Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

21 Pages·2017·2.48 MB·English

remote sensing
Article
Evapotranspiration Estimate over an Almond Orchard
Using Landsat Satellite Observations
RuyanHe1,2,YufangJin2,*,MaziarM.Kandelous3,DanieleZaccaria2,BlakeL.Sanden4,
RichardL.Snyder2,JinbaoJiang1andJanW.Hopmans2
1 CollegeofGeoscienceandSurveyingEngineering,ChinaUniversityofMining&Technology(Beijing),
Beijing100086,China;[email protected](R.H.);[email protected](J.J.)
2 DepartmentofLand,AirandWaterResources,UniversityofCalifornia,Davis,CA95616,USA;
[email protected](D.Z.);[email protected](R.L.S.);[email protected](J.W.H.)
3 DepartmentofCropandSoilScience,OregonStateUniversity,Corvallis,OR97331,USA;
[email protected]
4 UniversityofCaliforniaCooperativeExtension,Bakersfield,CA93307,USA;[email protected]
* Correspondence:[email protected]
AcademicEditors:GeorgeP.PetropoulosandPrasadS.Thenkabail
Received:7March2017;Accepted:1May2017;Published:5May2017
Abstract: California growers face challenges with water shortages and there is a strong need to
usetheleastamountofwaterwhileoptimizingyield. Timelyinformationonevapotranspiration
(ET), a dominant component of crop consumptive water use, is critical for growers to tailor
irrigationmanagementbasedonin-fieldspatialvariabilityandin-seasonvariations.Weevaluatedthe
performanceofaremotesensing-basedapproach,MappingEvapotranspirationathighResolution
withInternalizedCalibration(METRIC),inmappingEToveranalmondorchardinCalifornia,driven
byLandsatsatelliteobservations. ReferenceETfromanetworkofweatherstationsoverwell-watered
grass (ET ) was used for the internal calibration and for deriving ET at daily and extended time
o
period,insteadofalfalfabasedreferenceevapotranspiration(ET ). OurstudyshowedthatMETRIC
r
dailyETestimatesduringLandsatoverpassdatesagreedwellwiththefieldmeasurements. During
2009–2012, a root mean square error (RMSE) of 0.53 mm/day and a coefficient of determination
(R2) of 0.87 were found between METRIC versus observed daily ET. Monthly ET estimates had
a higher accuracy, with a RMSE of 12.08 mm/month, a R2 of 0.90, and a relatively small relative
meandifference(RMD)of9.68%during2009–2012growingseasons. NetradiationandNormalized
DifferenceVegetationIndex(NDVI)fromremotesensingobservationswerehighlycorrelatedwith
spatialandtemporalETestimates. AnempiricalmodelwasdevelopedtoestimatedailyETusing
NDVI,netradiation(R ),andvaporpressuredeficit(VPD).Thevalidationshowedthattheaccuracy
n
ofthiseasy-to-useempiricalmethodwasslightlylowerthanthatofMETRICbutstillreasonable,with
aRMSEof0.71mm/daywhencomparedtogroundmeasurements. TheremotesensingbasedET
estimatewillsupportavarietyofStateandlocalinterestsinwateruseandirrigationmanagement,for
bothplanningandregulatory/compliancepurposes,anditprovidesthefarmersobservation-based
guidanceforsite-specificandtime-sensitiveirrigationmanagement.
Keywords: evapotranspiration;landsat5TMand7ETM+;METRIC;almondorchard;consumptive
wateruse;centralvalley
1. Introduction
Almondhasbecomethefirstleadingrevenue-generatingcropcommodityinCalifornia,with
atotalvalueofannualproductionover5.9billiondollarsin2014. California’salmondacreagehas
beenrapidlygrowing,from700,000acres(bearingacreage: 590,000acres)in2005to1,020,000acres
RemoteSens.2017,9,436;doi:10.3390/rs9050436 www.mdpi.com/journal/remotesensing
RemoteSens.2017,9,436 2of21
(bearing acreage: 870,000 acres) in 2014, according to the California Department of Food and
Agriculture (CDFA) [1]. On the other hand, almond growers face water supply challenges due
totheincreasingfrequencyandseverityofrecurringdroughts. Waterusebyalmondorchardshasnot
beenwellstudied,especiallyundervariouscroppingconditions,andthisknowledgegaplimitsthe
capacityforgrowerstoachieveresource-efficientwatermanagement.
Agriculturalconsumptivewateruseconsistsofwaterremovedbyevapotranspiration(ET),viasoil
evaporation(E)andplanttranspiration(T).AccurateestimationandmappingofcropETacrossspace
andtimeareessentialforimprovingfield-scaleirrigationmanagementandforwatershedandregional
waterplanningandmanagement. Ground-basedfluxmeasurementmethodssuchaseddycovariance
(EC)[2],Bowenratio(BR)[3],andmostrecentlysurfacerenewal(SR)[4],canprovidehalf-hourly,
hourly,anddailyETmeasurementsandthusofferguidancetogrowersfortimesensitiveirrigation
scheduling. However, these measurements are limited by the small scale of the footprint area of
themeasurementstationsandtheycannotcaptureheterogeneitywithineachagriculturefield,and
sometimescannotrepresenttheentirefieldororchardconditions. Theequipmentformeasuringfield
ETthroughgroundmethodsisusuallyexpensive,whiletheirinstallationandmaintenancecouldalso
belabor-intensive.
In agricultural production areas, ET has been traditionally estimated as a product of the
weather-based ET of a hypothetical reference surface, such as alfalfa (ET ) or grass (ET ) under
r o
well-wateredcondition,andcropcoefficient(K )[5,6]. K isoftengeneralizedforeachcroptypeand
c c
overafewgrowthstagesfromlimitedfieldmeasurements,andthusdoesnotcapturethedetailed
temporaldynamicsofcropgrowthandspatialvariabilityofcropswithinandacrossthefieldblocks.
Forexample,valuesofalmondK of0.40atinitialstage,0.90atmid-seasonand0.65attheendofthe
c
seasonareusuallyconsideredforirrigationmanagementinallalmondorchards[5]. Nearrealtime
valuesofET arereadilyaccessibletogrowersthroughouttheStateofCaliforniafromtheCalifornia
o
IrrigationManagementInformationSystem(CIMIS)networkofmorethan150weatherstations,and
theyareusedextensivelyforirrigationmanagementinCalifornia. However,spatially-explicitseasonal
K valuesforestimatingwaterusebyalmondorchardsarenotyetavailableintheliterature. Updating
c
thealmondwateruseinformationisparticularlyimportantforCalifornia’salmondgrowerstoimprove
theiron-farmirrigationmanagement,sincealmondproductionindustryexperiencedmajorchangesin
cultivars,rootstocks,plantingdensity,canopymanagementpractices,andirrigationtechnologyand
methodsduringthepast15to20years.
NewapproachestotakeadvantageofremotesensingobservationstomapEThavebeendeveloped,
especially in recent decades, due to increasingly available free satellite observations from multiple
sensors[7–10].EmpiricalalgorithmswerebuilttorelateETtoremotelysensedvegetationindicesand
weatherdata[11–14].Thesealgorithmstypicallyperformwellinareaswheretheyarecalibrated,butthe
uncertaintyusuallyincreaseswhenappliedtootherareas. Thesemi-empiricalPriestley–Taylorequation,
calibratedwithETmeasurements,hasalsoshownpromisingresultsforestimatingETatcontinental
toglobalscale[7,8].Asuiteofalgorithmsthatwerebasedonsurfaceresidualoftheenergybalance
(REB)data,usingbothopticalandthermalremotesensingobservations,hasalsoprovidedestimatesof
ET.One-sourceREBmodelsincludeSurfaceEnergyBalanceAlgorithmforLand(SEBAL)[15],Surface
EnergyBalanceSystem(SEBS)[16],MappingEvapotranspirationathighResolutionwithInternalized
Calibration(METRIC)[17],andSimplifiedSurfaceEnergyBalance(SSEB)[18,19].Two-sourcemodels,
suchastheTwo-SourceModel(TSM)[20,21]andtheDisaggregatedAtmosphere-LandExchangeInverse
(DisALEXI)[22–25],integratethebiophysicalprocessesandremotesensingdatatoestimatetheplant
transpirationandevaporationfromnon-vegetatedsurfacesseparately.
METRIC estimates the near surface temperature gradient (dT) as an indexed function of
radiometric surface temperature, and thus does not rely on accurate estimate of land surface
temperaturefromthermalimagery[17,26,27].ItalsouseshourlyreferenceETtoauto-calibratethe
sensibleheatcalculationforeachsatelliteimage. ThisinternalcalibrationmakesETestimatesmore
robust. METRIC has been widely used to estimate agricultural water use in Idaho, California,
RemoteSens.2017,9,436 3of21
Remote Sens. 2017, 9, 436 3 of 21
and New Mexico for management of water rights, irrigation scheduling, and water resource
[27,28]. Regional scale ET mapping has also been done in the Texas High plains [29], in the
planning [28,29]. Regional scale ET mapping has also been done in the Texas High plains [30], in
mountainous areas of northern Portugal [30], and in the oasis area in Heibe River Basin in China [31],
the mountainous areas of northern Portugal [31], and in the oasis area in Heibe River Basin in
and the results showed reasonable accuracy. The METRIC method was used to provide good
China[32],andtheresultsshowedreasonableaccuracy. TheMETRICmethodwasusedtoprovide
estimates of ET over olive orchards [32–34] and over cotton fields [35]. It tends to outperform TSEB
goodestimatesofEToveroliveorchards[33–35]andovercottonfields[36]. Ittendstooutperform
when sufficient ancillary data are not available [35]. To our knowledge, however, there are no studies
TSEB when sufficient ancillary data are not available [36]. To our knowledge, however, there are
on almond ET estimation by METRIC, and it is not clear how remote sensing-based ET approaches
nostudiesonalmondETestimationbyMETRIC,anditisnotclearhowremotesensing-basedET
perform in almond orchard with the complex canopy structure of almond trees.
approachesperforminalmondorchardwiththecomplexcanopystructureofalmondtrees.
In this paper, we aim to: (1) assess the accuracy of ET estimates at the field scale in an almond
Inthispaper,weaimto: (1)assesstheaccuracyofETestimatesatthefieldscaleinanalmond
oorrcchhaarrdd uussiinngg tthhee MMEETTRRIICC aapppprrooaacchh,, ddrriivveenn bbyy LLaannddssaatt ssaatteelllliittee oobbsseerrvvaattiioonnss aanndd EETTo ffrroomm CCIIMMIISS;;
o
(2) examine the dominant drivers for temporal and spatial variation in crop water use; and (3)
(2)examinethedominantdriversfortemporalandspatialvariationincropwateruse;and(3)develop
develop and assess empirical methods that are easy to use for ET estimation at the farm scale.
andassessempiricalmethodsthatareeasytouseforETestimationatthefarmscale.
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(a) (b)
Figure 1. Study area shown as: (a) a Landsat 5 TM false color image; and (b) true color image from
Figure 1. Study area shown as: (a) a Landsat 5 TM false color image; and (b) true color image
Google Earth. The subset extent of the Landsat image (path 42, row 35) used in the Mapping
fromGoogleEarth. ThesubsetextentoftheLandsatimage(path42,row35)usedintheMapping
Evapotranspiration at high Resolution with Internalized Calibration (METRIC) approach for our
Evapotranspiration at high Resolution with Internalized Calibration (METRIC) approach for our
evapotranspiration (ET) estimate includes the almond orchard, well-watered vegetation area and bare
evapotranspiration(ET)estimateincludesthealmondorchard,well-wateredvegetationareaandbare
soil area. The nearest California Irrigation Management Information System (CIMIS) weather station
soilarea.ThenearestCaliforniaIrrigationManagementInformationSystem(CIMIS)weatherstation
(#146) and flux tower installed at the southern edge of the orchard are also shown.
(#146)andfluxtowerinstalledatthesouthernedgeoftheorchardarealsoshown.
RemoteSens.2017,9,436 4of21
Remote Sens. 2017, 9, 436 4 of 21
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at the Belridge CIMIS station.
2009–2012attheBelridgeCIMISstation.
2.2. Remotely Sensed Data
2.2. RemotelySensedData
Landsat 5 TM and 7 ETM+ observations were obtained from USGS (http://earthexplorer.usgs.gov/)
Landsat 5 TM and 7 ETM+ observations were obtained from USGS (http://earthexplorer.
for path 42 and row 35 during 2009–2012, covering the almond orchard study area (Supplementary
usgs.gov/) for path 42 and row 35 during 2009–2012, covering the almond orchard study area
Materials Table S1). Forty-six cloud-free images were obtained during the four year study period,
(SupplementaryMaterialsTableS1).Forty-sixcloud-freeimageswereobtainedduringthefouryear
with an average of 12 images per year, mostly from April to November. The Landsat revisiting time
studyperiod,withanaverageof12imagesperyear,mostlyfromApriltoNovember. TheLandsat
was 16 days, but most images during December to March rain season were not usable for our analysis
revisitingtimewas16days,butmostimagesduringDecembertoMarchrainseasonwerenotusable
due to frequent cloud cover. All the images were subsetted to a common domain, as shown in Figure
forouranalysisduetofrequentcloudcover. Alltheimagesweresubsettedtoacommondomain,as
1a. A careful examination of individual Landsat 7 ETM+ images showed that this domain had no data
showninFigure1a. AcarefulexaminationofindividualLandsat7ETM+imagesshowedthatthis
gaps during the study period. This subset of 801 by 801 pixels included both bare soil and well-
domainhadnodatagapsduringthestudyperiod. Thissubsetof801by801pixelsincludedbothbare
watered full cover vegetation areas, as needed by METRIC approach for the purpose of selecting cold
soilandwell-wateredfullcovervegetationareas,asneededbyMETRICapproachforthepurposeof
and hot pixels to calculate sensible heat.
selectingcoldandhotpixelstocalculatesensibleheat.
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ffrroommC CIMIMISIS( h(htttptp:/:///wwwwww..cciimmiiss..wwaatteerr..ccaa..ggoovv//)),, aass aanncciillllaarryy iinnppuuttss ttoo tthhee MMEETTRRIICC aalglgoorritithhmm..C CIIMMIISS
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tiapnpeimngo-mbuetcekre, traanidn gtaiupgpeintgo-cboulclekcett inrfaoirnm agtaioungeo ntoso lcaorlrleacdti atiinofno,rmsoailtitoenm poenr atsuorlea,ra irratdemiatpioerna, tusroei,l
rteelmatipveerahtuumrei,d iatiyr, wteimndpedriaretuctrieo,n raenladtisvpee ehdu,manidditpyr,e cwipinitdat idonir,ercetsiopne ctainvdel ys.pDeaetda, aarnedco lplereccteipditeavteioryn,
respectively. Data are collected every minute and archived as totals or averages each hour. Hourly
and daily values of reference ETo are not measured directly, but calculated from meteorological data
RemoteSens.2017,9,436 5of21
minuteandarchivedastotalsoraverageseachhour. HourlyanddailyvaluesofreferenceET arenot
o
measureddirectly,butcalculatedfrommeteorologicaldatausingbothCIMISPenmanequationand
thestandardizedreferenceevapotranspirationequationforshortcanopies[6]. ThestandardizedET
o
equationdatawereusedinthisstudy. ET wasonlyusedfortheinternalcalibrationofenergybalance
o
andforcalculatingthefractionofgrassreferenceETasanindextorepresenttherelativechangein
weather[17](See3.2below).
The closest CIMIS station, Belridge station (CIMIS #146; Lat/Lon: 35.50◦N/119.69◦W),
is approximately 2 km away from the almond orchard (Figure 1a). Belridge station was located
near the center of footprint of Landsat 5 TM and 7 ETM+ image subsets, and thus was selected
to provide weather input data sets and reference ET for the METRIC approach in the study area.
o
Inparticular,weusedhourlyreferenceET ,airtemperature,vaporpressureandwindspeedatLandsat
o
overpasstime,typicallyaround10:30a.m.,anddailyreferenceET duringLandsatoverpassdatesfrom
o
BelridgeCIMISstation. HourlyreferenceET wasusedtointernallycalibrateMETRICapproachfor
o
sensibleheatfluxcalculation. Airtemperaturewasrequiredtocorrectlandsurfacetemperature(LST)
estimatesfromLandsatthermalimagery. DailyreferenceET wasusedtoconvertinstantaneousETat
o
LandsatoverpasstimetodailyEToverthealmondorchardintheMETRICapproach,asshownbelow.
2.3.2. ActualEvapotranspirationMeasurements
Evapotranspiration was measured with a micrometeorological tower installed 9 m above the
groundinthealmondorchardinearly2008. Thissystemincludesanetradiometer,sonicanemometer,
twothermocouples,andsoilheatfluxplates,whichwereassembledtomeasurecomponentsofsurface
energyfluxes[38]. AREBSInc. Q7.2netradiometerwasmountedatabout10mheightontheflux
towertomeasurenetradiation(R ). Sensibleheatflux(H)wasmeasuredwithbothanRMYoung
n
81000RE 3-d sonic anemometer and with Campbell Scientific Inc. FW3 fine-wire thermocouples
as described in Shapland et al. [39]. The ground heat flux data were measured using three sets of
groundheatfluxpackages,includingaheatfluxplate(REBSInc.,Seattle,WA,USA,HFT3)andasoil
temperatureaveragingsensor(CampbellScientificInc.,Logan,UT,USA,Tcav),whichwereestablished
nearthetowertomeasuretheaveragegroundheatflux(G)atthesoilsurface[40]. Latentheatflux
(LE)wasthenestimatedastheresidualoftheenergybalance(LE=R −G−H)everyhalfhourand
n
convertedtoETbydividingtheLEinMJm−2 perhalfhourby2.45MJmm−1 ofwatervaporized.
HourlyanddailyETofthealmondorchardwascalculatedfromthehalf-hourlydatacollectedfrom
2009through2012. UsingthestationandmeasurementmethodologydescribedinShaplandetal.[39],
therewerealmostnomissinghalfhourestimatesofalmondET.DailyandmonthlycalculationsofET
wereusedtovalidatetheMETRICapproachforestimatingandmappingalmondET.
3. Methodology
3.1. Landsat5TMand7ETM+DataPreprocessing
Thedigitalnumber(DN)ofvisible,nearinfraredandshortwaveinfraredbandsofLandsat5TM
and7ETM+wasfirstconvertedtoradiance,andthenatmosphericallycorrectedtosurfacereflectance
by using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) [41]. Surface
albedo[42],normalizeddifferencevegetationindex(NDVI)[43]andleafareaindex(LAI)[44]were
derivedfromthespectralreflectancestocalculateenergyfluxes(R ,G,andH)intheMETRICapproach.
n
Theoriginalresolutionofthethermalband(band6)is120m. Weheredownscaledthethermal
datatoa30-meterresolutiontomatchtheresolutionofthereflectivebandsbyusingcubicconvolution
method. The thermal bandDN wasthen convertedto radiance usingthe coefficientsprovidedin
the imagery metadata. A correction to the radiance was further performed using the method of
Wukelicetal.[45],andthePlankequationwasthenappliedtocomputethelandsurfacetemperature
(LST).Cloudfraction(CF)maskforeachLandsatimage,providedbyUSGS(http://earthexplorer.
usgs.gov/),wasalsousedtoremovedataaffectedbycloudandcloudshadow.
RemoteSens.2017,9,436 6of21
3.2. METRICEvapotranspirationEstimate: GeneralApproach
METRIC™, a satellite-based surface energy balance approach, has been widely used to map
field-scalecropET,mostlyoverrowcrops. Itisaone-sourcemodelthatconsiderstranspirationof
vegetationandevaporationofsoilasawhole. Latentheatflux(LE),directlyrelatedtoET,iscomputed
asaresidualaccordingtosurfaceenergybalanceequation(Equation(1)):
LE =R −G−H (1)
n
Themajorenergycomponentsareestimatedasbelow:
R = (1−α)∗R ↓ +R ↓ −R ↑ −(1−ε )∗R ↓ (2)
n s l l 0 l
(cid:16) (cid:17)
G= 0.05+0.18∗e−0.52∗LAI ∗R (LAI≥0.5)
n
(cid:18) Ts−273.16 (cid:19)
G= 1.80∗ +0.084 ∗R (LAI<0.5) (3)
n
R
n
(cid:16) (cid:17)
H= ρ∗c ∗dT /r (4)
p ah
whereαisthesurfacealbedo,R ↓istheincomingshortwaveradiation(W/m2),R↓istheincoming
s l
longwaveradiation(W/m2),R↑istheoutgoinglongwaveradiation(W/m2),ε isthebroadband
l 0
surfaceemissivity,ρistheairdensity(1.15kg/m3),c istheairspecificheat(1004J/kg/K).Tsisthe
p
landsurfacetemperature(K),anddTisthenearsurface-airtemperaturedifference(K).dTisassumed
tohavealinearrelationshipwithTs(Equation(5))asdevelopedbyBastiaanssen[46](FigureS1).
dT=a∗Ts+b (5)
wherethecoefficientsaandbwerecalculatedusingtwosetsof“hot”and“cold”anchorpixels,as
shownbelow:
dT −dT
a= hot cold b=dT −a∗Ts (6)
Ts −Ts hot hot
hot cold
wheredT = Hhot∗rahhot anddT = Hcold∗rahcold. TheinitialvaluesofH,r ,andTswerefromthe
hot ρ∗cp cold ρ∗cp ah
selectedcoldandhotpixelsatthebeginningoftheiteration.
r is the aerodynamic resistance to heat transport (s/m). An iterative method was applied
ah
to calculate both r and H to reduce the effects of buoyancy of heated, light air at the surface.
ah
Thealgorithmstartedwithfindingasetsof“hot”and“cold”pixels. ItassumedthatLEequalsto0.05
and1.05timesreferenceET(ET )for“hot”and“cold“pixelsatthebeginningoftheiteration,and
o
thencalculateddT anddT (Equation(4)),followedbycalculatingaandb,andthenupdatingdT,
hot cold
H,andr . Weused5%ofreferenceET,insteadof0,toinitiatetheLEfor“hot”pixels,consideringthe
ah
possibilityofsoilmoisturecontentinbaresoilsshownby[17]. Wetestedthepossibleimpactofusing
ET forinternalcalibration,andfoundthatusing1.05asaconstantledtotheclosestagreementbetween
o
observedandestimatedET.TheMonin–Obuknovlength(L)wasusedtodefinethestabilityconditions
oftheatmosphereduringiterationinMETRIC[17]. Theiterationstopswhenthevalueofr remains
ah
stable[44](seeFigureS1). Aftertheconvergenceofthehotandcoldpixels,iterationsarealsoneeded
toconvergethesurfaceenergybalancecomponents. Dhungeletal.[47]demonstratedtheimproved
behaviorofsurfaceenergybalanceconvergenceinthelowwindspeed,whichisparticularlyimportant
toreduceuncertaintyinsurfaceenergybalancecomponentsduetothelackoftheconvergence. Inour
study,wesetthattheiterationprocessofHcalculationstopswhenthecoefficientsofaandbremain
stable. Ourtestshowedthatittypicallytakes5or6iterationstoreachconvergencewhilelowwind
conditionsrequiredmoreiterations. AsimplifiedflowchartofMETRICapproachisshowninFigure3.
RemoteSens.2017,9,436 7of21
Remote Sens. 2017, 9, 436 7 of 21

FigurFeig3u.reF 3lo. wFlcohwacrhtaortf oMf EMTERTIRCICa pappproroaacchh.. IInnppuut tddaatat asestest sinicnlucdluindgi nLganLdasnadt s5a tTM5 TaMnd a7n EdT7M+E TM+
obserovbasteiorvnastfiroonms fUroSmG SUaSnGdSw aenadth ewredaathtaerf rodmataB efrlroimdg eBeClrIMidgISe sCtaItMioInS asrteatuiosend atroe caulsceudl attoe icnasltcaunlataten eous
surfacinesetannetragnyeofluusx essur(fiancset aenntearngeyo uflsuvxeasl u(einssatatnLtaanndeosuats ovvaelurpesa sast tiLmaned)ssautc hovaesrpnaestsr atidmiaet)i osnucfhlu axs (Rnent) ,soil
heatfrlaudxia(tGio)na nflduxs e(nRsni)b, lseohile haetaftl ufxlu(xH ()Gi)n atnhde spernosciebsles inhegapt rfolucxe d(Hur)e i.nT thhee dperroicveesdsinings ptarnoctaednueoreu. sTfhrea ction
ofrefedreernivceedE iTns(tEanTtoaFn)eoanusd frreafcetiroenn coef rEeTfeorewnceere EtTh (eEnTuoFs)e adntdo rdefeerrievneced EaiTlyo wanerde mtheonn tuhsleyde tvo adpeoritvrea ndsapiliyr ation
and monthly evapotranspiration (ET) maps over the almond orchard. The detailed description of
(ET)mapsoverthealmondorchard. ThedetaileddescriptionofMETRICapproachcanbefoundin
METRIC approach can be found in Allen et al. [16,25].
Allenetal.[17,26].
In METRIC, ET of each pixel at Landsat overpass time (10:30 a.m.) is calculated from Equation
I(n7)M, anEdT RthIeCn, tEhTe forfacetaiocnh opfi xreeflearetnLcaen EdTs a(EtToovFe) rips acaslscutilmateed(1 b0y:3 d0ivai.dmin.)g iisncstaalncutalnaeteodusf rEoTm byE hqouuartliyo n(7),
and trheefenretnhcee fErTaoc (tEioqnuaotiforne (f8e)r)e, nobcetaiEnTed( EfrTomoF B)eislricdaglec uClIaMteIdS sbtaytidoniv. iIdt ainssguminesst athnatta tnheeo vuasluEeT ofb EyTohFo urly
at Landsat overpass time is the same as the average value of 24 h during Landsat overpass day [16,25].
reference ET (Equation (8)), obtained from Belridge CIMIS station. It assumes that the value of
o
ET FFaintaLllayn, tdhsea dtaoilvye ErTp aast seatcihm peixiesl tihs ecaslcaumlaeteads atsh ae parovdeuractg oef vETalouF eanodf d2a4ilyh rdefuerrienngceL EaTno d(Esqautaotivoenr pass
o
(9)), which is the accumulative 24 h reference ETo at the station during the dates when Landsat
day[17,26]. Finally,thedailyETateachpixeliscalculatedasaproductofET Fanddailyreference
satellite overpasses every 16 days and when imagery is not obscured by cloud. o
ET (Equation(9)),whichistheaccumulative24hreferenceET atthestationduringthedateswhen
o o
Landsatsatelliteoverpassesevery16dEaTy(cid:2919)(cid:2924)s(cid:2929)(cid:2930)a=nd36w0h0e∗nLiEm/(aλg∗erβy(cid:2933))is notobscuredbycloud. (5)
ET F=ET /ET (6)
(cid:2925) (cid:2919)(cid:2924)(cid:2929)(cid:2930) (cid:2899)_(cid:2919)(cid:2924)(cid:2929)(cid:2930)
ET =3600∗LE/(λ∗β ) (7)
inst w
ET_daily=ET F∗ET _daily (7)
(cid:2925) (cid:2899)
where ETinst is the instantaneous ET inE TmomF/=houErT, i3n6st0/0E isT uOs_einds tfor converting from seconds to hours, (8)
λ is the latent heat of vaporization in J/kg, βw is the density of water, ETo_inst is the hourly reference
ET_daily=ET F∗ET _daily (9)
ET of grass at the time of image, ET_daily is the 24o h actuaOl ET, and ETo_daily is the accumulative 24
whereh EreTferenicset hETeoi nonst athnet adnaeteo oufs iEmTagine amcqmu/ishitoiounr., 3600isusedforconvertingfromsecondstohours,λ
inst
ET-based irrigation scheduling requires continuous daily ET estimate especially during growing
isthelatentheatofvaporizationinJ/kg,β isthedensityofwater,ET isthehourlyreferenceET
w o_inst
season, so that ET losses could be replaced by applying the proper amount of water at the right time
ofgrassatthetimeofimage,ET_dailyisthe24hactualET,andET _dailyistheaccumulative24h
to meet the plant water demands. We applied a cubic spline interpolaotion to fill in the gaps of daily
referenceET onthedateofimageacquisition.
EToF usiong the Landsat-based estimates of EToF at the four most adjacent dates when Landsat clear
EskTy- boabsseedrviartriiognast iwonersec ahveadiluablilneg. Are cquubiirce sspcloinnet iwnuaso fuirssdt afiitltyedE tTo egsetnimeraattee ae sspmeocoiathlleydd EuTroiFn gcugrrvoew ing
seasobna,sseod tohna tthEeT nloons-sleinsecaoru cludrvbee nraetpulraec eodf tbhye taepmpplyoirnalg dtyhneapmriocps eorf afrmacotuionnt ooff rwefaerteenrcaet EthTe [4ri3g].h Tthtiem eto
meetctohmepplleatne ttiwmaet seerrdieesm ofa dnadilsy. EWTeoFa wppasli tehdena dcuerbivicedsp frlionme tihnet esrpplionleadti counrvtoe, fialnldin usthede igna cposmobfindaatiiloynE ToF
usingwtihthe tLhae ncodnstaitn-uboausse ddaeilsyt irmefaerteenscoef EETTo oaFt tahte tBheelrifdoguer CmIMosISt astdajtaiocne ntot ddeartievse wdahileyn ELTa ensdtimsaattec lfeoarr sky
obserevvaetriyo nsisnwglee rdeaayv. aMiloanbtlhel.yA, sceuasboincaslp, alinnde awnansuafilr sEtTfi etstetidmtaotegs ewneerrea tfeinaalslym aogogtrheegdatEedT frFomcu rdvaeilyb ased
o
values. The temporal interpolation was only done during the growing season from April to
onthenon-linearcurvenatureofthetemporaldynamicsoffractionofreferenceET[44]. Thecomplete
September, since there were too many missing data during October to March winter rainy season.
timeseriesofdailyET Fwasthenderivedfromthesplinedcurve,andusedincombinationwiththe
o
continuousdailyreferenceET attheBelridgeCIMISstationtoderivedailyETestimateforevery
3.3. Automated “Cold” and “Hoot” Pixel Selection
singleday. Monthly,seasonal,andannualETestimateswerefinallyaggregatedfromdailyvalues. The
In order to estimate sensible heat flux (H) accurately, two “anchor” pixels, named “cold” pixel
temporalinterpolationwasonlydoneduringthegrowingseasonfromApriltoSeptember,sincethere
and “hot” pixel must be selected to determine the relationship between Ts and dT in the METRIC
weretoomanymissingdataduringOctobertoMarchwinterrainyseason.
approach. “Cold” pixels represent well-watered, full-cover vegetation areas within an image, and
thus have relatively higher NDVI and relatively low Ts. The “hot” pixels, namely dry and bare soil,
3.3. Automated“Cold”and“Hot”PixelSelection
on the other hand, usually have low NDVI but high Ts. The selection of these anchor pixels typically
Iinnvoorldveesr stoomees ttirmainaitnegs aennds imblaenhuaela itmflaugxe i(nHte)rapcrcetuartaiotne,l ya,ntdw iso o“fatennc ahrobrit”rapriyx. eTlos, mnaakme ethde“ pcroolcde”ssp ixel
and“ hot”pixelmustbeselectedtodeterminetherelationshipbetweenTsanddTintheMETRIC
approach. “Cold”pixelsrepresentwell-watered, full-covervegetationareaswithinanimage,and
thushaverelativelyhigherNDVIandrelativelylowTs. The“hot”pixels,namelydryandbaresoil,
RemoteSens.2017,9,436 8of21
oRnemtohtee Soenths. e2r01h7a, 9n, d43,6u s uallyhavelowNDVIbuthighTs. Theselectionoftheseanchorpixelstypi8c oafl l2y1
involvessometrainingandmanualimageinterpretation,andisoftenarbitrary. Tomaketheprocess
mmoorree ccoonnssiisstteenntt,, wweed deevveeloloppeedda am metehthooddt otos esleelcetctth tohsoespe ipxeixlselasu atuomtoamtiactaicllayllbya bseadseodn otnh ethseta stitsattiicsstiocfs
NofD NVDIVanI danTds mTsa mpsa,pfos,l lfoowlloinwginangd anindt eingrteagtirnagtisnigm siilmarilcaorn ccoenpctsepptrso pproospedosbeyd pbrye vpiroeuvsiosutus dstiuesd[i4e8s –[5417]–.
T5h0]e. cTuhme uculamtivuelahtiivsteo hgirsatmogorfamND oVf NIaDnVdIT asnwde Tres wfiresrteg feinrsetr agteende.rTatheedv. aTlhuee svoalfuNesD oVfI NatDtVheI apto tihnet opfo5in%t
(o9f5 5%%) a(9n5d%t)h aenvda ltuhees voafluTessa otft hTes pato tihnet opfo9in5t% o(f5 9%5%)i n(5t%he) ianc cthuem auclcautmeduplaetrecde npteargceenctuargvee scuwrverees uwseerde
iunsecodm inb incoamtiobninaastitohnr eassh otlhdrsesfhoorliddse nfotirf yiidnegnhtiofyti(ncgo ldh)otp i(xceollsd.)T poixalellosw. Tfoo rafllleoxwib ifloitry ,flwexeibeixltirtya,c twede
aelxltpraixcteelsd faalllli npgixbeolst hfawlliitnhgin b±ot0h.0 1woitfhtihne ±N0D.0V1 Iotfh rtehseh oNldDsVaIn tdhwreisthhoinld±s 0a.n5d◦ CwoitfhTisn th±r0e.5s h°oCld o,fa nTds
athvreersahgoelddo, vanerdt ahveeseratgweod soevtsero fthpeisxee ltswtoo csaetlcsu olfa tpeixtheles mtoe acnalcTuslfaotre “thhoe tm”aenand T“cso flodr” “phioxte”l sa(nFdig “ucroeld4)”.
Wpiexetelss t(eFdigimuraeg e4r)i. eWsien dteisffteerde nimtsaegaesroiness ainn ddfioffuenredntth saetatshoenrse saunldti nfgouTnsd− tdhaTt rtehlaet iroenssuhltipinsgw Tits h−t hdiTs
arueltaotmioantsihcispesl ewctiitohn thmise tahuotdomwaetriec ssiemleiclatirotno mtheotsheodfr owmerme asinmuiallasr etloe cthtioosnes .from manual selections.
(a) (b)
Figure 4. Automatic selection of “cold” pixels and “hot” pixels based on accumulative percentage of
Figure4.Automaticselectionof“cold”pixelsand“hot”pixelsbasedonaccumulativepercentageof
(a) NDVI and (b) land surface temperature (LST) maps. Red curve shows the accumulative
(a)NDVIand(b)landsurfacetemperature(LST)maps.Redcurveshowstheaccumulativepercentage.
percentage.
3.4. EvaluationofEvapotranspirationEstimate
3.4. Evaluation of Evapotranspiration Estimate
ToassesstheuncertaintiesofMETRICapproachinestimatingEToverthealmondorchard,we
To assess the uncertainties of METRIC approach in estimating ET over the almond orchard, we
comparedtheMETRICETwithfieldmeasurements. Comparisonsweremadeforinstantaneous,daily
compared the METRIC ET with field measurements. Comparisons were made for instantaneous,
andmonthlyET.Consideringthefetchareaof50by50moftheinstalledfluxtowerinthestudyarea,
daily and monthly ET. Considering the fetch area of 50 by 50 m of the installed flux tower in the study
weaveragedtheMETRICETover2by3pixelscenteredatthefluxtowerlocation. Fourstatisticswere
area, we averaged the METRIC ET over 2 by 3 pixels centered at the flux tower location. Four statistics
reported,whicharemeanbias,meanrelativedifference(MRD),rootmeansquarederror(RMSE)and
were reported, which are mean bias, mean relative difference (MRD), root mean squared error
determinationcoefficient(R2). Theequationsofbias,MRDandRMSEareshownasfollows:
(RMSE) and determination coefficient (R2). The equations of bias, MRD and RMSE are shown as
follows:
Bias=ET −ET (10)
METRIC Tower
Bias = ET −ET (8)
|ET (cid:2897)(cid:2889)(cid:2904)(cid:2902)(cid:2893)(cid:2887)−ET(cid:2904)(cid:2925)(cid:2933)(cid:2915)(cid:2928)|
MRD(%) = METRIC Tower ∗100 (11)
|ET −ET |
MRD (%)= (cid:2897)(cid:2889)(cid:2904)E(cid:2902)T(cid:2893)(cid:2887)Tower (cid:2904)(cid:2925)(cid:2933)(cid:2915)(cid:2928) ×100 (9)
(cid:115) ET(cid:2904)(cid:2925)(cid:2933)(cid:2915)(cid:2928)
∑n (ET −ET )2
RMRMSESE== (cid:3496)∑i=(cid:2924)(cid:2919)(cid:2880)1(cid:2869)(ETM(cid:2897)(cid:2889)E(cid:2904)T(cid:2902)Rn(cid:2893)I(cid:2887)C−ET(cid:2904)T(cid:2925)o(cid:2933)w(cid:2915)e(cid:2928)r)(cid:2870) (1(102) )
n
whereET isMETRIC-basedETestimateatLandsat5TMand7ETM+overpassdateandET
METRIC Tower
isground-basedETmeasurementatthesamedayfrommicrometeorologicaltower. Wealsoexamined
where ETMETRIC is METRIC-based ET estimate at Landsat 5 TM and 7 ETM+ overpass date and ETTower
theabilityofMETRICapproachtocapturetheinter-annualandspatialvariationofET.
is ground-based ET measurement at the same day from micrometeorological tower. We also
examined the ability of METRIC approach to capture the inter-annual and spatial variation of ET.
3.5. SimplifiedEmpiricalApproachesforEvapotranspirationEstimate
3.5. SMimapnlyifiegdr oEwmeprirsicdaol Anpoptrohaachvees tfohre Ecvaappaoctriatynstpoiraimtiopnl eEmsteimntatseo phisticated remote sensing based
algorithmfortheirirrigationmanagement,especiallyonadailybasis. Wethusalsoaimedtodevelop
Many growers do not have the capacity to implement sophisticated remote sensing based
algorithm for their irrigation management, especially on a daily basis. We thus also aimed to develop
easy-to-use and cost-effective approaches for growers to estimate ET with easily accessible data such
as NDVI and weather data in this study. We took advantage of the spatially explicit ET estimates
RemoteSens.2017,9,436 9of21
easy-to-useandcost-effectiveapproachesforgrowerstoestimateETwitheasilyaccessibledatasuchas
NReDmoVteI Saennsd. 2w01e7a, t9h, 4e3r6d atainthisstudy. WetookadvantageofthespatiallyexplicitETestimatesder9i ovfe 2d1
fromLandsatdata,andexaminedthemainfactorscontrollingspatialandtemporalETvariationusing
derived from Landsat data, and examined the main factors controlling spatial and temporal ET
bothtemporalandspatialdata. WequantifiedtherelationshipbetweenMETRICET,asadependent
variation using both temporal and spatial data. We quantified the relationship between METRIC ET,
variable,andNDVI,whichpotentiallycanberelativelyeasytoderivefromimagerybydrones,and
as a dependent variable, and NDVI, which potentially can be relatively easy to derive from imagery
asuiteofenvironmentalvariablesthatgrowershaveeasyaccessto. Multivariatestatisticalmodels
by drones, and a suite of environmental variables that growers have easy access to. Multivariate
werebuiltusingthreeyearsofspatialdata. Theunivariatelinearandnonlinearrelationshipsbetween
statistical models were built using three years of spatial data. The univariate linear and nonlinear
ETandtheexplanatoryvariableswerefirstexamined,andthenvariouscombinationsoflinearand
relationships between ET and the explanatory variables were first examined, and then various
exponentialtermsofkeyETdrivingfactors,includingNDVI,netradiation,andvaporpressuredeficit,
combinations of linear and exponential terms of key ET driving factors, including NDVI, net
were explored. The parameters were optimized using “lsqcurvefit” in MATLAB to minimize the
radiation, and vapor pressure deficit, were explored. The parameters were optimized using
differencebetweenpredictedandMETRIC-estimatedET.Themodelswerevalidatedwiththerestyear
“lsqcurvefit” in MATLAB to minimize the difference between predicted and METRIC-estimated ET.
ofbothspatialETdataandfieldmeasuredETaswell.
The models were validated with the rest year of both spatial ET data and field measured ET as well.
4. Results
4. Results
4.1. EvaluationwithFluxTowerData
4.1. Evaluation with Flux Tower Data
WecomparedtheinstantaneousMETRC-basedETestimateusingLandsat5TMand7ETM+
data,Wavee croamgepdaroevde rthae 2inbsytan3tLanaenodussa tMpEixTeRlCw-ibnadseodw EcTe netsetrimedataet uthsiengfl uLxantodwsaetr 5l oTcMat iaonnd, w7 iEthTMET+
vdaaltuae, savdeurarignegd 1o0v:e0r0 aa 2. mby. 3t oLa1n1d:0s0at ap.imxe.l wdeinridvoewd cfreonmterfledu xatt tohwe eflrumx teoawsuerre lmoceantitosnd, uwriitnhg ELT avnadlusaest
odvuerrinpga s1s0in:0g0 daa.mte.s t.o A11g:g0r0e ga.amte.d deErTiveesdt ifmroamte sfluatx dtoawilye,rm moenatshulrye,manendtsa ndnuurianlgt iLmaendscsaatle osvwerepraesasilnsog
cdoamteps.a Aregdgwreigtahtecdo rEreTs peostnimdiantgesfi aetl ddmaileya, smuroenmthelnyt,s ainndo radnenrutaol vtiamlied astcealtehse wpeerrfeo armlsoa nccoemopfaMreEdT wRIitCh
acporprreospacohndwinhge nfieiltdw maesaaspurpelmieednttos imn aoprdEerT taot vdailfifdearteen tthtei mpeerfsocramleasnocev eorf aMnEaTlRmIoCn adpoprrcohacahrd wihnetnh eit
swoaust haperpnlieSdan toJ omaaqpu EinTV aat ldleifyf.erent time scales over an almond orchard in the southern San Joaquin Valley.
44..11..11.. IInnssttaannttaanneeoouuss aanndd DDaaiillyy EETT oonn LLaannddssaatt OOvveerrppaassssiinngg DDaatteess
TThhee iinnssttaannttaanneeoouuss EETT dduurriinngg LLaannddssaatt oovveerrppaassssiinngg ttiimmee aarroouunndd 1100::3300 aa..mm..,, ddiirreeccttllyy eessttiimmaatteedd
tthhrroouugghh MMEETTRRIICC aapppprrooaacchh,, aaggrreeeedd wweellll wwiitthh fifieelldd mmeeaassuurreemmeennttss iinn ggeenneerraall,, wwiitthh aa RR22 ooff 00..7744 aanndd
RRMMSSEE ooff0 0.1.111m mmm/h/h,oouver,r oavtoetra al otfo4ta6lL oafn 4d6s aLtaonvderspaat sosvinegrpcalosusidn-gfr eceloduady-sfrferoe mda2y0s0 9frtoom20 21020(9F igtou r2e051a2
a(FnidguTraeb l5ea 1a)n.dW Thaebnlea g1g).r Weghaetend aagtgdreagilaytetdim aet sdcaailley, taimbeet tsecralaeg, rae ebmetetenrt awgaresefmouenndt ,wwaist hfoaunRd2,o wf0it.h8 7a,
RRM2 oSfE 0o.8f70, .8R0MmSmE /odf a0y.8.0H migmhe/draaycc. uHraigchyewr aasccfuourancdy dwuarsin fgouthnedg drouwriningg tsheea sgoronw,ei.ngg., Rse2arseomn,a ein.gs.,t hRe2
sraemmea,inbsu tthReM sSaEmwe, absurte dRuMceSdE tow0a.s5 3remdmuc/edda tyoa 0n.d53m meamn/rdealyat iavnedd imffeearenn rceela(MtivReD d)ilfofewreenrecde t(oM8R.0D%)
(lFoiwguerreed5 btoa 8n.d0%Ta (bFliegu1)r.e 5b and Table 1).
(a) (b)
Figure 5. Site-level comparison of: (a) instantaneous; and (b) daily ET estimated by Landsat-METRIC
Figure5.Site-levelcomparisonof:(a)instantaneous;and(b)dailyETestimatedbyLandsat-METRIC
on a total of 46 Landsat images with those measured by flux tower, at Landsat overpass time around
onatotalof46Landsatimageswiththosemeasuredbyfluxtower,atLandsatoverpasstimearound
10:30 a.m. and on Landsat overpass dates during 2009–2012 over the almond orchard.
10:30a.m.andonLandsatoverpassdatesduring2009–2012overthealmondorchard.
RemoteSens.2017,9,436 10of21
Remote Sens. 2017, 9, 436 10 of 21
TTaabbllee 11.. AAccccuurraaccyy ooff iinnssttaannttaanneeoouuss aanndd ddaaiillyy MMEETTRRIICC EETT eessttiimmaatteess iinn ccoommppaarriissoonn wwiitthh flfluuxx ttoowweerr
mmeeaassuurreemmeennttss dduurriinngg LLaannddssaatt oovveerrppaassss ddaatteess iinn 22000099––22001122..
Instantaneous ET Daily ET
InstantaneousET DailyET
Number RMSE Bias MRD RMSE Bias MRD
Year Number R2 RMSE Bias R2 RMSE Bias MRD
Year of Days R2 (mm/h) (mm/h) MR(D%()% ) R2 (mm/Day) (mm/Day) (%)
ofDays (mm/h) (mm/h) (mm/Day) (mm/Day) (%)
All clear-sky Landsat overpassing dates
2009 14 0.82 0.11 A0.l0l0c lear-sk2y7L.9a1n dsat0o.8v7e rpassing0.9d3a tes −0.02 31.82
22000190 1141 00..8425 00.1.112 0.00.002 271.69.170 00.8.776 00.9.931 −−00.0.322 3111.8.629
22001101 1110 00..4859 00.1.122 0.00.204 162.87.047 00.7.968 00.9.716 −00..3327 1217.6.391
22001112 1101 00..8990 00.1.120 0−.004.06 281.34.702 00.9.986 00.7.469 0.03.727 2173.3.617
2T0o1ta2l 1416 00..9704 00.1.101 −00.0.060 132.10.279 00.9.867 00.4.890 0.02.706 1231.6.678
Total 46 0.74 0.11 0.00 21.79 0.87 0.80 0.06 21.68
Growing season (April–September)
2009 9 0.69 0.05 −0.G02ro wing6s.e3a8s on(A0p.r7i8l– Septem0b.5e6r ) −0.16 7.56
22000190 99 00..6896 00.0.059 −00.0.025 61.328.64 00.7.889 00.5.468 −−00.1.066 76.5.466
22001101 98 00..8769 00.0.191 0.00.501 121.56.493 00.8.994 00.4.584 −00..0163 68.4.361
22001112 88 00..7690 00.1.111 0−.001.06 151.09.380 00.9.848 00.5.544 0.01.326 89.3.713
2T0o1ta2l 834 00..6604 00.1.019 −00.0.060 101.18.032 00.8.887 00.5.543 0.02.603 97.7.936
Total 34 0.64 0.09 0.00 11.32 0.87 0.53 0.03 7.96
The METRIC approach captured similar seasonal dynamics of daily ET as the field
measurements (Figure 6 and FigureS2). For example, estimated ET varied from 1.61 mm on 3
TheMETRICapproachcapturedsimilarseasonaldynamicsofdailyETasthefieldmeasurements
December to 7.72 mm on 28 July in 2009. Similarly, the measured ET was lowest in January and
(Figure6andFigureS2).Forexample,estimatedETvariedfrom1.61mmon3Decemberto7.72mmon
December, increased gradually until June and July with a maximum of 9.15 mm/day on 20 June, and
28Julyin2009. Similarly,themeasuredETwaslowestinJanuaryandDecember,increasedgradually
then declined again, reaching a minimum ET of 0.17 mm/day on 29 December. The large day-to-day
untilJuneandJulywithamaximumof9.15mm/dayon20June,andthendeclinedagain,reaching
aflmucitnuiamtiuomn oEfT fioefld0. 1m7emasmu/redda yETo nm2o9sDtlyec feomllobwer.edT hthealat rogfe thdea yr-etfoe-rdeanycefl EucTtou. aTthioen moefafine lddamilye aMsuErTedRIECT-
estimated ET was 5.10 mm/day, averaged over a total of usable 14 Landsat overpassing days in 2009,
mostlyfollowedthatofthereferenceET . ThemeandailyMETRIC-estimatedETwas5.10mm/day,
o
which was similar to that from the field measurements (5.12 mm/day) and about 7% higher than the
averagedoveratotalofusable14Landsatoverpassingdaysin2009,whichwassimilartothatfrom
tmheeafine dldaimlye raesfuerreemnceen EtsTo( 5o.1f 24.m80m m/mda/dya)ya.n Ad haibgohu bti7a%s ohf itghhee MrtEhTaRnItCh eapmperaonacdha wilyasr efofeurnedn cine EwTintoefr
o
in both 2009 and 2010.
4.80mm/day. AhighbiasoftheMETRICapproachwasfoundinwinterinboth2009and2010.

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