatzone.Byfar,the images.Visualinspectionofimagesdidnotreveal largerpartofoursamplewascomposedofvineplots anyirregularities,andthusthesecasesdeserve onthehillypart.Heretwopointsmustbeconsidered. furtheranddeeperinvestigation,possiblyincluding Thefirstoneisthatthereisnoclearslope-based moredetailedsoilinformationandfieldinspection. criteriontoseparatethethreecategoriesinour sample;thisissimplynotobserved.Thesecond Between70 %and100 %ofGrandCruvineyards pointisthatthereflectancehasitsorigininvine werecorrectlyidentifiedinBeaune;thesenumbers leaves,andtheamountofreflectancedependsonthe werebetween55.6 %and77.8 %inNuits.The relativeorientationoftheplantleafsurfacewith questionis:inoursatellitereflectancedata,what respecttosunlight.However,plantleavesorient differentiatestheGrandCrucategoryfromthe

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others?Resultsshowedthatasystematicallyhigher asforvineyardcategorydiscrimination,terrainand reflectanceseemstoberealforPinotnoir,andthat soil-terroiraretheultimatedifferentiationfactors. forChardonnaythedifferentiationprocessseemsto bemoresubtle.ItisknownthatASTERsatellite CONCLUSION bandsB4toB8aresensitivetowater,meaningthat Wehaveshownthatsatellitedataisfunctionalto higherwatercontentinatargetwillreduce revealvineyardquality.Thiscanbeseenas reflectance.PinotnoirGrandCrushadhigher surprising.Spectraldifferencesshouldcome reflectance.Whetherthismeanslesswaterinleaves essentiallyfromsoilfeatures,whicharetransmitted andsoilisnottotallyclear.Atkinson(2011) tothevineandtovineleaves.Inthisstudy,Remote suggestedthatGrandCrusoilshaveamorestable Sensing techniques were valuable in the capacity of water storage, due to sub-soil characterizationofterroirs,inthiscasewithrespect characteristics;peculiaritiesintop-soil,theonethatis tovineyardquality.Itcanbenotedthattheultimate observedfromspace,seemtobelessevident.This factordefiningqualityistheresultingwine,andwine questioncanbebetterunderstoodinfuturestudies, qualityresultsnotonlyfromthesoilcomponentsof basedonmoredetailedspectralinformationanda theterroirconceptbutalsofromviticulturaland widerdatabase. winemakingpractices.Thesenon-geochemical ParcelswithChardonnaywerewellseparatedfrom factorscanperhapsexplainpartofthe« wrong » thosewithPinotnoir.Again,thequestioniswhether identificationscommentedintheDiscussion,keeping thefollowingcommentinmind(Thackrey2001):« I thisisduetovegetationorsoil.Wealready believethatthequalityofFrenchwineisduetoa mentionedthatduringthevegetativeseasonthesoil Frenchgeniusforviticultureandwinemaking,(...), iswellcoveredbythecanopyandalsothatthissoil nottothesubsoil ».Thefact thatthedifferences tendstobeinshadowatthemomentofimage betweentheseclimatcategoriesareatleastpartially acquisition.Thisperceptionleadsustobelievethat duetoterroircharacteristicssuggeststhatterroir,or theseparationisduetospectraldifferencesbetween moreprecisely,thesoil,influencesthevineandvine thetwovarieties,expressedbyleafreflectance.In canopyuptothepointthatdetectionofvineyard anotherinvestigation(DaSilvaandDucati,2009)we qualitybyRemoteSensingbecomespossible. demonstratedthatredandwhitegrapescouldbe spectrallyseparated,thecausebeingtheanthocyanin Acknowledgements :ASTERL1Bdatawereobtained pigmentwhichispresentinleafcellsofredgrapes. throughtheonlineDataPoolattheNASALandProcesses However,the2003winterresultprovidesadeeper Distributed Active Archive Center (LP DAAC), insight,sincewehaveonlysoilreflectanceand USGS/EarthResourcesObservationandScience(EROS) separationisstillwelldone.ThisisforCôtede Center,SiouxFalls,SouthDakota(https://lpdaac.usgs. gov/get_data).JRDisgratefulforthehospitalityofthe Beaune,wherethebestterroirsforChardonnayand staffoftheInternationalVintageMasterattheÉcole Pinotwerepinpointedbycenturiesofstudy.The Supérieured’Agriculture(ESA)ofAngers,France,where extremelyvariableBurgundiansoilwasand thisinvestigationwasstartedduringhisstayasVisiting continuestobedeterminantforthesechoices(Fanet, Professorin2011,benefitingfromErasmusMundus 2008;PitiotandServant,2010),andinalimestone- financialsupport. dominatedenvironment,subtledifferencesmade Chardonnaytobefrequentlyplacedwherethereisa REFERENCES certaindominanceofclaywithmarl-limestone.Other AbramsM.,HookS.,RamachandranB.,2002.ASTER factorscanalsoplayarole,liketerrainorientation, User Handbook.Availableat:http://asterweb.jpl. withoftenmoresouthandsouth-westfacingparcels nasa.gov/content/03_data/04_Documents/aster_user_ forChardonnayandatendencyforPinotnoirto guide_v2.pdf(accessedonNovember12,2013). occupythehigherpartsofthehillylandscape.All AltaweelM.,2005.TheuseofASTERsatelliteimageryin thesefactorsmayinducespectraldifferencesinthe archaeologicalcontexts.Archaeological Prospection, infrared.Ontheonehand,asouthernexposurewould 12,3,151-166. makethetoplayersofsoildrier,possiblyincreasing AtkinsonJ.,2011.TerroirandtheCôtedeNuits.J. Wine reflectance.Butontheotherhand,moreclayed, Res.,22,1,35-41. marl-limestoneterrainsretainmorewater,anditis BerkA.,AndersonG.P.,AcharyaP.K.,BernsteinL.S., wellknownthathighersoilhumidityreducesthe MuratovL.,LeeJ.,FoxM.,Adler-GoldenS.M., reflectanceatinfrared(BowersandHanks,1965); ChetwyndJ.H.J.-R.,HokeM.L.,LockwoodR.B., moreover,ifChardonnayismorefrequentlyplacedat GardnerJ.A.,CooleyT.W.,BorelC.C.,LewisP.E., thelowerpartofhills,againhumiditywillbehigher, ShettleE.P.,2006.MODTRAN5:2006Update.In: andbothfactorswouldreducereflectance.Therefore, Proceedings of SPIE,vol.6233,62331F.

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BlauthD.A.,DucatiJ.R.,2010.AWeb-basedsystemfor LacarF.M.,LewisM.M.,GriersonI.T.,2001.Useof vineyardsmanagement,relatinginventorydata, hyperspectralreflectancefordiscriminationbetween vectorsandimages.Computers and Electronics in grapevarieties.In : Proc. IEEE International Agriculture,71,2,182-188. Geoscience and Remote Sensing Symposium BowersS.A.,HanksR.J.,1965.Reflectionofradiant (Sydney,Australia),vol.6,pp.2878-2880. energyfromsoils.Soil Science,100,2,130-138. MatherP.,1999.Computer Processing of Remotely- BramleyR.G.V.,ProffittA.P.B.,1999.Managing Sensed Images: An Introduction.Wiley,Chichester. variabilityinviticulturalproduction.Australian PitiotS.,PouponP.,2009.Atlas des Grands Vignobles de Grapegrower and Winemaker,427,July1999,11- Bourgogne.Lusigny-sur-Ouche,France. 16. PitiotS.,ServantJ.-C.,2010.Les Vins de Bourgogne. CampbellJ.B.,WynneR.H.,2011.Introduction to Remote Beaune,France. Sensing.GuilfordPress,NewYork. ThackreyS.,2001.An Afternoon with Sean Thackrey. CeminG.,DucatiJ.R.,2011.Spectraldiscriminationof Availableat :http://www.gangofpour.com/bree/ grapevarietiesandasearchforterroireffectsusing remotesensing.J. Wine Res.,22,1,57-78. profiles/thackrey/thackrey4.html(accessedon November23,2013). DaSilvaP.,DucatiJ.R.,2009.Spectralfeaturesof vineyardsinsouthBrazilfromASTERimaging.Int. TuckerC.J.,1979.Redandphotographicinfraredlinear J. Remote Sensing,30,23,6085-6098. combinationsformonitoringvegetation.Remote Sensing Environ.,8,2,127-150. DucatiJ.R.,SarateR.E.,FachelJ.M.G.,2014.Application ofremotesensingtechniquestodiscriminatebetween VanLeeuwenC.,SeguinG.,2006.Theconceptofterroir conventionalandorganicvineyardsintheLoire inviticulture.J. Wine Res.,17,1,1-10. Valley,France.J. Int. Sci. Vigne Vin,48,135-144. WittendalF.,2004.GreatBurgundywines:aprincipal FanetJ.,2008.Les Terroirs du Vin.HachettePratique, componentanalysisof“LaCôte”vineyards.In: 11th Paris. Oenometrics Conference (,France),31p. IwasakiA.,TonookaH.,2005.Validationofcrosstalk Zarco-TejadaP.J.,BerjónA.,López-LozanoR., correctionalgorithmforASTER/SWIR.IEEE Miller J.R.,MartínMuñozA.P.,CachorroV., Transactions on Geoscience and Remote Sensing,43, GonzálezM.R.,deFrutosA.,2005.Assessing 12,2747-2751. vineyardconditionwithhyperspectralindices:leaf JensenJ.R.,2007.Remote Sensing of the Environment: An andcanopyreflectancesimulationinarow- Earth Resource Perspective.PrenticeHall,New structureddiscontinuouscanopy.Remote Sensing York. Environ., 99,3,271-287.

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Appendix A. Climats and lieu-dits included in this study

Climat Commune Category Côte Sur Le Bois Nord Cheilly-lès-Maranges Communale Beaune Sur Le Bois Sud Cheilly-lès-Maranges Communale Beaune Les Charmes Dessus Santenay Communale Beaune Les Cornières Santenay Communale Beaune Les Hâtes Santenay Communale Beaune Les Prarons Dessus Santenay Communale Beaune Les Champs Claudes Santenay Communale Beaune Les Benoites Chassagne-Montrachet Communale Beaune Champs de Morjot Chassagne-Montrachet Communale Beaune Les Lombardes Chassagne-Montrachet Communale Beaune Clos Bernot Chassagne-Montrachet Communale Beaune La Caniere Chassagne-Montrachet Communale Beaune Les Masures Chassagne-Montrachet Communale Beaune Le Concis du Champs Chassagne-Montrachet Communale Beaune Les Chambres Chassagne-Montrachet Communale Beaune Les Hautés Auxey-Duresses Communale Beaune La Macabrée Auxey-Duresses Communale Beaune Les Boutonniers Auxey-Duresses Communale Beaune Les Famines Volnay Communale Beaune Les Grands Poisots Volnay Communale Beaune Les Cras (Pommard) Pommard Communale Beaune Village Pommard Communale Beaune Les Prevoles Beaune Communale Beaune Les Peuillets com. Savigny-lès-Beaune Communale Beaune Poirier Malchaussé Chorey-lès-Beaune Communale Beaune Les Pimentiers Savigny-lès-Beaune Communale Beaune Aux Fourches Savigny-lès-Beaune Communale Beaune Aux Champs Chardons Savigny-lès-Beaune Communale Beaune Aux Petits Liards Savigny-lès-Beaune Communale Beaune Les Beaumonts ouest Chorey-lès-Beaune Communale Beaune Tue-Boeuf Chorey-lès-Beaune Communale Beaune Les Bons Ores Chorey-lès-Beaune Communale Beaune Les Champs Longs Chorey-lès-Beaune Communale Beaune Les Cras (Aloxe-Corton) Aloxe-Corton Communale Beaune Les Valoziéres Aloxe-Corton Communale Beaune Sur Herbeux Pernand-Vergelesses Communale Beaune La Mort Ladoix Communale Beaune Les Embazées Chassagne-Montrachet Premier Cru Beaune Les Grands Clos Chassagne-Montrachet Premier Cru Beaune La Chapelle Chassagne-Montrachet Premier Cru Beaune Les Chaumées Chassagne-Montrachet Premier Cru Beaune Les Fairendes Chassagne-Montrachet Premier Cru Beaune En Cailleret Puligny-Montrachet Premier Cru Beaune Les Champs Gain Puligny-Montrachet Premier Cru Beaune Chassagne Chassagne-Montrachet Premier Cru Beaune Clos Saint-Jean Chassagne-Montrachet Premier Cru Beaune Les Chenevottes Chassagne-Montrachet Premier Cru Beaune Le Montrachet Chassagne-Montrachet Grand Cru Beaune Bâtard Montrachet S Puligny-Montrachet Grand Cru Beaune Bâtard Montrachet N Puligny-Montrachet Grand Cru Beaune Montrachet Puligny-Montrachet Grand Cru Beaune Chevaller Montrachet Puligny-Montrachet Grand Cru Beaune Les Tremblots Puligny-Montrachet Communale Beaune Les Houillères Puligny-Montrachet Communale Beaune Le Cailleret Puligny-Montrachet Premier Cru Beaune Les Pucelles Puligny-Montrachet Premier Cru Beaune Clavaillon Puligny-Montrachet Premier Cru Beaune

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Les Perrieres Puligny-Montrachet Premier Cru Beaune Les Combettes Puligny-Montrachet Premier Cru Beaune Les Referts Puligny-Montrachet Premier Cru Beaune Les Charmes-Dessus Mersault Premier Cru Beaune Les Levrons Puligny-Montrachet Communale Beaune Les Reuchaux Puligny-Montrachet Communale Beaune Corvée des Vignes Puligny-Montrachet Communale Beaune Le Limozin Mersault Communale Beaune Les Pelles-Dessous Mersault Communale Beaune Sous La Velle Mersault Communale Beaune Les Grands Charrons Mersault Communale Beaune Les Tillets Mersault Communale Beaune Les Clous Dessus est Mersault Communale Beaune Les Clous Dessus ouest Mersault Communale Beaune Les Vireuils Dessus est Mersault Communale Beaune Les Vireuils Dessus ouest Mersault Communale Beaune Le Cromin Mersault Communale Beaune Les Meix Chavaux Mersault Communale Beaune Les Clos Roussots est Maranges Premier Cru Beaune Les Clos Roussots ouest Maranges Premier Cru Beaune Beauregard Santenay Premier Cru Beaune La Comme Santenay Premier Cru Beaune Les Graviéres Santenay Premier Cru Beaune Les Champs Fulliot Monthélie Premier Cru Beaune Clos des Chenes Volnay Premier Cru Beaune En Champans Volnay Premier Cru Beaune Frémiets Volnay Premier Cru Beaune Les Bertins Pommard Premier Cru Beaune Clos de la Commaraine Pommard Premier Cru Beaune La Refene Pommard Premier Cru Beaune Clos Blanc Pommard Premier Cru Beaune Les Grands Epenots Pommard Premier Cru Beaune Les Petits Epenots 1.SE Pommard Premier Cru Beaune Les Petits Epenots 2.NW Pommard Premier Cru Beaune Les Petits Epenots 3.NE Pommard Premier Cru Beaune Les Epenotes Beaune Premier Cru Beaune Le Clos des Mouches Beaune Premier Cru Beaune Les Aigrots Beaune Premier Cru Beaune Champs Pimont Beaune Premier Cru Beaune Les Avaux Beaune Premier Cru Beaune Les Tuvilains Beaune Premier Cru Beaune Belissand Beaune Premier Cru Beaune Les Teurons Beaune Premier Cru Beaune Les Greves Beaune Premier Cru Beaune Les Cents Vignes Beaune Premier Cru Beaune Clos du Roi Beaune Premier Cru Beaune Les Peuillets 1er Savigny-lès-Beaune Premier Cru Beaune Les Narbantons Savigny-lès-Beaune Premier Cru Beaune Aux Clous Savigny-lès-Beaune Premier Cru Beaune Aux Serpentieres Savigny-lès-Beaune Premier Cru Beaune Aux Vergelesses Savigny-lès-Beaune Premier Cru Beaune Les Basses Vergelesses Pernand-Vergelesses Premier Cru Beaune Le Clos du Roi Aloxe-Corton Grand Cru Beaune Les Bressandes Aloxe-Corton Grand Cru Beaune Les Renardes Aloxe-Corton Grand Cru Beaune Les Pougets Aloxe-Corton Grand Cru Beaune Le Corton Aloxe-Corton Grand Cru Beaune Le Clos de Magny Communale Nuits La Montagne Corgoloin Communale Nuits

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Les Chaillots Corgoloin Communale Nuits Aux Quartiers Corgoloin Communale Nuits Les Monts de Boncourt Corgoloin Communale Nuits Aux Fauques Communale Nuits Belle Vue Comblanchien Communale Nuits Le Vaucrain Comblanchien Communale Nuits Les Vignottes Premeaux Communale Nuits Au Leurey Premeaux Communale Nuits Preau Communale Nuits Vignois Brochon Communale Nuits Les Vallerots Nuits-Saint-Georges Communale Nuits Les Longecourts Nuits-Saint-Georges Communale Nuits Les Chaliots Nuits-Saint-Georges Communale Nuits Les Charmois Nuits-Saint-Georges Communale Nuits La Charmotte Nuits-Saint-Georges Communale Nuits Aux Allots Nuits-Saint-Georges Communale Nuits Aux Saints Jacques Nuits-Saint-Georges Communale Nuits Aux Herbues Nuits-Saint-Georges Communale Nuits Aux Raviolles Vosne-Romanée Communale Nuits Aux Lavières Nuits-Saint-Georges Communale Nuits Au Bas de Combe Nuits-Saint-Georges Communale Nuits Aux Athees Nuits-Saint-Georges Communale Nuits Les Condemennes Chambolle- Communale Nuits Les Babilleres Chambolle-Musigny Communale Nuits Les Athets Chambolle-Musigny Communale Nuits Les Herbues Chambolle-Musigny Communale Nuits Les Porroux Morey-Saint-Denis Communale Nuits Clos Solon Morey-Saint-Denis Communale Nuits Les Crais Morey-Saint-Denis Communale Nuits Les Cognées Morey-Saint-Denis Communale Nuits Les Crais Gillon Morey-Saint-Denis Communale Nuits Les Seuvrées Gevrey-Chambertin Communale Nuits Le Fourneau Gevrey-Chambertin Communale Nuits Pressonnier Gevrey-Chambertin Communale Nuits La Burie Gevrey-Chambertin Communale Nuits Croix des Champs Gevrey-Chambertin Communale Nuits La Platière Gevrey-Chambertin Communale Nuits Creux Brouillard Gevrey-Chambertin Communale Nuits La Justice Gevrey-Chambertin Communale Nuits Billard Brochon Communale Nuits Les Jeunes Rois Brochon Communale Nuits En Auvonne Communale Nuits Es Barres Marsannay-La-Côte Communale Nuits Champforey Marsannay-La-Côte Communale Nuits Clos de la Marechale Premeaux Premier Cru Nuits Clos Arlot Premeaux Premier Cru Nuits Aux Perdrix Premeaux Premier Cru Nuits Aux Corvées Premeaux Premier Cru Nuits Les Forêts Premeaux Premier Cru Nuits Les Saint-Georges Nuits-Saint-Georges Premier Cru Nuits Les Poirets Nuits-Saint-Georges Premier Cru Nuits Les Pruliers Nuits-Saint-Georges Premier Cru Nuits Aux Bousselots Nuits-Saint-Georges Premier Cru Nuits Aux Boudots Nuits-Saint-Georges Premier Cru Nuits Les Terres Blanches Premeaux Premier Cru Nuits Aux Malconsorts Vosne-Romanée Premier Cru Nuits Les Chaumes Vosne-Romanée Premier Cru Nuits Les Suchots ouest Vosne-Romanée Premier Cru Nuits Les Suchots est Vosne-Romanée Premier Cru Nuits

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Les Crâs Vougeot Premier Cru Nuits La Vigne Blanche Vougeot Premier Cru Nuits Les Sentiers Vougeot Premier Cru Nuits Les Milandes Morey-Saint-Denis Premier Cru Nuits La Perrière Premier Cru Nuits Clos du Chapitre Fixin Premier Cru Nuits Les Cazetiers Gevrey-Chambertin Premier Cru Nuits Lavaut Gevrey-Chambertin Premier Cru Nuits La Tâche Vosne-Romanée Grand Cru Nuits La Romanée Vosne-Romanée Grand Cru Nuits La Romanée Conti Vosne-Romanée Grand Cru Nuits Romanée Saint-Vivant nord Vosne-Romanée Grand Cru Nuits Romanée Saint-Vivant sud Vosne-Romanée Grand Cru Nuits Les Treux Flagey-Échezeaux Grand Cru Nuits Les Grands Échezeaux Flagey-Échezeaux Grand Cru Nuits Echezeaux du Dessus Flagey-Échezeaux Grand Cru Nuits Clos de Vougeot ouest Vougeot Grand Cru Nuits Clos de Vougeot est Vougeot Grand Cru Nuits Les Musigny Chambolle-Musigny Grand Cru Nuits Les Bonnes Mares Chambolle-Musigny Grand Cru Nuits Clos de Tart Morey-Saint-Denis Grand Cru Nuits Clos des Lambrays Morey-Saint-Denis Grand Cru Nuits Latricieres Gevrey-Chambertin Grand Cru Nuits Chambertin Gevrey-Chambertin Grand Cru Nuits Mazoyeres ou Charmes Gevrey-Chambertin Grand Cru Nuits Clos de Beze Gevrey-Chambertin Grand Cru Nuits

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