Satellite-based irrigation advisory services: A common tool for different experiences from Europe to Australia

(1)

ContentslistsavailableatScienceDirect

Agricultural

Water

Management

j o ur na l h o me p a g e :w w w . e l s e v i e r . c o m / l o c a te / a g w a t

Satellite-based

irrigation

advisory

services:

A

common

tool

for

different

experiences

from

Europe

to

Australia

Francesco

Vuolo

a

_,

_Guido

_D’Urso

b,c,∗

_,

_Carlo

_De

_Michele

c

_,

_Biagio

_Bianchi

d

_,

_Michael

_Cutting

e

a_University_of_Natural_Resources_and_Life_Sciences_(BOKU),_P._Jordan_Str.82,_Vienna,_Austria b_Department_of_Agriculture,_University_of_Naples_Federico_II,_Via_Università₁₀₀_Portici_(NA),_Italy c_Ariespace_s.r.l.,_Spin-off_company_of_the_University_of_Naples_Federico_II,_Centro_Direz._Is.A/3,_Naples,_Italy d_Dept._Agriculture_and_Environm._Sci.,_University_of_Bari_Aldo_Moro,_Via_Orabona_4,_Bari,_Italy

e_South_Australian_{Murray-Darling}_Basin_Natural_Resources_Management_Board,_Strathalbyn,_South_Australia

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Availableonline28August2014 Keywords:

Remotesensing webGIS

Cropwaterrequirements Irrigationadvisoryservices

a

b

s

t

r

a

c

t

EarthObservationtechniquesarewidelyrecognisedinsupportingthemanagementoflandandwater resourcesandtheyarenowadaysbeingtransferredtooperativeapplications.Inthispaper,wepresentthe currentstatusofasatellite-basedirrigationadvisorysystembasedondedicatedwebGISorfarmersand districtmanagers,inthreedifferentagriculturalsystemsandenvironments:SouthernItaly,Austriaand SouthernAustralia.Mapsofcanopydevelopment(leafareaindex,albedoandsoilcover)arederivedfrom high-resolution(20m)multispectralsatelliteimages,deliveredinnearrealtime(24–36h)andprocessed byusingin-situagro-meteorologicaldata.Theoutputsofthisprocedureare:(i)apersonalisedirrigation advice,basedonthecalculationofcropevapotranspirationunderstandardconditions(accordingto FAO-56definitionandbyusingthedirectapproach)bytakingintoaccounttheactualcanopydevelopmentand cropvariabilityatsub-plotscale;(ii)timelydeliveryoftheinformation,consistinginmapsandsuggested irrigationvolumeapplications,timelypublishedonadedicatedwebGIS-sitewithaccessrestrictedto growersandbasinauthoritiesinordertobettercontroltheirrigationprocessandconsequentlyimprove itsoverallefficiency.Thekey-pointsofthisprocedureare:(a)personalisedirrigationadvice;(b)timely deliveryoftheinformation.Finalusershaveprovidedimportantfeedbackontheusageoftheinformation provided;i.e.farmersareabletorecognisewithoutdifficultiestheirparcelsontheimagesandthey scheduletheirrigationsbytakingintoaccounttheinformationprovided.Thecropheterogeneitycaptured bythehighresolutionimagesisconsideredasavaluableadd-oninformationtoidentifythevariabilityof soiltextureandfertility,plantnutrition,ordifferentperformanceofirrigationsystems.Allthefarmers haveevaluatedpositivelytheusefulnessoftheinformationprovided,andinmostcasesanincreaseof irrigationefficiencywasachieved,becauseofthereductionofwatervolumes.

1. Introduction

EarthObservation (EO) data and geo-spatial tools are more and morefrequentlyused tosupportvarious agricultural prac-tices. The ﬁrst feasibility studies, description of methodologies and prototypes for precise crop management date back tothe early1980s. A review can befound in Moran et al. (1997). In thestudy,theauthorsanalysed differentaspectsofinformation requirementsandidentiﬁedvariousareasofapplication(e.g.soil

∗ Correspondingauthorat:DepartmentofAgriculture,UniversityofNaples Fed-ericoII,ViaUniversità100,Portici(NA),Italy.Tel.:+393472310830..

E-mailaddresses:durso@unina.it,guido.durso@unina.it(G.D’Urso).

mapping, yield estimation, crop evapotranspiration, phenology, etc.) towhich satellite-based remote sensing couldcontribute. Theyalsohighlightedthetechnicallimitationsofsensing instru-mentsandprovidedrecommendationsfortheintegrationofthese technologiesincropmanagementpractices.Inrecentyears, contin-uousadvancementsinspacetechnologiesbroughtnewobserving capabilitiesintermsofspectral,spatialandtemporalresolutions andmostof thecriticalissues describedin Moranetal.(1997) arenowovercome.Recentchangesinpricingpolicyalsoallowed acost-effectiveuseofhighspatialresolutionimagery(10–30-m pixelsize).Forinstance,free-of-chargeaccesstoLandsat8data (byU.S. Geological Survey– USGS – &NASA)is now available withinlessthan 24hofacquisition.Theavailability offree and openaccessdataforscientiﬁcandcommercialuseisexpectedto http://dx.doi.org/10.1016/j.agwat.2014.08.004

(2)

furtherimprovewiththelaunch(scheduledfor2014)ofSentinel 2missions,developedbyEuropeanSpaceAgency(ESA)withinthe Copernicusinitiative(formerGlobalMonitoringforEnvironment andSecurity–GMES–programme).Thisinitiativehasalso stimu-latedthedevelopmentofvariousoperationalservicesandtechnical capacitiesatEuropeanlevel(NEREUS,2012).

Thankstotheseprogresses,theuseofremotelysenseddatahas becomemorecommonandwillbefurtherintegratedin agricul-tureservicestosupportmanagement,monitoringandcontrolling activitiesat different spatial scales includingprecision farming (Lee et al.,2010).Forinstance, mapsofbiophysical parameters ofvegetationareusedinyieldpredictionmodelsat administra-tivelevel(Doraiswamyetal.,2005;Maetal.,2011;Remboldetal., 2013).Otherexamplesareimplementedatcountryorregionallevel toderivecropwaterneedsfromsatelliteestimatesof biophysi-calparametersassimilatedinagro-meteorologicalmodels(D’Urso etal.,2010),tomonitorthenitrogenstatusand toapply fertil-izerwithvariablerates(e.g.,FarmSat)ortoderiveagronomical variables(Casaetal.,2012;Jégoetal.,2012).Ontheotherhand, surfaceenergybalancemethodsbasedonsatelliteobservationsin thethermal region(Bastiaanssenetal.,1998;Allenetal.,2007; KustasandAnderson,2009)havebeendevelopedandappliedin manyareaswiththeaimofdeterminingactualevapotranspiration. Thesemethodshaveshowntheirpotentialityinassessingthewater balanceofirrigatedareasandthecorrespondingwateraccounting practices(Allenetal.,2005;Andersonetal.,2007;Karimietal., 2013)buttheiruseforirrigationadvisoryservicesisconstrained bythelimitedspatialandtemporalresolutionofE.O.-basedthermal observationsandbythecomplexityofalgorithmsfornearreal-time operationalprocedures(OsannJochumetal.,2005).Reviewsof dif-ferentprocedurestodeterminecropevapotranspirationandwater requirementsfromremotesensingcanbefoundinCouraultetal. (2005)andVerstraetenetal.(2008).

Diversely,atemporalresolutionofabout7–10daysbetween satelliteobservationsisusuallyconsideredadequatetomonitorthe variousphasesofthecropdevelopmentthroughoutthegrowing season.Thistemporalresolutioncannowadaysbeassuredbyusing differentplatformsorconstellationofplatformscarryingvisible (VIS)andnearinfrared(NIR)sensingcameras.

Theobjectiveofthisstudyistopresentanadvancedandfully operationalirrigationadvisoryservicebasedontheutilisationof VIS–NIRsatelliteobservationsforcropwatermanagementatﬁeld andirrigationschemelevels;theservicehasbeenimplementedin threedifferentcountries,byusingasimilarwebGISplatformbut differentnames:

-IRRISAT (www.irrisat.it), a regional operational service sup-portedbyruraldevelopmentfundsinSouthernItaly.

-EO4Water(www.eo4water.com),acasestudyofknowledgeand technology transfer in Lower Austria funded by the Austrian SpaceApplicationProgramme.

-IRRIEYEinSouthern-Australia(www.irrieye.com),co-fundedby SouthAustralianMurray-DarlingBasinNaturalResources Man-agementBoard.

Theﬁrstconceptofsatellite-basedirrigationadvisoryservices was designed in the context of the DEMETER UE-RTD project in 2005 (D’Urso and Calera Belmonte, 2005). It was succes-sively improvedand automatised to meet the requirementsof individual farmers with personalised weekly irrigation advices at ﬁeld scale and regional level by using SMS (Vuolo et al., 2005;DeMicheleetal.,2009).Thedevelopmentofadvisory ser-vices based on webGIS platforms has been initially developed within the PLEIADeS UE-project (www.pleiades.es)and further tested inSIRIUS (www.sirius-gmes.es).More recently,a similar application has been developed in California by NASA, within

theTOPS SatelliteIrrigationManagementSupportproject (eco-cast.arc.nasa.gov/simsi/).

Generally,theserviceconceptisbasedontwomain compo-nents:(a)theprocessingoftime-seriesofhighspatialresolution (10–30-mpixelsize)imagesfromsatellite,currentlyavailablefrom publicandcommercialdataproviders,totimelymonitorthecrop growth;(b)theestimationofthecropwaterrequirementbytaking intoaccounttheactualcanopydevelopmentthroughoutthe grow-ingseason;(c)theadaptationandintegrationinlocalmanagement practices&toolsofeasytousegeo-spatialtechnologiestomakethe informationavailabletousersandtosupportthedecision-making process.

Inthis paperwegivea detaileddescriptionof methodologi-calapproachesandprocessingchain,withresultsfromthethree applicationsinEuropeandAustralia.

2. Users’requirementsinsatellite-basedirrigationadvisory services

Irrigationadvisoryservicesareaddressedtothreedifferentuser segments:

-Farmers,smallandlargescaleagri-businesses.

-Watermanagersatirrigationschemeorcatchmentlevel. -Authorities in charge of water management (such as river

basinauthority,government),NationalIrrigationPlanMonitoring Ofﬁce.

Mapsofcropwaterrequirements,irrigatedareas,cropvigour andotherproductsofthesatelliteimageprocessing,canbe aggre-gatedoverdifferenttemporalscales(weekly,monthly,etc.)and land management units (ﬁeld, farm, district, etc.) to meet the requirementsofdifferentusers.

Thetechnologicalimplementationof satellite-basedirrigation advisoryservicesneedstoﬁndacompromisebetweenthe follow-ingelements:

i.availabilityofancillaryinputdata,withnoorminimal contri-butionfromend-users;

ii.elaborationandprocessingtime,withminimumpossibletime lagbetweenE.O.acquisitiondateandinformationdeliveryto ﬁnalusers;

iii.accuracyofalgorithmsforderivingcropwaterrequirements, withminimumpossibleparameterisation.

For example, it is difﬁcult to provide an irrigation advisory servicebasedonadailysoilwaterbalance,whichneedsasinput soilhydrauliccharacteristicsandactualirrigationscheduling.Ifsoil mapsmaybefoundatdifferentlevelofdetailandaccuracy,actual irrigationvolumeanddatesareveryseldomavailable,unless auto-maticmeteringdevicesareinstalledoracontinuousdirectinputis demandedtofarmersforeachindividualplot.

Thesecondrequirementfornear-realtimeprocessingofE.O. datagenerallyconﬂictswiththegenerationofcropmaps,whichcan beelaboratedwithsomeconﬁdenceonlyattheendoftheirrigation season.

Onthebasisoftheseconsiderations,theirrigationadvice pro-vided byIRRISAT/EO4WATER/IRRIEYE described in this workis meanttolimitexcesswaterapplication,inordertoachievea bet-terutilisationofwaterresourcesandcostsavings(irrigationfees, equipmentruntime, energy requiredfor pumpingand fertilizer consumption).Theirrigationadvisoryserviceisdeliveredweekly anditisonlyconcerningthesuggestedamountintheconsidered period,asdeﬁnedlaterinSection3,andnotthescheduling,which islefttofarmer’sdecision.

(3)

Table1

EO-basedirrigationadvisoryservices:productsdeliveredtoﬁnalusers.

Productcharacteristics Listofmapsortextinformation Howproductssupporttheuserneeds Static

(updatedoncepergrowingseason)

•BasicGISlayers(irrigationunitboundaries) (vectorlayer)

•Irrigatedareasandcropinventory(maps and/orvectorlayer,beforetheendofthe season)

1.Supporttovisualisation,analysisanddata interpretation

2.Controlonirrigatedareas

Dynamic

(Updatedwitheachnewsatelliteacquisition orweekly)

•Colourcomposites(maps) •Leafareaindex(LAI)(maps)

•Cropcoefﬁcient(Kc)(mapsandgraphical)

•Cropwaterrequirements(mapsand graphical)

•Dailyagro-meteorologicaldata(textand graphical)

1.Basicagriculturalandwaterplanning. 2.Reductionofover-irrigation. 3.Monitoringofcropvigour

Cumulatedovertimeofdynamicdata(time intervalisﬂexible)

•Totalcropwaterconsumption(e.g.decadeor yearlyforirrigationscheme)

•Statisticsandtime-seriesdata,graphical informationperplotoraggregatedtodistrict levelbasedontheirrigationormanagement unitboundaries(textandgraphical)

1.Monitoringandcontrolofexploitationplans. 2.Calculateseasonalcropspeciﬁccropwater use.

Inourexperience,alsobasedonagriculturalmechanics applica-tions,mostfarmersarenotonlyinterestedinknowingtheirrigation volumesbutalsothespatialandtemporaldistributionofsomecrop characteristics(i.e.cropvigour)overthegrowingseasonforeach oneoftheirﬁelds;theinformationoncanopydevelopment,which istheresultofallagronomicpractices,canalsobeusedforabetter managementofallcropinputs.

Fromthewatermanagersperspective,thisinformationhastobe aggregatedattheirrigationschemelevelinordertoreduce opera-tionandmaintenance(O&M)costs,depreciationofinfrastructures and administration costs. At irrigation district and catchment scale, the knowledge of the spatial distribution of crop water requirementsenablesthewater manager tobetterallocatethe intra-schemewaterdistributionandtomonitorandcontrolthe waterexploitationplans.Asacontroltool,mapsoftheirrigated areas,derivedfromtheanalysisoftime-seriesofEOdata,can sup-portthedetectionofnon-authorisedwaterabstractions.InTable1 thestandardsetofproductsdeployedtosupporttheuserneedsare summarised.

However,thetechnologicaladoptionofspace-basedsolutions for crop/irrigation water management is a complex process. Favourable conditions depend on several technical, social and economicaspects(BaptistaandSousa,2005).Amongseveral fac-tors,theconnectionwiththeusers and theembeddingofnew technologies into standard practices remains the most critical issue.Especiallyintechnologytransferprojects,thelocalcurrent practicesandsystemshavetobeconsideredforadaptingthe imple-mentationoftheinnovationaccordingly.

3. Methodologicalbackgroundfortheestimationofcrop waterrequirementsinsatellite-basedirrigationadvisory services

The standard approach proposed by Food and Agricultural Organization(FAO)(Allenetal.,1998)forcalculatingcropwater requirements(CWR)canbeadaptedtoremotesensingdata.Crop waterrequirements(CWR)arecommonlycalculatedasfollows: CWR=

ETcrop−Pn

(1) whereETcrop(mm)isthecropevapotranspirationunderstandard conditionsi.e.cropsgrownin largeﬁeldsunderexcellent agro-nomicandsoilwaterconditions,(Allenetal.,1998);Pn,iseffective precipitation depending on canopy development described by

meansoftheLeafAreaIndexLAI,andthefractionalvegetationcover fc,accordinglytoBraden(1985): Pn=P−aLAI

1− 1 a+ fcp ALAI

(2)

where Pis the precipitationabove thecanopy, ais an empiri-cal parameterrepresenting thecropsaturationper unit foliage area(≈2.8mmd−1 formostcrops).Thecalculationisdonewith referencetoagiventimeinterval,compatiblewiththetemporal resolutionofmeteorologicaldata.

In most operative scenarios, the reference evapotranspira-tionET0 (mm,onhourlyordailybasis)is determinedbyusing the FAO-56 and ASCE standard equations (Task Committee on StandardizationofReferenceEvapotranspiration,2005).Ifweather dataarenotavailable,ET0canbederivedfromgeostationary satel-litedataaccordingtoDeBruinetal.(2010).Anoperationalproduct isdistributedbySatelliteApplicationFacilityonLandSurface Anal-ysis(LANDSAF:landsaf.meteo.pt/).Successively,ETcropisderived bymultiplyingET0 withthecropcoefficientvalueKc,commonly attributedfromafieldevaluationofthecropdevelopmentand phe-nologicalstagebyusingthetablesproposedbytheoriginalauthors andalsoreportedintheFAO-56procedure.ThecropcoefficientKc isaproxyoftheparametersdescribingthecanopydevelopment, i.e.LAI,surfacealbedoandcropheight.Thesamecanopy param-etersenteringthedirectcalculationofETcroparealsoinfluencing toagreatextentthespectralresponseofacroppedsurface,i.e.the wayitappearsfromaremotesensor inthevisibleandinfrared wavelengths.

Reflectance-basedKc values, incorporatingthe effects ofsoil type, crop, plant growth and crop management (Wiegand and Richardson,1984),canbedeterminedonapixel-by-pixelbasisby usingsatelliteobservationsinthevisibleandnear-infraredspectral domainstocomputevegetationindexesvalues(Nealeetal.,1989; Glennetal.,2011).Thisapproachhasbeenappliedalsotocanopies notcoveringuniformlythesoilsurface,suchasvineyards,to deter-minethevalueofthebasalcropcoefficient(Camposetal.,2010). However,withtheexceptionoflimitedexperimentscarriedoutby usingmicrometeorologicaltechniquesorlysimetermeasurements, theattributionofcropcoefficients(andconsequentlythe correla-tionwithobservedreflectancevalues)islargelybasedonsubjective fieldobservations,withlimitedpossibilitiesofvalidationbasedon measurementsofcanopyparameters.

An alternative approach was developed by D’Urso and Menenti (1995), for the direct calculation of ETcrop, based on

(4)

Penman–Monteith(P–M)equationasimplementedintheFAO-56 procedure(one-stepapproach),hereinindicatedwith“P-MFAO”: ETcrop=86, 400

(Rn−G) +cp(es−ea)Ca +

1+Ca/Cs

(3) where ETcrop is expressed in mmd−1 and: is the latent heat ofvaporisation of water (Jkg−1) istheslope of thesaturated vapourpressure–temperaturecurvees (T)(kPaK−1)Rnisthenet radiationfluxdensity(Wm−2)Gistheheatfluxdensityintothe soil (Wm−2) is the air density (kgm−3)cp is the air specific heat(Jkg−1K−1)(es−ea)isthevapourpressuredeficit(kPa)atthe givenairtemperatureTaCaistheaerodynamicconductanceforheat transport(ms−1)isthethermodynamicpsychrometricconstant (kPaK−1),andCsisthesurfaceconductance(ms−1),dependingon canopytranspirationandsoilevaporation.

Theminimumsetofclimaticdataneededforthecalculationof Eq.(3)aretheairtemperatureTa(◦C),therelativehumidityRH(%), thewindspeedUz(ms−1),andtheﬂuxdensityofincomingshort waveradiationK↓(Wm−2).Theremainingvariablescanbeeither directlymeasuredorestimatedfromTa,RH,UandK↓(Jensenetal.,

1990).Thismodelconsidersthecanopyasa“bigleaf”,withasurface areaexpressedbytheLAI,acropheighthc andahemispherical spectrallyintegratedalbedor,whichisneededtocalculateRn.By usingthecanopyvaluesforahypotheticalgrassreferencecropi.e. hc=0.12m;r=0.23andLAI=2.88,Eq.(3)givesthereferencecrop evapotranspirationET0.

Thetwo conductance terms Ca and Cs are calculated asthe inverseoftheresistancesdeﬁnedbyAllenetal.(1998):

Ca= k 2_U ln

zU−d zom

ln

zT−d zoh

(4) Cs=0.005LAI

∀

LAI≤4 Cs=0.02

∀

LAI>4 (5)

InEq.(4),kisthevonKarman’sconstant(0.41),zUandzTare respectivelythemeasurementheightsforwind-speedand tem-perature,disthezero-planedisplacementheightandthevariables z0m,z0hrepresenttheroughnesslengthsformomentumandheat respectively being estimatedfrom canopy height hc (Brutsaert,

1982).Consequently,Cabecomes essentiallyafunction ofwind speedandcanopyheight.

ThesurfaceconductanceCsdependsonincomingsolar radia-tion,vapourpressuredeﬁcitandsoilwaterdeﬁcit.Underpotential or“standard”conditions,i.e.whensoilwateravailabilityfor tran-spirationisnotlimited,CscanbeapproximatedbyEq.(5);similar expressionscanbefoundinrecentstudiesbyCleughetal.(2007) andYanetal.(2012),whichcanbeconsideredvalidforawiderange ofirrigatedcrops.

Theproposedapproacheliminatestheneedfordeterminingthe valueofa cropcoefﬁcient;nevertheless, due toits wideusein irrigationpractice,itisalsopossibletoderivean“analytical”crop coefﬁcientKcderivedbyEq.(3)appliedtwice,foragivencropand thereferencecrop;hence,theresultingfunctiondependsoncrop parametersandmeteorologicaldata(D’UrsoandMenenti,1995; D’UrsoandCaleraBelmonte,2005):

Kc= ETcrop ET0 =f

r,hc,LAI;Ta,RH,U,K↓

(6) The P–M FAO calculations based on Eqs. (3)–(5) allow for mappingETcropwithinputdataconsistingofground-based mete-orologicaldataandmapsofthecropparametersr,hc,LAI.These mapscanbeobtainedfromtheprocessingofsatelliteimages.A reviewofprocedurestoderivecropparametersfrommultispectral

EOdatacanbefoundinD’Ursoetal.(2010);themethodologies implementedinthecurrentapplicationaredescribedinthenext section.LAI is themostrelevant parameterforestimating crop waterrequirements:itsvalueisnotonlyneededforcalculating ETcrop,butalsofordeterminingthefractionalvegetationcoverfc andtheeffectiveprecipitationPninEq.(2).Inasimilarway,crop heighthccanbederivedasafunctionofLAI,providedthatcroptype mapsareavailable.Inadditiontothis,EObasedLAIandKcmaps alsoprovidefarmerswithspatialdataonthecanopydevelopment anduniformity,asaresultofdifferentagronomicpracticesinthe ﬁeld.ArecentreviewofmethodsforLAIestimationfromoptical satellitedatacanbefoundinVuoloetal.(2013).Semi-empirical methodscanbeusedtoderiveLAIfromsurfacereﬂectanceinthe visibleandnear-infraredrangeswithsatisfactoryaccuracyforthe presentaimwithouta-prioriknowledgeofcroptypes.

Whenthecanopydoesnotcovercompletelythesoilsurface,a correctionneedstobeappliedtoconsidersoilevaporation.More complex EO approaches based on two-source schemes can be applied(Yanetal.,2012;ShuttleworthandWallace,1985)atthe costofaheavierparameterisation.Wehavecomparedtheresults ofShuttleworthandWallace(S–W)modelwiththeresultsofthe P-MFAOmodel,byusingﬁveyearsofdailymeteorologicalvalues acquiredfromastandardweatherstationinSouthernItalyduring summermonths(irrigationseason).

AsshownintheplotsofFig.1,forLAI=0.5andasurface resis-tanceof thesubstrate(rs

s)in theS–Wmodelof 1000sm−1 the resultingvaluesareincloseagreement;however,ifthesoil sur-faceiswet,thesurfaceresistancers

s canbeaslowas500sm−1 henceP–MFAOisabout20–25%lowerthanS–W.Onthebasisof theseconsiderations,inourelaborationsoftheCWR,wehave con-sideredaminimumthresholdvalueofETcrop=0.4ET0forLAI≤0.5 tocorrectforsoilevaporation.

ThevalidationofETcrophasbeencarriedoutbyusing microme-teorologicaltechniquesinwell-wateredplots(D’Ursoetal.,2010), aswellascross-comparisonofdifferentmethodsinirrigatedcrops (Rubioetal.,2005).InFig.2theresultsofthisvalidationfora case-studyinSardiniaarepresentedforacornirrigatedfield(starting fromtheemergencestage)andforanalfalfacrop.Theseplots con-firmthecloseagreementbetweenaveragehourlyvaluesofETcrop estimatedbyusingthedescribedmethodologyandthe correspond-ingfluxmeasurementsmadebyeddy-covariance.

TheIRRISAT/EO4WATER/IRRIEYEadvisoryserviceisbasedon theapproach described above,which issuitable for afast gen-erationofEOproductsinanoperativeprocessingchaininorder todistributethe“personalised”informationtousersin“near real-time”,i.e.between24and48hafterthesatelliteacquisition,thus fulﬁllingtheimplementationrequirementsoutlinedinSection2.

4. Descriptionoftheprocessingchainanddataused

The processing chain in IRRISAT/EO4Water/IRRIEYE advisory serviceismainlycomposedofthreestages:

-Beforetheirrigationseason:selectionofEOdataproviderand pro-grammingrequest.

-Within48hfromeachimageacquisition: -Pre-processingofEOimages.

-EO-basedcropdevelopmentproducts.

-CalculationofCWRandsuggestedirrigationdepth(pixel-scale andplotscale).

-Deliveryofinformationtoﬁnalusers.

TheselectionoftheEOdataisdrivenbythespatial,spectral andtemporalresolutionoftheconsideredsystem.Theminimum requirementsare:30mspatialresolution,availabilityofgreen,red

(5)

Fig.1. ComparisonofevapotranspirationETpcalculatedbymeansofEq.(3)andthetwo-sourcesmodelofShuttleworthandWallace(1985)forLAI=0.5andtwodifferent

valuesforsubstrateresistancers

s(LEFT:highwetnessconditions:500sm−1;RIGHT:mediumwetnessconditions:1000sm−1).Meteorologicalinputreferstoﬁveyearsof

dailydataacquiredfromJunetoSeptember,SelePlain,Campania,Italy.

andnear-infraredbands,7–9daysrepeatcyclewithviewingangle lessthan20◦fromnadir.Inaddition,datadeliveryhastobedone byFTPwithin24–48hfromoverpass(“rush”service).

Within theadvisory service, theSpanish DEIMOS-1 EO sys-tem(www.deimos-imaging.com)hasbeenchosensinceitmeets theserequirements. The weekly temporal resolution is possible thankstotheavailability ofa constellation(DMC-II),composed offourplatformsofsimilarcharacteristics:largeswath(600km); IFOVatground22m;threespectralbands(0.52–0.60;0.63–0.69; 0.77–0.90␮m)with8bitradiometricresolution.Imagesarealready distributedinUTM WGS84geographical referencesystem,with nearest–neighbor pixel re-sampling, and delivered via FTP in near-realtime(24–36h).Theelaborationsperformedafterimage deliveryaresketchedinFig.3.

Thequalitycheckinstep(a)ofFig.3consistsinassessingthe integrityoftheimagedataandthepresenceofscatteredclouds (normallyimageswithcloudscoveringmorethan20%oftheimage arerejected). The geometrical correctionalready performed by thedataprovidermaybeeventuallyverifiedintermsofaccuracy byoverlaying andcomparinga referencemap. Theatmospheric correction,neededtoeliminateorcompensatediffusionand scat-teringbyatmosphere,isperformedbyusingtheradiativetransfer model inversion implemented in themodule ATCOR of ERDAS Imaginecommercialsoftwarewithstandardatmosphericprofiles (GeosystemGmbh,2014).Invarianttargetwithintheimageare usedtofindthemostappropriateatmosphericparameterstobe usedintheelaboration.

ThenumberedsequenceofelaborationsforderivingEO-based cropdevelopmentmaps is shown in thebox (b) of Fig.3.The albedo (r)neededfor derivingthenetradiant fluxin Eq.(3) is anapproximationofthehemisphericalandspectrallyintegrated surfacealbedo;consideringthelimitedspectralresolutionofEO datanormallyavailable,thealbedoiscalculatedasaweightedsum ofsurfacespectral reflectance derivedfromtheatmospheric correction,withbroadbandcoefficientswrepresentingthe cor-respondingfractionof thesolarirradiancein each sensor band (D’UrsoandCaleraBelmonte,2005):

r=

n

=1w (7)

The LAI is derived from surface reﬂectance by applyingthe modelCLAIR(Clevers,1989):

LAI=−1 ˛ ln

1− WDVI WDVI∞

(8) where˛isanempiricalshape parameter,mainlydependingon canopy architecture, which is determined from ﬁeld measure-ments(asshownlater,0.34–0.35forItalyandAustriaand0.22for Australia,withprevailingtreecrops);WDVIistheWeighted Dif-ferenceVegetationIndex,andWDVI_∞istheasymptoticvaluedfor LAI→∞.TheWDVIisgivenby

WDVI=NIR−reds,NIR s,red

(9)

Fig.2.FieldvalidationofevapotranspirationunderstandardconditionsETp,Eq.(3),incornandalfalfairrigatedcropsbymeansofEddyCovariancemeasurements;Sardinia

(6)

Fig.3.Flow-chartoftheprocessingchainafterimagedelivery;symbolsarereportedinthetext. Hence,theanalyticalprocedureforproducingLAImapsfrom

satellite-basedimagesofspectralsurfacereﬂectancecanbe summ-arisedinthefollowingsteps:

-identiﬁcationofthesoil-lineslopeinEq.(9)(ratioofaveragebare soilreﬂectanceinNIRandredbandss,NIR,s,redusuallybetween 0.9and1.3);

-calculationoftheWeightedDifferenceVegetationIndexWDVI bymeansofEq.(9);

-identiﬁcationofWDVI_∞incorrespondenceofpixelswith maxi-mumvegetationcover(usuallybetween0.55and0.75); -calculationofLAIbymeansofEq.(8).

Itshouldbenoticedthattheselectionandutilisationof image-basedparameterssuchasthesoil-lineslope(inEq.(9))andWDVI∞ mayallowfora detection andpartialcompensationof existing errorsintheatmosphericcorrectionprocedure,inthecasethat theyareresultingoutsidetheintervalsgivenabove.

ThefractionalvegetationcoverfcisderivedfromLAIbyusing apolynomialempiricalexpression,whichcoefﬁcientsare deter-minedfromﬁeldmeasurementsandarevalidforawiderangeof crops:

fc=−0.0038LAI4+0.054LAI3−0.30LAI2+0.82LAI

∀

LAI≤5 (10) Cropheighthccanbederivedinasimilarway,butacropspeciﬁc relationshiphastobedeterminedempirically.Whenthis infor-mationorthecropmapsisnotavailable,hcisﬁxedto0.4m.The

assumptionmadeisthattheinfluenceofcropheighthconthevalue ofETcropissmallfordifferentLAIvalues;intheplotsofFig.4,we havecomparedthecalculationofETcropforhc=0.4and1.0mand differentLAIvalues,withthesamemeteorologicaldatasetofFig.1. Theassumptionofafixedvalueforhcisacceptablefordaily inter-vals undertheconsideredmeteorologicalconditions, whichare characterisedbyalargerradiationtermofP-MFAO(firstaddendum inEq.(3))–comparedtotheaerodynamicterm.This approxima-tioneliminatestheneedforcroptypemapswhicharenottimely available,i.e.classificationofEOdataforthispurposecanbedone onlyattheendoftheirrigationseason.

Thecanopyparametersr,hc,LAIcanbeconsideredasconstant foratimeperiodofapprox.Sevendaysfromthedateofthesatellite overpass.Finally,itispossibletocalculateETcropbyusingtheP-M FAOEq.(3),byusingthedailymeteorologicaldataobservedduring theprevious7daysorsincetheprevioussatelliteacquisition;ina similarway,thenetprecipitationPnisderivedfromEq.(2)andthe cropwaterrequirementsCWRfromEq.(1).Thepixel-basedCWR mapistransformedintoavectormapbymeansofdigitisedplot boundaries.Theresultingirrigationadviceforthegenericplotiis thencalculatedfromasimplewaterbalancewhichonagivenday jwritesasfollows:

di,j=di,j−1+ IRR_i,j−1

i −

CWR_i,j−1 (11)

withIRR_i,j−1representingtheirrigationdepthinplotionthe pre-viousday,di,jthesoilwaterdepletion(startingfromagiveninitial valueofday0)anditheon-farmirrigationefﬁciency(where infor-mationontheirrigationmethodsisavailable).Inthesimplestform,

(7)

Fig.4.ComparisonofevapotranspirationETp,calculatedforﬁveyearsofsummerdailymeteodatabyusingEq.(3),Seleplain,Campania,Italy,forhc=0.4and1mandtwo

differentLAIvalues.

i.e.whennoadditionalinformation onsoilproperties andlocal hydrologicalconditionsisknown,themaximumirrigationdepth isgivenby:

IRR_i,j₌_−d_i,j di,j<0 (12)

IRR_i,j=0 di,j≥0

VolumesarethencalculatedbymultiplyingIRRi,j fortheplot extensionai,commonlyderivedfromthewebGIS.

Thelaststepisthedeliveryoftheirrigationadvicetoﬁnalusers. TheauthorsofthisworkhavedevelopedacommonwebGIStool whichhasbeenimplementedinthree differentareas: Southern Italy,LowerAustriaandSouthernAustralia.Thedistributionofthe informationtofarmersandirrigationagenciesisdonethrougha dedicatedwebmappingtool,developedinanopen-sourcesoftware environment.ThestructureofthewebGIShasbeenslightlyadapted toeachareaforconsideringthelocalusersrequirements, accord-inglytothecriteriaoutlinedinTable1.Theserviceimplementedin

Italyiscalled“IRRISAT”(www.irrisat.it),inSouthernAustralia “Irri-Eye”(www.irrieye.com),inAustria,Marchfeldregion,“EO4Water” (www.eo4water.com).

ThededicatedwebGIS-siteisthemainrepositoryforthemaps and irrigationsuggested volumeapplications, which aretimely published,withaccessrestrictedtofarmersandwaterauthorities, inordertobettercontroltheirrigationprocessandconsequently improveitsoverallefﬁciency.Oncetheuserisloggedin,the inter-facewindowisorganisedintwoframes:MAPandDATA.Inthe MAPframe(left,Fig.5)theuserwillﬁnd:

• Toolsforbrowsingandqueryingmaps. • Boundariesoftheirrigatedplots.

IntheDATAframe(rightpanel,Fig.5),instead: • Theplotdetails(crop,irrigationmethod)

• The temporal series of ETcrop, effectiverainfall and irrigation applications.

(8)

Fig.6.Satelliteimageoverlayinfalsecolours.

ThestandardbackgroundisaGoogleorBingsatelliteimagery, buttheusercanoverlaythesatelliteimageacquiredonagiven datebyselecting“Satellite”fromthetoolbarinthemapframe.The DEIMOSsatelliteforagivendatecanbeoverlaidinfalsecolours, withtheintensityofredproportionaltothecropvigour(Fig.6). Inaddition,theusercanvisualisetherationETcrop/ET0, represent-ingthe“analytical”cropcoefﬁcientofEq.(6);inthecorresponding mapshowninFig.7,wherehighestvaluesarepresentedwithblue tonalities,theusercanvisualisethegrowthuniformitywithinthe selectedplot.

Averagevalues of themain variables, i.e.,ETcrop,Pn,IRR, for eachplotarecalculatedbythesystemandtheycanbedisplayedin graphicalformfromtheDATApaneloftheuserinterface(Fig.8).

Theusercanselecttheintegrationtimeintervalforthesevariables accordinglytohisschedulingpractice.

TheinformationintheDATApanelisalsosentviaSMStothe farmer; asummary reportis sentby However,moreand more farmershaveappreciatedthededicatedwebGISinterface,dueto itsintuitiveuseandrichnessofinformation.

AsimilarinterfacehasbeendevelopedfortheIrrigation Con-sortiaandWaterUserassociations.Thetoolallowsforevaluating cropwaterrequirementsaggregatedatdistrictlevel,foramore efﬁ-cientmanagementoftheconveyanceanddistributionnetwork.The webGIStoolsaregoingtobefurtherexpandedtolinktheﬁnancial managementoftheirrigationfeesatfarmleveltothecropwater requirements.

Fig.7. ETp/ET0oranalyticalcropcoefﬁcientmapoverlay(redtones:lowvalues).(Forinterpretationofthereferencestocolourinthisﬁgurelegend,thereaderisreferred

(9)

Fig.8.GraphsofthetemporalevolutionofaveragevaluesofETp,effectiveprecipitationandcropwaterrequirementsforaselectedplot.

5. ExperiencesofIMPLEMENTATIONSinEuropeandin Australia

5.1. Italy,CampaniaRegion:IRRISAT

IRRISATis theIrrigationAdvisoryService basedonnear-real timedistributionofEOproducts,alreadyoperativeintheCampania regionsince2007.Theserviceisimplementedintheframework oftheRuralDevelopmentPlanoftheCampaniaRegion,Measure 124HealthCheck(www.irrisat.it),asafurtherstepforthe imple-mentationofE.U.Directiven.60/2000intheagriculturalsector.In 2013,theservicehascoveredfourdifferentWaterUser Associa-tion(irrigationconsortia),withmorethan660farmersandatotal extensionof5500ha(Table2).

InIRRISAT,asetof12 DEIMOS-1imageshavebeenacquired frommid-JunetoearlySeptember2012and2013,withanaverage temporalresolutionofabout7days.LAImapshavebeenderived byusingEq.(8)with˛=0.35,whichvaluehasbeendeterminedin previousfieldcampaignswithinthesamearea(D’UrsoandCalera Belmonte,2005)bymeansofaportableLAIdigitalmeter(Licor LAI-2000).TheaccuracyofcanopyparametersestimationinIRRISAT ischeckedfromperiodicfieldmeasurementsperformedin coin-cidenceofthesatelliteacquisitions.During2012,geo-referenced measurementsofLAIwiththesameinstrumentwerecollectedin 10plotswithmaizeandalfalfafieldsonaweeklybasisandthen Table2

SummaryofIRRISATserviceinCampaniaregion,Italy,irrigationseason2012.

Irrigationdistrict Farmers Plots Extensionha

DestraSele 353 704 1935.60

Paestum 209 416 1166.48

Sannio-Alifano 29 208 782.57

Volturno 78 465 1618.92

Total 669 1793 5503.57

comparedwiththeestimatesfromsatellite;anexampleof this comparisoninamaizeﬁeldisgiveninFig.9.Wenoticeherethe under-estimationforLAI>3.5fromE.O.data,duetotheknown sat-urationeffectofvegetationindexes.However,theresultingeffect onETcropisnegligeable,duetotheasymptoticbehaviourofEq.(3) forhighLAIvalues.ThevalidationcampaignonLAIhasshownthat theempiricalcalibrationparameter˛inEq.(8)canbeconsidered astemporally“stable”,thuseliminatingtheneedforLAIcalibration everyyear.

A comparison between the irrigationvolumes suggested by IRRISATandactualonesatfarmanddistrictlevelsisoftendifﬁcult duetothescarceavailabilityofﬁelddata.Thisevaluationhasbeen

Fig.9. TemporalevolutionofLAIestimatedfromsatelliteimageanalysisandfrom ﬁeldmeasurementscarriedoutbyusingaLAILicor2000portableanalyser,inacorn irrigatedﬁeld,Campania,Italy.

(10)

Fig.10.CumulativeirrigationdepthsuggestedbyIRRISATandappliedbyafarmer inacornﬁeldof7.7haduring2012.

possibleinselectedfarmsandirrigationdistrictswithintheIRRISAT areaswhereautomaticregistrationsofwaterwithdrawalsfromthe distributionnetworkwereavailable.Anexampleofthe compari-sonatfarmscaleisshowninFig.10,whilsttheplotinFig.11is referredtoadistrictintheSeleriverplain.Onaverage,farmers apply15%moreirrigationthantheIRRISATvolume,whichcanbe consideredasasubstantialagreement.Overirrigationisobserved atthebeginningoftheseasonandincoincidenceofprecipitation events(asinmid-September);thislatterﬁndingispartiallydueto thelagbetweenadvicetimeandirrigationapplication.

Allthefarmersevaluatedpositivelytheusefulnessofthe infor-mationprovided,especiallywhen itwasmadereadilyavailable bymeansofSMSorweeklyreports.Itwasproventhatthe adop-tionofIRRISATserviceproducedanincreaseofirrigationefﬁciency, becauseof the reduction of water volumescompared to tradi-tionalpractice.Accordinglytotheestimationmadebylocalwater managersand authorities,thetraditional appliedirrigation vol-umeswerevaryingbetween4500and6000m3_/ha_for_maize_and between6500and12,000m3_/ha_for_alfalfa;_during_last_years,_since theintroductionofmeteringdevicesattheoutletsoftheirrigation network,theirrigationvolumeshavebeenprogressivelyreduced, buttheyarestillhigherthancropwaterrequirementsgivenby IRRISAT,whicharebetween3500and4500m3_/ha_for_maize_and

Fig.11.Comparisonfortheirrigationdistrict“Boscariello”,DestraSeleConsortium, 1200ha,betweenmeasuredandIRRISATsuggestedvolumes,for10daysperiods duringtheirrigationseason2012.

between5000and10,000m3_/ha_for_alfalfa._This_means_that_the adoption of IRRISAT can introduce important savings in water resources,withpossibleincrementsofproductionandquality.To this respect,we havenoticedthatin smallextensionfarmsthe irrigationefficiencyislowerandfarmersarealsomorereluctant thanotherstofullyadoptIRRISATintheirrigationscheduling.One possiblereasonresidesintherestrictionofeconomicresourcesfor labourforceandirrigationequipment;sincewaterpricingisnotyet subjectedtometeringcontrols,itissimplyeasiertokeepirrigation goingforanexcessiveamountoftime;inadditiontothis, ineffi-cientanddatedequipmentareoftenused.Diversely,whenthefarm extensionisaboveten-twelvehectares,we foundfarmersmore motivatedtodealwithinnovativetechnologiesandreadyto under-standtheimportanceoftestingareductionofirrigationvolumes. AssuchthepossibleimpactofIRRISATtechnologyinthecontextof waterpricingevolutioninsouthernItalymaybeofsomerelevance. AsmentionedinSection2,cropheterogeneitycapturedbythe satelliteimageshasbeenconsideredbyfarmersasavaluable infor-mationtoidentifytheexistingvariability,insomecasesalready knownfrompersonal fieldexperience;otherfeatureswerealso evidenced,i.e.variabilityofsoiltextureandfertility,plantnutrition, differentperformanceofirrigationsystems.

5.2. Austria,Marchfeldregion:EO4Water

EO4Water(Earthobservationtechnologiesforruralwater man-agement) is a research project funded by the Austrian Space Applications Programme (under Die Österreichische Forschungs-förderungsgesellschaft – FFG) to test and demonstrate the transferabilityofsatellitetechnologiesformappingwaterneeds in Marchfeldregion,one ofthemajorcropproduction areasof LowerAustria.Inthisarea,withmorethan60,000haofcropland (∼21,000regularlyirrigated)thereisastrongneedandapotential demandforapplicationsthatsupportefﬁcientwatermanagement process.Theclimateissemi-aridwithannualprecipitationsofless than550mm(about250–300mmfromMaytoSeptember).Crops suchasvegetablesandsugarbeet(whichisoneofthemostwater demandingcropsinthisarea)requirebetween200and600mm ofwaterforkgdrymatter.Forsummercropswithhighbiomass potential this canreach a total water demandfor the growing periodof around800mm,whichclearly cannotbeprovidedby precipitationundercurrentclimaticconditions.

Ontheonehand,farmersinMarchfeldareveryinterestedin improvingtheefﬁciencyofirrigation.Indeed,this mayresultin reducedoperationcosts,andinyieldstabilityasrequiredto accom-plishproductioncontracts.Ontheotherhand,thereisanincreased ecologicalawarenessamongconsumersaboutlocal,high-quality andfreshfood,howitisproducedandwhetherthewaterfootprint inthevariousproductionstepsissustainable.

Theoverallduration oftheprojectis 2years,witha techni-cal campaignin theﬁrst year (2012)and a second operational campaignduringthesecondyear(2013)oftheproject.Thetotal coverage is more than 1700ha, which corresponds to approx. 8–10%ofthetotalsummercroppedarea(∼21,000ha–estimates basedon2010satellitedata).Inyear1wealsoconducted experi-mentalandvalidationactivities.BasedonDEIMOS-1acquisitions, thefollowingstepshavebeenperformed:(1)calibrationand vali-dationofasemi-empiricalmodeltoestimateLAI;(2)estimation ofcropwaterneedsandpreliminarycomparisonwithirrigation volumesatdistrictand atﬁeld scale.In this workwefocus on activity2.

Fieldcampaignswerecarriedoutcontemporaneouslytoeach satelliteacquisitionin2012tocalibrateasite-speciﬁcLAI-WDVI model.Intotal51indirectgroundmeasurementsofLAIwere car-riedtocoverarangeofcroptypesandphenologicalconditions.The

(11)

Fig.12.Scatterplotofsatellite-basedvs.ﬁeldestimatesofLAIforthetwoyears;Marchfeld,Austria.

measurementsprotocolsandthetuningproceduresweredescribed inVuoloetal.(2013).Groundmeasurementswererepeatedinthe year2013(n=71)duringdifferentcampaignsthroughoutthe grow-ingseasonandcomparedtoLandsat-8andDEIMOS-1LAIestimates. Fig.12showsthescatterplotofsatellite-basedvs.fieldestimatesof LAIforthetwoyears.LAIestimateswereobtainedusingan image-specifictuningofthesoil-lineslopeandWDVI_∞andaconstant˛ coefficientvalueof0.34asdescribedinVuoloetal.(2013).

Thesatelliteestimationswereinlinewiththeground measure-mentswithaveryhighcoefﬁcientofdetermination(R2₌_0.85)_and aRootMeanSquareError(RMSE)of0.42.Theimprovements com-paredtotheyear2012(RMSE=0.86)arepartiallyexplainedbythe factthatintheyear2013measurementswerecarriedoutinthe samelocations(eightﬁelds)andthereforetheycaughtalowercrop variabilitycomparedtotheyear2012.

An estimation of maximum crop water needs at irrigation schemelevel (Marchfeld area) during the growing seasonwas achievedfortheyear2010(usingarchivedLandsat-5data)and 2012(usingDEIMOS-1data).Resultswerecomparedwiththe over-allirrigationvolumesdistributedin theareaovertheirrigation season(June–August)duringpastyears.Foracomparisonatthe ﬁeldscale,twosugarbeetparcelshavebeenselectedduringthe irrigationperiod(endofMaytotheendofAugust–year2012);in Fig.13thesatellite-basedKccurvesarecomparedwiththestandard FAOtables;andwecomparedthesevolumeswithsatellite-based estimationsofcropwaterneeds.Inthiscase,satellitedatadidnot covertheentiregrowingperiodandtheﬁrstimagewasacquired onlyonJune17th.Therefore,Kccurvesfortheinitialand develop-mentgrowthstageswereconstructedknowingtheplantingdate (March15th)andthedurationofeachgrowthstage.Itispossibleto noticethatcropdevelopmentvariabilitydeterminesalowerETcrop thantheoneresultingfromstandardKcvalues.

CumulativeCWRshavebeencalculatedbyusingEq.(1)a;the correspondentsuppliedvolumesfortheparcelsarecomparedin Fig.14aandb.Wenoticeanoverallgoodagreementbetweenthe estimatedandtheprovidedirrigationvolumes.Forparcel“a”,the cumulatedirrigationvolumeresultedslightlylowerthanthe max-imumwaterneedthroughoutthegrowingseason.Forparcel“b”, irrigationvolumesalsofollowedthetrendoftheestimatedcrop waterneed.However,inthis lattercase, thetrendfollowedthe higherenvelopofthemaximumwaterneedcurveandexceeded themaximumvolumesneartheendofthegrowingseason.

Wemiss detailed information onthecropmanagement and soilconditions thatwould allowustointerpret thedifferences ingrowthandwaterusebetweenthetwoparcels(samecultivar

andclimaticconditions);thereforewecanonlyspeculateon possi-bilites.Althoughparcel“b”receivedslightlymorewaterthanparcel “a”(Fig.14),thecropdevelopmentdepictedinFig.13showsthat parcel“a”reachedamaximumKcvalueof1.1,whileparcel“b”only reachedavalueof0.9.ThedifferencesinthemaximumKcmight beexplainedbyvaryingsoilconditionsbetweenthetwoparcelsor duetoaninitialcropwaterstress,oracombinationofbothfactors. Sugarbeetismostsensitivetowatershortageduringinitialand earlyplantdevelopmentstages.Parcel“a”receivedafirstirrigation (notconsideredinthegraphbecauseveryearlyintheseason)soon afterplanting(March26th,20mm)andasecondirrigationalready attheendofMay.Incontrast,parcel“b”receviedthefirstirrigation onlyonJune12th.Thetimingofirrigationatthebeginningofthe seasonmightexplainthedifferencesinthemaximumcropgrowth. Despite theseinitialdifferences, irrigation schedulingwas kept verysimilarforthetwoparcels,withanadditionalirrigationnear theendofAugustforparcel“b”.Acommonmanagemetpractice istoinducesoilwaterstresslateintheseasontoincreasesucrose concentrationinthebeetroot(withsmallreductioninrootyield). AccordingtotheFAO-56,“whenthistypeofwaterstressis prac-tised,thevalueforKcendisreducedfrom1.0to0.6”.Aswelook closertoFig.13(a),wecanobserveareductionoftheKcvalue.In contrast,thisreductioncannotbeobservedinFig.13(b).Thisdata suggestthatanincreaseinwatervolumeslaterintheseasonwould nothelptheplanttorecoverfromtheinitialwaterstress.Instead ofusingthestandardwatervolumes,irrigationforparcel“b”could havebeenmodifiedtothesatellite-basedestimatedvolumesto maximisewatereconomyandtoachievealate-season(beforethe harvest)waterstress(whichdoesnotseemtohappeninthiscase). Inbothcases,wenoticethattheuseofstandardFAO-56Kcvalues wouldresultinanover-irrigation.

5.3. SouthernAustralia,MurrayDarling:IrriEye

TheSouthAustralianMurray-DarlingBasinNaturalResources ManagementBoardhaschosenthedistrictofBookpurnongfora case-studystartedin2011andcontinuedthroughouttheirrigation season2012–2013overanareaofabout1100ha(ofwhich470ha citrus,480havines–winegrapes).Anear-realtimetrialbasedon theacquisitionofmultispectralhighresolutionimagesfromthe SpanishsatelliteDEIMOS-1hasbeenplannedbetweenSeptember and March.The requiredmeteorologicaldata havebeen down-loadedfromthespeciﬁcsectionoftheS.A.Murray-DarlingBasin website(www.aws-samdbnrm.sa.gov.au).Anintensivecampaign ofﬁeldmeasurementofLAIinthethreemaintreecropspresent

(12)

Fig.13.Satellite-basedKc(dots)andstandardKc(greenline)valuesfortwosugarbeetparcels(200daygrowingperiod).SugarbeetwasplantedonMarch15th.First

irrigationoccurredonMay28th(day73)andJune12th(day87),irrigationseason2012,forparcel“a”and“b”respectively.(Forinterpretationofthereferencestocolourin thisﬁgurelegend,thereaderisreferredtothewebversionofthisarticle.)

Fig.14.Satellite-basedestimationsofcropwaterneedsvs.suppliedirrigationvolumesforparcel“a”(left)and“b”(right).Theblueareaindicatestherangeofvaluesofcrop waterrequirementsfortotalandeffectiveprecipitation.(Forinterpretationofthereferencestocolourinthisﬁgurelegend,thereaderisreferredtothewebversionofthis article.)

intheareahasallowedforasitespeciﬁccalibrationoftheCLAIR model(Fig.15);theresultingvalueofthe˛ofEq.(8)hasresulted equalto0.22;thelowervaluecomparedtothecorrespondingin ItalyandAustria,whereherbaceouscropsaredominant,isdueto thecanopystructureoftreecrops.

ThegraphonFig.16indicatesthevariationofcropcoefﬁcient determinedfromsatelliteimagesforthecitrusﬁeldsanalysedand thecomparisonwiththestandardvaluescurrentlyadoptedinthe irrigationpracticeinthearea,basedonFAOtables(calculatedfrom fractionalvegetationcover;Eq.(98)inAllenetal.,1998).Wenotice thatforcitrustheaveragesatellitevaluesareconsistentwithFAO, butwealsonoticealargevariabilityinthesatellite-basedvalues.

Fig.15.Comparisonofsatellite-basedestimatesofLAIvs.ﬁeldmeasurementsfor theprevailingtreecropsintheBookpournongarea,SouthernAustralia,IRRIEYE campaign2013.

Thiscanbeexplainedbydifferencesinthetreemanagement,as wellasinthepresenceofunderstory.However,whenweconsider thecumulativeirrigationvolumesatfarmscale,wenoticea sub-stantialconcordancebetweensatellite-basedirrigationvolumes andactualapplications(2.8vs.3.1measuredMl/haforcitrus;2.8 vs.2.7measuredforvineyards;BillRuedigerFarm,Bookpournong). Itshouldbeoutlinedthatthiscomparisondoesnotincludewater availability derivedfromprecipitationswhichhavebeenhigher thanaverageduringtheobservationperiod.Theseﬁndings con-ﬁrmtheapplicabilityoftheprocedurefortheestimationofCWR describedinSection2fortreecrops;however,furthervalidationis neededforacompleteaccuracyassessmentinthiscase.

ThecurrentirrigationschedulinginBookpurnongisbasedon robustmeteorologicalinformation,butitconsidersstandard val-uesofcropcoefficientsfromFAOtablesandalthoughthisapproach mightbesatisfactoryatbasinlevel,itdoesnotconsiderthelarge variabilityofcropdevelopmentatthefarmlevel,duetodifferent soilconditionsandagronomicpractices.Thesatellite-basedcrop coefficients,whichtakeintoaccounttheseeffectsinanimplicit way,allowforasite-specificevaluationofirrigationvolumesand offersignificantpotentialtoachievesubstantialimprovementsin waterandenergyefficiencyatbasinandfarmlevelsandonthefinal qualityofproduction.Theintuitiveweb-interfacegivesthe grow-erstheabilitytomonitorthecanopydevelopmentatplotlevelin near-realtimeandadditionally,theevaluationofnetprecipitation, thankstoabettercalculationofcanopyinterceptionbasedfrom satelliteLAIvaluesmayreducetheirrigationapplication.

(13)

Fig.16.Comparisonofcitruscropcoefﬁcients(averageand±st.dev.)estimatedfromIRRIEYEandfromFAOtable,currentlyusedintheBookpournongarea,SouthAustralia.

FromtheexperiencescarriedinEuropeandinAustralia,weare abletoevaluatetheoverallcostofsatellite-basedirrigationservices likeIRRISAT/EO4Water/IrriEye.Thecosttotheﬁnalusersisstrongly dependentontheextensionofirrigatedareawithineachsatellite imageandthenumberofacquisitions,i.e.above3000hatheﬁnal costisestimatedaround8–10D/hawith10–11acquisitions dur-ingtheentireirrigationseason.Thefurtherexpectedreductionsof marketpricesofhighresolutionsatelliteimageswillcertainlymake theIRRISAT/EO4Water/IrriEyeservicesevenmorecost-effectivein thenearfuture.

6. Concludingremarks

Thethreeapplicationsofthesatellite-basedwebGISirrigation advisoryservice,in spiteoftheenvironmentalandclimatic dif-ferences,evidence strong similarities in theresponse fromthe user’sside.Thesatellite-basedcropwaterrequirementprocedure adoptedintheirrigationadvisoryservicesIRRISAT/EO4Waterand IRRIEYimplicitlytakesintoaccountthepresenceofvariabilityof cropdevelopmentatplotlevel,duetodifferentsoilconditionsand agronomicpractices.Consequently,satellite-basedirrigation vol-umesgiveasite-specificevaluationofirrigationvolumes,which canbesubstantiallydifferentfromestimationsbasedonstandard Kc tables. These differences are of course smoothed out when consideringaggregated spatial ortemporal scales;however, an irrigationschedulingbasedontheeffectivecropdevelopmentmay bringsubstantialadvantageseitherontherationaleuseofwater andenergyatbasinandfarmlevelsoronthefinalqualityof produc-tion.Comparedtocrop-coefficientapproach,thismethodallowsfor anaccuracyassessmentbasedonobjectivemeasurementsofLAI.

TheinformationonETcrop,evaluatedonthebasisoftheeffective cropdevelopment,providesanupperlimittotheamountofwater tobeappliedbyfarmers;inmanycontexts,byconvincingfarmers inlimitingtheirapplicationswithinETcropitispossibletoachieve consistentsavingsofwaterresources.

Theintuitiveweb-interfacegivesthegrowersthepossibilityto monitorthecanopydevelopmentatplotlevelinnear-realtime; inaddition,anevaluationofnetprecipitation,thankstoabetter calculationof canopy interception basedfrom satelliteLAI val-ues,mayreducetheirrigationapplication.Theknowledgeofcrop developmentvariabilitycanbeeasilyintegratedintomoderncrop management practices, i.e. high-level accuracy GPS-compatible controllersused onautomaticagriculturalmachineries, sprayer auto-shut-offsystems,spreaderordrillauto-sectioncontrols.

However,feedbackfromﬁnalusers(bothfarmersandirrigation watermanagers)isessentialtoimprovetheeffectivenessofthe

serviceintermsofwateruseoptimisation,especiallyduringthe earlystageofserviceimplementation.

Acknowledgements

ThisworkhasbeensupportedbytheE.U.-RuralDevelopment PlanofCampaniaRegion2007–2013,Measure124“HealthCheck”, undergrantD.R.D.n.44del14/06/2010“IRRISAT”;theauthorsalso acknowledgethesupportfromtheAustrianSpaceApplications Pro-gramme(DieÖsterreichischeForschungsförderungsgesellschaft– FFG)forEO4WaterandfromtheSouthAustralianMurray-Darling BasinNaturalResourcesManagementBoardforIrriEye.

References

Allen,R.G.,Pereira,L.S.,Raes,D.,SmithM.,1998.Cropevapotranspiration– guide-linesforcomputingcropwaterrequirements.FAOIrrigationandDrainagePaper 56.

Allen,R.G.,Tasumi,M.,Morse,A.,Trezza,R.,2005.ALandsat-basedenergy bal-anceandevapotranspirationmodelinWesternUSwaterrightsregulationand planning.Irrig.Drain.Syst.19,251–268.

Allen,R.G.,Tasumi,M.,Trezza,R.,2007.Satellite-basedenergybalanceformapping evapotranspirationwithinternalizedcalibration(METRIC)-model.J.Irrig.Drain. Eng.133,380–394.

Anderson,M.,Norman,J.M.,Mecikalski,J.R.,Otkin,J.A.,Kustas,W.P.,2007.A clima-tologicalstudyofevapotranspirationandmoisturestressacrossthecontinental UnitedStatesbasedonthermalremotesensing:1.Modelformulation.J. Geo-phys.Res.112,D10117.

Baptista,A.,Sousa,V.,2005.TransferabilityofDEMETER.Acasestudyoftheirrigation schemeofVeigaDeChaves.AIPConferenceProceedingsonEarthObservation forVegetationMonitoringandWaterManagement,852,84–92.

Bastiaanssen,W.G.M.,Menenti,M.,Feddes,R.A.,Holtslag,A.A.M.,1998.Aremote sensingsurfaceenergybalancealgorithmforland(SEBAL).1.Formulation.J. Hydrol.212–213,198–212.

Braden, H., 1985. Ein energiehaushalts-und verdunstungsmodell for wasser undstoffhaushaltsuntersuchungenlandwirtschaftlichgenutzereinzugsgebiete. Mitt.Dtsch.Bodenkd.Ges.42,294–299.

Campos,I.,Neale, C.M.U.,Calera,A.,Balbontín,C.,González-Piqueras,J.,2010.

Assessing satellite-basedbasal crop coefﬁcients for irrigatedgrapes (Vitis viniferaL.).Agric.WaterManag.98(1),45–54.

Brutsaert,W.H.,1982.Evaporationintotheatmosphere.D.ReidelPublishingCo.

Casa,R.,Varella,H.,Buis,S.,Guérif,M.,DeSolan,B.,Baret,F.,2012.Forcingawheat cropmodelwithLAIdatatoaccessagronomicvariables:evaluationoftheimpact ofmodelandLAIuncertaintiesandcomparisonwithanempiricalapproach.Eur. J.Agron.37(1),1–10.

Cleugh,H.A.,Leuning,R.,Mu,Q.,Running,S.W.,2007.Regionalevaporationestimates fromﬂuxtowerandMODISsatellitedata(2007).RemoteSens.Environ.106, 285–304.

Clevers,J.G.P.W.,1989.Theapplicationofaweightedinfrared-redvegetationindex forestimatingleafareaindexbycorrectingforsoilmoisture.RemoteSens. Environ.29,25–37.

Courault,D.,Seguin,B.,Olioso,A.,2005.Reviewonestimationofevapotranspiration fromremotesensingdata:fromempiricaltonumericalmodelingapproaches. Irrig.Drain.Syst.19(3–4),223–249.

D’Urso,G.,Menenti,M.,1995.Mappingcropcoefﬁcientsinirrigatedareasfrom Land-satTMimages.ProceedingsoftheEuropeanSymposiumonSatelliteRemote

(14)

SensingII,Europto,Paris,sett.’95;SPIE,InternationalSocietyofOptical Engi-neering,2585,41–47.

D’Urso,G.,CaleraBelmonte,A.,2005.Operativeapproachestodeterminecropwater requirementsfromEarthObservationdata:methodologiesandapplications.AIP ConferenceProceedingsonEarthObservationforVegetationMonitoringand WaterManagement,852,14–25.

D’Urso,G.,Richter,K.,Calera,A.,Osann,M.A.,Escadafal,R.,Garatuza-Pajan,J.,Vuolo, F.,2010.Earthobservationproductsforoperationalirrigationmanagementin thecontextofthePLEIADeSproject.Agric.WaterManag.98(2),271–282.

DeBruin,H.A.R.,Trigo,I.F.,Jitan,M.A.,TemesgenEnku,N.,vanderTol,C.,Gieske, A.S.M.,2010.Referencecropevapotranspirationderivedfromgeo-stationary satelliteimagery:acasestudyfortheFogeraﬂoodplain,NW-Ethiopiaandthe JordanValley,Jordan.Hydrol.EarthSyst.Sci.14(11),2219–2228.

DeMichele,C.,Vuolo,F.,D’Urso,G.,Marotta,L.,Richter,K.,2009.TheIrrigation AdvisoryProgramofCampaniaRegion:fromresearchtooperationalsupport fortheWaterDirectiveinAgriculture.In:Proceedingsofthe33rdInternational SymposiumonRemoteSensingofEnvironment,Stresa,Italy.

Doraiswamy, P.C., Sinclair, T.R., Hollinger, S., Akhmedov, B., Stern, A., Prueger, J., 2005. Application of MODIS derived parameters for regional crop yield assessment. Remote Sens. Environ. 97 (2), 192–202,

http://dx.doi.org/10.1016/j.rse.2005.03.015.

Geosystem Gmbh, 2014. ATCOR for Imagine 2014, User Manual. Avail-ablefrom: http://www.geosystems.de/atcor/downloads/ATCOR-for-IMAGINE-2014Manual.pdf .

Glenn,E.P.,Neale,C.M.U.,Hunsaker,D.J.,Nagler,P.L.,2011.Vegetationindex-based cropcoefﬁcientstoestimateevapotranspirationbyremotesensingin agricul-turalandnaturalecosystems.Hydrol.Process.25,4050–4062.

Jégo,G.,Pattey,E.,Liu,J.,2012.UsingLeafAreaIndex,retrievedfromopticalimagery, intheSTICScropmodelforpredictingyieldandbiomassofﬁeldcrops.Field CropsRes.131,63–74.

Jensen,M.E.,Burman,R.D.,Allen,R.G.,1990.Evapotranspirationandirrigationwater requirements.ASCEManual(no.701).

Karimi,P.,Bastiaanssen,W.G.M.,Molden,D.,Cheema,M.J.M.,2013.Basin-wide wateraccountingusingremotesensingdata:thecaseoftransboundaryIndus Basin.Hydrol.EarthSyst.Sci.17,2473–2486.

Kustas,W.P.,Anderson,M.,2009.Advancesinthermalinfraredremotesensingfor landsurfacemodelling.Agric.ForestMeteorol.149,2071–2081.

Lee,W.S.,Alchanatis,V.,Yang,C.,Hirafuji,M.,Moshou,D.,Li,C.,2010.Sensing tech-nologiesforprecisionspecialtycropproduction.Comput.Electron.Agric.74(1), 2–33.

Ma,G.,Huang,J.,Wu,W.,Fan,J.,Zou,J.,Wu,S.,2011.AssimilationofMODIS-LAIinto theWOFOSTmodelforforecastingregionalwinterwheatyield.Math.Comput. Model.58,634–643.

Moran,M.S.,Inoue,Y.,Barnes,E.M.,1997.Opportunitiesandlimitationsfor image-basedremotesensinginprecisioncropmanagement.RemoteSens.Environ.61, 319–346.

Neale, C.M.U., Bausch, W.C., Heermann, D.F., 1989. Development of reﬂectance-based crop coefﬁcients for corn. Trans. ASAE 32 (6), 1891–1899.

NEREUS,2012.TheGrowingUseofGMESAcrossEurope’sregions.Retrievedfrom http://esamultimedia.esa.int/multimedia/publications/NEREUS/ .

OsannJochum,M.A.,Calera,A.,2005.Operationalspace-assistedirrigationadvisory services:overviewofandlessonslearnedfromtheprojectDEMETER.AIP Con-ferenceProceedingsonEarthObservationforVegetationMonitoringandWater Management,DEMETERpartners,852,3–13.

Rembold,F.,Atzberger,C.,Rojas,O.,Savin,I.,2013.Usinglowresolutionsatellite imageryforyieldpredictionandyieldanomalydetection.RemoteSens.5(4), 1704–1733.

Rubio,E.,Colin,J.,D’Urso,G.,Trezza,R.,Allen,R.,Calera,A.,González,J.,Jochum, A.,Menenti,M.,Tasumi,M.,Kelly,C.,Vuolo,F.,2005.Goldendaycomparison ofmethodstoretrieveET(Kc-NDVI,Kc-analytical,MSSEBS.METRIC).AIP Con-ferenceProceedingsonEarthObservationforVegetationMonitoringandWater Management852,193–200.

Shuttleworth,W.J.,Wallace,J.S.,1985.Evaporationfromsparsecrops—anenergy combinationtheory.Q.J.R.Meteorol.Soc.111,839–855.

TaskCommitteeonStandardizationofReferenceEvapotranspiration,2005.ASCE StandardizedReferenceEvapotranspirationEquation.ASCE-EWRITask Commit-teeReport.

Verstraeten,W.W.,Veroustraete,F.,Feyen,J.,2008.Assessmentof evapotranspira-tionandsoilmoisturecontentacrossdifferentscalesofobservation.Sensors8 (1),70–117.

Vuolo,F.,DeMichele,C.,Lazzaro,U.,2005.Successstories:analysisandevaluation oftheItaliancase-studyofDEMETERintheDestraSelePlain.AIPConference ProceedingsonEarthObservationforVegetationMonitoringandWater Man-agement852,33–42.

Vuolo,F.,Neugebauer,N.,FalangaBolognesi,S.,Atzberger,C.,D‘Urso,G.,2013. Esti-mationofleafareaindexusingDEIMOS-1data:applicationandtransferability ofasemi-empiricalrelationshipbetweentwoagriculturalareas.RemoteSens. 5(3),1274–1291.

Wiegand,C.L.,Richardson,A.J.,1984.Leafarea,lightinterception,andyieldestimates fromspectralcomponentsanalysis.Agron.J.76,543–548.

Yan,H.,Wang,S.Q.,Billesbach,D.,Oechel,W.,Zhang,J.H.,Meyers,T.,Martin,T.A., Matamala,R.,Baldocchi,D.,Bohrer,G.,Dragoni,D.,Scott,R.,2012.Global esti-mationofevapotranspirationusingaleafareaindex-basedsurfaceenergyand waterbalancemodel.RemoteSens.Environ.124,581–595.