Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield

23 Pages·2016·5.57 MB·English

remote sensing
Article
Do Agrometeorological Data Improve Optical
Satellite-Based Estimations of the Herbaceous Yield
in Sahelian Semi-Arid Ecosystems?
AbdoulAzizDiouf1,2,*,PierreHiernaux3,MartinBrandt4,GayaneFaye1,BakaryDjaby5,
MouhamadouBambaDiop1,JacquesAndréNdione1andBernardTychon2
1 CentredeSuiviEcologique,RueAiméCésairexLéonGontranDamas,BP15532Fann-Dakar,Senegal;
[email protected](G.F.);[email protected](M.B.D.);[email protected](J.A.N.)
2 Water,EnvironmentandDevelopmentUnit,UniversityofLiège,AvenuedeLongwyB6700,
6700Arlon,Belgium;[email protected]
3 GéosciencesEnvironnementToulouse(GET),ObservatoireMidi-Pyrénées,UMR
5563(CNRS/UPS/IRD/CNES),14AvenueEdouardBelin,31400Toulouse,France;
[email protected]
4 DepartmentofGeosciencesandNaturalResourceManagement,UniversityofCopenhagen,
1350Copenhagen,Denmark;[email protected]
5 CentreRegionalAGRHYMET,BP11011Niamey,Niger;[email protected]
* Correspondence:[email protected];Tel.:[phone]
AcademicEditors:SangramGanguly,ComptonTucker,AlfredoR.HueteandPrasadS.Thenkabail
Received:23April2016;Accepted:10August2016;Published:18August2016
Abstract: Quantitativeestimatesofforageavailabilityattheendofthegrowingseasoninrangelands
arehelpfulforpastorallivestockmanagersandforlocal,nationalandregionalstakeholdersinnatural
resource management. For this reason, remote sensing data such as the Fraction of Absorbed
Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant
productivityforabout40years. ThisstudycombinestraditionalFAPAR-basedassessmentswith
agrometeorologicalvariablescomputedbythegeospatialwaterbalanceprogram,GeoWRSI,using
rainfallandpotentialevapotranspirationsatellitegriddeddatatoestimatetheannualherbaceous
yieldinthesemi-aridareasofSenegal. Itshowedthatamachine-learningmodelcombiningFAPAR
seasonal metrics with various agrometeorological data provided better estimations of the in situ
annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on
FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55;
RMSE=585kg·DM/ha). Allthemodelsprovidedreasonableoutputsandshowedadecreasein
themeanannualyieldwithincreasinglatitude,togetherwithanincreaseinrelativeinter-annual
variation. Inparticular,theadditionaluseofagrometeorologicalinformationmitigatedthesaturation
effectsthatcharacterizetheplantindicesofareaswithhighplantproductivity. Inaddition,thedate
oftheonsetofthegrowingseasonderivedfromsmoothedFAPARseasonaldynamicsshowedno
significantrelationship(0.05p-level)withtheannualherbaceousyieldacrossthewholestudiedarea.
Thedateoftheonsetofrainfallhowever,wassignificantlyrelatedtotheherbaceousyieldandits
inclusioninfodderbiomassmodelscouldconstituteasignificantimprovementinforecastingrisksof
amassherbaceousdeficitatanearlystageoftheyear.
Keywords: herbaceousannualyield;FAPAR;startofseason;grasslands;GeoWRSI;satelliteremote
sensing;Cubist;landcoverclass;Sahel;Senegal
RemoteSens.2016,8,668;doi:10.3390/rs8080668 www.mdpi.com/journal/remotesensing
RemoteSens.2016,8,668 2of23
1. Introduction
IntheSahelbeltsouthoftheSaharadesert,anaridtosemi-aridregion,thevegetationiscomposed
ofaherbaceouslayerdominatedbyannualgrassesandscatteredwoodyplantsincludingbushes,
shrubsandsmalltrees,amongwhichareseveralthornyspecies[1,2]. Theseareasprovidethebulk
ofpastorallivestockfeeding[3]andcontributetocarbonsequestration,nutrientuptakeandcycling,
soilfixationandsoilbiologicactivity,aswellaswatercycleregulation. Inthiscontext,anaccurate
evaluationofherbaceousmassyieldattheendofthegrowingseasonisessentialforensuringthe
rationaluseofavailableresourcesandenvironmentalsustainability. Vegetationindicesderivedfrom
satellitedatahavebeenwidelyusedtomonitorSahelianherbaceousproductivityforabout40-years.
Aftertheseveredroughtof1970s,thefirstapplicationsofherbaceousyieldestimationinSahelian
rangelandsusingtheNormalizedDifferenceVegetationIndex(NDVI)fromtheNationalOceanicand
AtmosphericAdministration-AdvancedVeryHighResolutionRadiometer(NOAA-AVHRR)were
published and were pioneer studies in the field of applied remote sensing [4–6]. In the past two
decadesandwithregardtothetechnologicaladvancesinsensordesignforvegetationmonitoring[7],
newsatellitessuchastheSatellitePourl’ObservationdelaTerre-VEGETATION(SPOT-VGT)and
theModerateResolutionImagingSpectroradiometer(MODIS)-TERRA/AQUAhavebeenlaunched
andmoredatasetshavebecomeavailablewithhigherspatialanddensertemporalresolutions[8,9].
In addition to the NDVI, the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)
in relation to surface processes such as photosynthesis [10] has been recognized as constituting a
key variable in the assessment of vegetation status [11,12]. For this purpose, the metrics of the
FAPARseasonaldynamicsarecommonlyusedinenvironmentalstudies[13–15]andtheirpotentialfor
herbaceousmassmonitoringinSahelianrangelandshasrecentlybeenendorsedby[16]. Moststudies
however, rely exclusively on satellite-based vegetation indices (e.g., [13,17]), with only a minority
combiningtheseindiceswithrainfalldata,soilwaterstatusindicatorsandgroundplantmassdata.
RainfalldistributionisgenerallyconsideredasthemaindriverofplantgrowthintheWestAfrican
Sahel[18–21],althoughtherearemanyotherlocaldriversofplants’photosyntheticcapacityandspatial
variabilityintheSahel[22,23]. Amongthem, thesupplyofmineralnutrientssuchasnitrogen(N)
andphosphorus(P)aremajordeterminantsofplantgrowthrate[24]. IntheSahel,NandParethe
mainlimitingfactorstoplantproductionwhenrainfallissufficient. Forthisreason,[25]proposedis
thesubdividingoftheSahelbeltaccordingtothe250mmisohyet,withrainfallasthemainlimiting
factor below 250 mm and the nutrients N and P the main limiting factors above 250 mm annual
rainfall. ThewidetemporalvariabilityofNandPhowever,inrelationtothesoilmoistureregime
andtheabsenceofreliableinformationontheirspatialdistributionacrossSahelianregionsmakesit
difficulttoincludethisinformationinstatisticalmodelingapproaches. Incontrast,comprehensive
rainfall data has become increasingly accessible for use in the Sahel through the development of
satellite-basedproductssuchastheTropicalApplicationsofMeteorologyusingSATellite(TAMSAT)
data and ground-based observations [26], the Famine Early Warning System NETwork (FEWS
NET) Rainfall Estimate (RFE) [27] and the African Rainfall Climatology (ARC) [28]. The use of
rainfall data as the overall driver of plant growth is supported by the high variability of Sahelian
herbaceousproductivity,whichisdifficulttopredictintimeandspace[29,30]. Notallrainfallwater
however,isdirectlyavailabletoplants. Rainfallwateravailabilityismediatedbytheredistribution
(i.e.,run-off/run-on)onthesoilsurfaceinrelationtosoilphysicalcharacteristics(i.e.,structureand
texture),totopography[25]andtothephysicalnatureofthecanopy[31]. Thiscouldhelpexplain
thepoorrelationshipobservedbetweenherbaceousyieldandannualtotalrainfallintheSahelian
rangelandsofNigerandMali[32,33]. Itjustifiestheconceptofawaterrequirementindex(alsocalleda
watersatisfactionindexorawaterrequirementsatisfactionindex,WRSI)developedandimplemented
through a soil water balance model named the Crop Specific Water Balance (CSWB) by the Food
and Agricultural Organization (FAO) of the United Nations. With this model, the water balance
ofagivencropcanbecalculatedintimeincrements, usually10days(i.e., dekad), aspresentedin
Equation(1)[34]. Then,whenfilledwithcomplementaryparameters,theCSWBmodelproducesaset
RemoteSens.2016,8,668 3of23
ofwaterstatusindicatorsgenerallyusedtoassesstheeffectofweatherconditionsoncropdevelopment
andyield[35,36].
W =W +R−ETm−(r+i) (1)
d d–1
where,
W : amountofwaterstoredinthesoilattheendofthedekad(d)
d
W : amountofwaterstoredinthesoilattheendofthepreviousdekad(d–1)
d–1
R:cumulatedrainfallduringthedekad
ETm: maximumevapotranspirationinthedecadalperiod
r: representsthewaterlossesduetorunoffinthedecadalperiod
i: representsthewaterlossesduetodeeppercolationinthedecadalperiod
For its agricultural monitoring activities, FEWS NET developed a grid cell-based modeling
environment from the FAO’s CSWB [37]. The established geospatial model, GeoWRSI, was then
enhancedby[38]forWestAfrica’sSahelianrangelands[39]. Plantproductionmodelsestablished
solelyfromtheabsorbedPAR,asmentionedearlierandaccordingto[40],neglectthedirecteffects
ofotherfactorssuchaswateravailabilityandnutrientshortageonplantgrowth. Modelsincluding
bothsoilmoisture(i.e.,soilwaterstatusindicators)andthemetricsofFAPARseasonaldynamicsare
thereforeexpectedtoovercomeFAPAR-relatedproblems(e.g.,saturationandcloudcontamination)
and to improve herbaceous standing mass estimations. In such models, agrometeorological data
introduceinformationaboutsoilwateravailability,whereasFAPARmetricsprovideinformationon
herbaceousproductivityandspeciespatternsnottakenintoconsiderationbytheagrometeorological
component[41].
Inthiscontext,theoverallaimofourstudywastopredicttheherbaceousyieldacrossthearidto
sub-humidareasofSenegal. Theoriginalityofthestudywasthecombinationofsatellite-basedrainfall
estimates,agro-ecologicaldataandsatellite-derivedFAPARmetricsusingamachinelearningapproach.
Themorespecificobjectiveswereto:(i)developthreeherbaceousmodelsbasedonlyonFAPARmetrics,
onlyonagrometeorologicalvariablesandonthecombinedFAPARandagrometeorologicalvariables;
(ii)conductaspatio-temporalcomparisonofmodeloutputs;and(iii)analyzetherelationshipbetween
herbaceousyieldsandtheonset/endofseasonmetricscalculatedfromFAPARandsatellite-based
rainfalldataforearlywarningpurposes.
2. MaterialsandMethods
2.1. StudyArea
The study area covered 15 districts belonging to five administrative regions (i.e., Saint-Louis,
Matam,Kaffrine,LougaandTambacounda),withatotalareaof125,000km2(Figure1). Thearealies
intheSahelianandnorthernSudano-GuineanzoneofSenegalbetween16.69◦Nand12.63◦Nlatitude
and 16.74◦W and 11.86◦W longitude. It includes all natural grazing areas of the pastoral domain
as defined by [42], as well as some croplands, including fallows. The mean annual precipitation
varies between 200 and 980 mm from north to south, with reference to the FEWS NET rainfall
estimatesforthe2000–2015period[43,44]. Therainyseason,drivenbytheWestAfricanmonsoon,
is unimodal, occurring over 3–5 months between June and October. The studied area includes
different ecoregions, with a prevalence of red-brown sandy soils, ferruginous tropical sandy soils,
leptic, gley and vertic soils[45,46]. Typical of the Sahel, the herbaceous vegetation is particularly
dependentontheintra-seasonalrainfalldistribution[47]andisdominatedbyannualplantswithC4
photosynthesis[48]. AccordingtotheCentredeSuiviEcologique(CSE)[49,50],thenorthernzone
(≤300mmannualrainfall)ischaracterizedbyPoaceaesuchasChlorisprieurii,Aristidamutabilisand
Dactyloctenium aegyptium, as well as legumes such as Alysicarpus ovalifolius and Zornia glochidiata,
whereasinthecentralzone(between300and500mm)thecharacteristicspeciesareZorniaglochidiata
(Fabaceae) and Schoenfeldia gracilis, Pennisetum pedicellatum and Eragrostis tremula (all Poaceae).
Towardsthesouth, AndropogoneaespeciessuchasAndropogonpseudapricusandA.amplectansare
RemoteSens.2016,8,668 4of23
themostcommonspecies. Otherspecies,suchasSpermacocestachydea(Rubiaceae),Cassiaobtusifolia
Remote Sens. 2016, 8, 668 4 of 23
(Fabaceae)andFimbristylisexilis(Cyperaceae),occurirregularly,dependingontheterrainmorphology
(e.g., deFpimrebsrissitoylniss )exainlisd (hCuympearanceaane)d, olicvceusr toircrkegiunlaflrulye, ndceep.enTdhinegs tound itehde aterreraainc omntoarpinhsolnoginye (ela.gn.,d cover
depressions) and human and livestock influence. The studied area contains nine land cover classes
classesthat,whenexcludingcroppedlands,coincidewithwoodyplantdensity,whichincreasesfrom
that, when excluding cropped lands, coincide with woody plant density, which increases from north
northtosouth(Figure1,Table1). Thesenineclasseswereconstitutedbyaggregatingthe60classesof
to south (Figure 1, Table 1). These nine classes were constituted by aggregating the 60 classes of the
theoriginallandcoverdatabaseofSenegal[51]andmoreorlesscorrespondedwiththoseusedby[52]
original land cover database of Senegal [51] and more or less corresponded with those used by [52]
foranalfyozri anngallyaznindgc loanvde rcocvhearn cghaenigne Sine nSeengeaglabl beetwtweeeenn 11999900 aannd d2020050. 5.

Figure1F.igLuorcea 1t.i Looncaotfiotnh eofm thoen mitoonriitnorginsgit seisteas nanddt thhee mmaaiinn lalanndd cocvoevr eclracsslaess s[5e1s].[ T51h]e. sTithese asriet iedseanrtiefiiedde ntified
bytheirbyp othseitiri opnosiintioan ginr iad gwridh ewrheeCrex Cixn dinidciactaetess tthhee ccoolluummn nnunmubmerb aenrda nLyd cLoyrrecsoprornedssp toon tdhes ltionet heline
number with x and y varying between 1 and 9. The isohyets are based on average rainfall estimates
numberwithxandyvaryingbetween1and9.Theisohyetsarebasedonaveragerainfallestimates
provided by FEWS NET between 2000 and 2015 [44].
providedbyFEWSNETbetween2000and2015[44].
Table 1. General Descriptions of Land Cover Classes [53,54]. Woody Cover Values were obtained
Table1.frGomen tehrea lWDoeosdcyr iCpotivoenr sMoafpL aPnrodviCdeodv ebry C[1la5]s saensd[ 5C3o,r5r4es].poWnodo tdo ythCe oAvveerrVagaeldu eVsawlueesr eoof bPtiaxeinlse dfrom
theWooCdoyveCreodv beyr CMlaasspesP. rovidedby[15]andCorrespondtotheAveragedValuesofPixelsCovered
byClaLsasneds C.over Area Area Woody Number of
Abbreviation Short Description
Class (km²) (%) Cover (%) Sites
Open to closed herbaceous vegetation
LaHnedrbCacoevoeurs HER 38A,04re3 a 3A0r.5e3a Wo9o dy Nu1m3 ber
Class Abbreviation withS shpaorrsteD treeessc rainpdt isohnrubs (km2) (%) Cover(%) ofSites
Shrubs very
SVO Very open shrubs 33,724 27.07 17 8
open Opentoclosedherbaceousvegetation
Herbaceous HER 38,043 30.53 9 13
Shrubs open to withsparsetreesandshrubs
SOC Open to closed shrubs 12,155 9.75 28 2
closed
Shrubsvery
Treeosp veenry open SVTOVO Very oVpeerny torepees,n gsahllreuryb sforest 83838,97 24 277.1.03 7 2157 18
Trees open to
Shrucblsosoepde n SOTCOC OpenO top celnosteod ctlroeesse,d gaslhlerruyb fsorest 91227,41 55 79..4745 2258 20
toclosed Large to small tree plantations and
Agriculture AGR 19,483 15.64 14 0
Treesvery rainfed herbaceous crops
TVO Veryopentrees,galleryforest 8889 7.13 25 1
open Bare areas, urban areas and water
Other classes - 3041 2.44 - 0
bodies
Treesopen
Generic heTiOghCt classificOatpioenn:t ohecrlobsaecdeotruese s=, 0g.a0l3le rtoy f3o mres; tshrub9s2 =7 40.3 to7 5.4 m4 ; trees =2 53 to 30 m. 0
toclosed
Largetosmalltreeplantationsand
Agriculture AGR rainfedherbaceouscrops 19,483 15.64 14 0
Bareareas,urbanareasand
Otherclasses - 3041 2.44 - 0
waterbodies
Genericheightclassification:herbaceous=0.03to3m;shrubs=0.3to5m;trees=3to30m.
RemoteSens.2016,8,668 5of23
2.2. DataandProcessing
2.2.1. HistoricalFieldHerbaceousYields
Theinsituherbaceousmassdatausedinthisstudyforthecalibrationofthemodelswerecollected
annuallyfrom2000to2015(exceptfor2004)from24fieldsiteslocatedinfreeaccessnaturalrangelands,
as shown in Figure 1 (black dots). The sites were selected away from the main water points and
pastoralcampsinordertoavoidheavygrazing. Eachfieldsitecovereda3×3km2 homogeneous
area and represented the most common land cover classes. Above-ground herbaceous mass was
measured towards the end of the growing season, in early October. The technique used was the
stratifiedsamplinglineoriginallyproposedbytheInternationalLivestockCentreforAfrica(ILCA)
formonitoringpastoralecosystemsintheGourmaofMali[55]. Alonga1000mtransect,fourstrata
(baresoil, andlow, mediumandhighherbaceousmassproduction)wereidentified. Then, taking
into account the variability of herbage mass in the three covered strata, between 35 and 100 plots
(each1m2)weresampledrandomlyalongthetransectlineandfreshherbaceousmasswascutand
weighedwithineachplot. Afterre-sampling,threesamplesforeachstratum(i.e.,ninesamplesper
site)werekeptfordrying. Thedrymatterrateobtainedbydividingthedryherbaceousmassweight
bythefreshmassweight,aswellastherelativefrequencyalongthe1000mtransect,werethenused
tocalculatetheherbaceousmassyieldinkg·DM/ha,firstforeachofthethreestrataandthenforthe
sitebyaddingthemtogether. Notethatthebiomassdatawerenotregularlycollectedinallmonitoring
sitesduetooccasionallackoflogisticsortheearlypassageofbushfiresbeforefieldmeasures[16].
Formoredetailedinformationontheoverallherbaceousmasscollection,see[16].
2.2.2. FAPARVegetationDynamicsandCalculatedMetrics
GEOV1CopernicusGlobalLandFAPARrunsfrom24December1998tothepresentdayata
groundsamplingdistanceof1/112◦ (about1kmattheEquator)and10-daysteps[56]. Derivedfrom
theSPOT-VEGETATION(from2000to2013)andProba-V(for2014and2015)instruments,thisproduct
isfreelyavailableonhttp://land.copernicus.eu/global. Fordetailedinformationontheprinciples
usedtoestimatetheGEOV1FAPARproduct,see[10].
In order to calculate seasonal metrics, the GEOV1 FAPAR time series were filtered using the
Savitzky-Golay(SG)fittingmethodavailableviaTIMESATsoftware[16,57]. Filteringisessentialin
semi-aridareassuchastheSahel,wheremanyfactors(e.g.,clouds,aerosols,shadows,surfacewater)
tendtoproducenoisyanderroneousFAPARvalues[58,59]. FromthefilteredFAPARtimeseries,eight
metricswereretrievedwithTIMESATandusedinthisstudy(Figure2): thestartoftheplantgrowth
season(SOS);theendoftheseason(EOS);thelengthofgrowingseason(LOS);themaximumFAPAR
valueovertheseason(PEAK);theamplitude(AMPL)correspondingtothedifferencebetweenPEAK
andtheaveragedleftandrightminimumvaluesovertheongoingannualcycle(BVAL);thesmall
integratedFAPAR(SINT)fromSOStoEOSandaboveBVAL;thesmallintegratedFAPARfromSOSto
PEAKtimeandaboveBVAL(GSINT);andthedecreasingrateduringthesenescencephase(RDERIV).
Allthesemetricshavebeendescribedby[15,16],exceptforGSINT,whichwascomputedtoinclude
information during the green-up phase. SOS and EOS are essential for calculating the cumulated
FAPARmetricsandweresettooccurat20%and50%oftheseasonalAMPLbeforeandafterthepeak
value,respectively[16]. Allthemetricswerederivedforeachyearbetween2000and2015onapixel
basisand1kmspatialresolution. Theannualvalueswerethenaveragedforeachsiteoveranareaof
about3×3km2tomatch,asfaraspossible,thespatialsamplingofthegrounddata.
ReRmeomteotSee nSse.n2s.0 21061,68,, 86,6 6868 6o6f o2f3 23

Figure2. SeasonalFAPARmetricsconsideredinthisstudyandshownforasinglepixel. Thebase
Figure 2. Seasonal FAPAR metrics considered in this study and shown for a single pixel. The base
value(BVAL)representstheaveragedminimumvaluesovertheannualcycle(i.e.,beforeandafterthe
value (BVAL) represents the averaged minimum values over the annual cycle (i.e., before and after
growingseason).
the growing season).
2.22..23..3O. ObtbatianiinnigngA AgrgormometeetoeroorloolgoigcaiclaDl aDtaata
AAgrgormometeetoeroorloolgoigciacladl adtaataw wasacsa clacluclualtaetdedu suinsigngth teheG GeoeWoWRSRISmI modoedleol foFf EFWEWSSN NETETa daadpatpetdedto toa a
grgidridce clle-lbl-absaesdemd omdoedlienlginegn venirvoinrmonemnte[n3t7 []3f7r]o mfrotmhe tohrei goinriaglinwaal twerabtearla bnacleaanlcgeo ariltghomritohfmFA oOf F[3A5O]. A[3s5].
noAtesd nboyte[d6 0b]y,t h[6e0G], etohWe GRSeIoWmoRdSeI lmwoadseeln whaans ceendhaanndceedx taenndd eedxtinentodeadn oinpteor aatnio onpalermatoidoneaflo rmAofdreic afo,r
CeAnftrriacal,A Cmenetrriacal AanmderAicfag hanandi Astfagnhaafntiesrtathn eafwteorr tkheb yw[o6r1k] .bTy h[6e1W]. TRhSeI iWndRiScIa tinordi(cEaqtoura t(iEoqnu(a2t)io)nis (t2h))e is
ratthioe oraftsieoa osof nsaelasaoctnuaall accrtoupale vcraoppo tervaanpspoitrraatniospni(rAatEioTnc) (tAoEthTec)s etoa stohnea slecarsoopnwala cterorpre wquaitreerm reeqnut,iwrehmicehnt,
isweqhuicihv ailse netqtuoivpaolteenntt itaol cprootpenetviaalp ocrtroapn sepviarpatoitornan(PspEiTrac)ti[o6n0 ](.PAEETTcc) r[e6p0]r.e sAeEntTsct hreeparcetsueanltas mthoeu natctoufal
waamterouwnitth odfr awwantefrro mwitthhedrsaowilnw afrteormre stherev osoirilb ywvaetegre traetisoenrvtorairn sbpyir avteiognetaantidonso tirlaenvsappiorraatitoionn a[6n0d,6 2so]il
anedvaPpEoTrcatisiotnh e[6c0u,6m2u] laantidv ePEopTtci mis uthme ccruompuwlaattievrer eoqputiimreummen ctraospm weattbery rreaqinufiarellmaenndt saosi lmmeot ibsytu rraeinfofrall
aagnivde nsoailc cmuomisutularteio fnorp ae rgioivde[n3 9a]c.cFuomlluolwatiinogn [p6e0r]i,otdh e[3P9E].T cFoaltloawgiinvgen [6t0im], ethine PthEeTgcr aotw ai nggivseena stoimnei sin
catlhceu lgartoewdibnyg mseualstoipnl yisi ncgaltchuelapteodte bnyti amluevltaipploytirnagn sthpeir patoiotennt(iPaEl Tev)abpyotthraencsrpoipractoioenffi (cPieEnTt) (bKyc )t,haes cirnop
Eqcoueaftfiiocnie(n3t) .(KTch)e, aAsE inTc Eiqsudaettieornm (i3n).e dThbey AaEsTetc oisf fduentecrtmioninseidn tbeyg raa stient gofr afiunnfcatliloanms oinutnetg(rPatPinTg), rpaliannfatll
avaamiloabulnet w(PaPteTr)(, PpAlWan)t, carvitaiiclaalbsleo ilwwaatetre r(lPeAveWl()S, WcrCiti)c,aslo islowila wteratheor ldleivnegl c(aSpWacCit)y, (sWoilH wCa),tecrro hporlodointg
decpapthac(RityD ()WanHdCs)o, iclrwopa treorocto dnetpentht ((RSWD)) aantdth seoeiln wdaotfert hceonsttuendty (SpWer)io adt .thSeW encdan obf ethees stitmudayte pdeurisoindg. SaW
socialnw baete ersbtiamlaantceed [u6s0i,n63g] aw siothil Ewqauteart iboanlsan(4c)e. [60,63] with Equations (4).
∑A∑ETAcETc
WWRSRIS=I= (cid:3400)10×01 00 (2()2)
∑P∑ETPcETc
PEPTEcT=c=KcK(cid:3400)cP×ETP ET (3()3)
SWSW=i =SWSWi−+1P+PTPP−TiA−ETAcE Tci (4()4)
(cid:2919) (cid:2919)(cid:2879)(cid:2869) (cid:2919) (cid:2919)
whereicorrespondstothetimestep.
where i corresponds to the time step.
For more information on the development and parameterization of the GeoWRSI model,
For more information on the development and parameterization of the GeoWRSI model, see
see[60,61]. The main parameters needed to run the model are PPT, PET, WHC and Kc values.
[60,61]. The main parameters needed to run the model are PPT, PET, WHC and Kc values. We ran
WeranthemodelinthisstudyusingtheAfrica-wideblendedsatellite-gaugerainfallestimateimages
the model in this study using the Africa-wide blended satellite-gauge rainfall estimate images
implementedbytheClimatePredictionCenter(CPC)ofNOAA[43,44]andtheglobalPETimages
implemented by the Climate Prediction Center (CPC) of NOAA [43,44] and the global PET images
estimated from the 6-hourly numerical meteorological model output using the Penman-Monteith
estimated from the 6-hourly numerical meteorological model output using the Penman-Monteith
equation[64].RainfallandPETimagesweredownloadedindekadaltimestepswitha0.1degree(about
equation [64]. Rainfall and PET images were downloaded in dekadal time steps with a 0.1 degree
10km)and1degree(about110km)spatialresolutionrespectively,fromtheftpserveroftheClimate
(about 10 km) and 1 degree (about 110 km) spatial resolution respectively, from the ftp server of the
Climate Hazard Group for the archive data between 2000 and 2010 [65] and the FEWS NET server
Remote Sens. 2016, 8, 668 7 of 23
for the latest data between 2011 and 2015 [66]. The WHC image shows the spatial variation of easily
available water capacity in the upper 100 cm, based on soil physical characteristics [37] and was
obtained from the FAO digital soil map of the world [67] with a 0.1 degree spatial resolution. Kc
values correspond to those proposed by [68] for the extensive grazing pastures in semi-arid climates
(Figure 3). These values were chosen with the assumption that the study area is uniform in hydro-
climatic conditions and after a visual analysis of WRSI variable which showed the most reasonable
distribution for a median year regarding rainfall conditions.
The rainy season onset date (SOSp) was calculated for each modeling grid cell using the criteria
of at least 25 mm of rainfall received over a given dekad, followed by a total of 20 mm for the next
two dekads as applied by [37] after [69]. The end-of-season date (EOSp) occurred when a
climatological ratio between rainfall and potential evapotranspiration (i.e., PPT < PET/2) was
observed [60]. SOSp and EOSp were in turn used to calculate the herbaceous vegetation WRSI. In
RaedmdotietSioenns .t2o0 16W,8R,6S6I8, SOSp and EOSp, we calculated 12 other agrometeorological variables fo7ro fth23e
growing periods of each year (Table A1), particularly actual evapotranspiration (AET), water deficit
(WDEF) and water surplus (WSUR) that accumulated during each of the four herbaceous vegetation
HazardGroupforthearchivedatabetween2000and2010[65]andtheFEWSNETserverforthelatest
growth phases (illustrated in Figure 3): initial (i); vegetative (v); flowering (f); and ripening (r). The
databetween2011and2015[66]. TheWHCimageshowsthespatialvariationofeasilyavailablewater
seasonal fractions assigned to these four phases were 23%, 39%, 23% and 15% respectively, (numbers
capacityintheupper100cm,basedonsoilphysicalcharacteristics[37]andwasobtainedfromthe
taken from the Sahelian Transpiration, Evaporation, and Productivity [STEP] model) [40]. The
FAOdigitalsoilmapoftheworld[67]witha0.1degreespatialresolution. Kcvaluescorrespondto
computed rainfall variables corresponded to the seasonal amount accumulated from the start to the
thoseproposedby[68]fortheextensivegrazingpasturesinsemi-aridclimates(Figure3). Thesevalues
end of the rainy season (PPTc) and the mean rainfall from the start to the end of the season (PPTm).
werechosenwiththeassumptionthatthestudyareaisuniforminhydro-climaticconditionsandafter
All the agrometeorological images were re-sampled to a 1 km resolution in order to match the FAPAR
avisualanalysisofWRSIvariablewhichshowedthemostreasonabledistributionforamedianyear
metrics using a bilinear interpolation method.
regardingrainfallconditions.

Figure3. OverallcropcoefficientcurveforSenegal’sSahelianrangelandsduringagrowingseason
Figure 3. Overall crop coefficient curve for Senegal’s Sahelian rangelands during a growing season of
of90days.Thegrowingperiodwasdividedintofourphases:initial(i);vegetative(v);flowering(f);
90 days. The growing period was divided into four phases: initial (i); vegetative (v); flowering (f);
ripening(r). SOSpindicatesthemeandateoftheonsetoftherainyseasoninthe2000-2015period.
ripening (r). SOSp indicates the mean date of the onset of the rainy season in the 2000-2015 period.
Numbersinbracketsindicatethetotaldaysofthephase.
Numbers in brackets indicate the total days of the phase.
2.3. MTheethroadisn yseasononsetdate(SOSp)wascalculatedforeachmodelinggridcellusingthecriteria
ofatleast25mmofrainfallreceivedoveragivendekad,followedbyatotalof20mmforthenexttwo
The overall approach applied for elaborating the estimation models of herbaceous yield is
dekadsasappliedby[37]after[69]. Theend-of-seasondate(EOSp)occurredwhenaclimatological
presented in Figure 4.
ratiobetweenrainfallandpotentialevapotranspiration(i.e.,PPT<PET/2)wasobserved[60]. SOSp
andEOSpwereinturnusedtocalculatetheherbaceousvegetationWRSI.InadditiontoWRSI,SOSp
andEOSp,wecalculated12otheragrometeorologicalvariablesforthegrowingperiodsofeachyear
( Table A1), particularly actual evapotranspiration (AET), water deficit (WDEF) and water surplus
(WSUR)thataccumulatedduringeachofthefourherbaceousvegetationgrowthphases(illustrated
inFigure3): initial(i);vegetative(v);flowering(f);andripening(r). Theseasonalfractionsassigned
tothesefourphaseswere23%, 39%, 23%and15%respectively, (numberstakenfromtheSahelian
Transpiration,Evaporation,andProductivity[STEP]model)[40]. Thecomputedrainfallvariables
correspondedtotheseasonalamountaccumulatedfromthestarttotheendoftherainyseason(PPTc)
and the mean rainfall from the start to the end of the season (PPTm). All the agrometeorological
imageswerere-sampledtoa1kmresolutioninordertomatchtheFAPARmetricsusingabilinear
interpolationmethod.
2.3. Methods
The overall approach applied for elaborating the estimation models of herbaceous yield is
presentedinFigure4.
RemoteSens.2016,8,668 8of23
Remote Sens. 2016, 8, 668 8 of 23

Figure4.Workflowforthedevelopmentoftherule-basedpiecewiseregression(i.e.,Cubist)models
Figure 4. Workflow for the development of the rule-based piecewise regression (i.e., Cubist) models
forherbaceousyieldestimation.
for herbaceous yield estimation.
22..33..11.. EExxppllaannaattoorryy VVaarriiaabbllee SSeelleeccttiioonn ffoorr HHeerrbbaacceeoouuss MMaassss EEssttiimmaattiioonn
OOvveerr--fifittttiinnggi issa am maajojorrd drraawwbbaacckko offm maachchinineel elaeranrnininggm metehtohdosds[7 [07]0a] nadndc acnano coccucruwr whehnenth tehreeries ias
sat rsotrnognrge rlaetlaiotinosnhsihpipb ebtewtweeenenth tehee xepxlpalnaantaotroyryv varairaibablelse.s.I nIno orrddeerrt otoa avvooiiddo ovveerr--fifittttiinngg,, oonnllyy rreelleevvaanntt
vvaarriiaabblleess wwiitthh nnoo ssiiggnniifificcaanntt ccoolllliinneeaarriittyy sshhoouulldd bbee kkeepptt iinn aa mmooddeell.. RReedduunnddaanntt pprreeddiiccttoorrss ccaann ttrriiggggeerr
ssuubbssttaannttiiaall iinnssttaabbiilliittyy iinn aa mmooddeell’’ss ccooeefffificciieennttss.. WWrraappppeerr mmeetthhooddss ((ee..gg..,, bbaacckkwwaarrdd aanndd ffoorrwwaarrdd)) aarree
wwiiddeellyy uusseedd iinn tthhee sseeaarrcchh pprroocceedduurree ffoorr pprreeddiiccttoorrss’’ sseelleeccttiioonn iinn eennvviirroonnmmeennttaall ssttuuddiieess ((ee..gg..,, [[7711––7733]]))..
AAss rreeppoorrtteedd bbyy [[7744]] aanndd [[7755]] hhoowweevveerr,, tthhee pprriinncciippllee ooff tthheessee mmeetthhooddss lliieess iinn rreeppeeaatteedd hhyyppootthheessiiss tteessttss
wwiitthh tthhee ssaammee ddaattaa,, wwhhiicchh iinnvvaalliiddaatteess mmaannyy ooff tthheeiirr ssttaattiissttiiccaall pprrooppeerrttiieess.. IInn oorrddeerr ttoo oovveerrccoommee tthhiiss
ddrraawwbbaacckk,, [[7766]] pprrooppoosseedd tthhee rreeccuurrssiivvee ffeeaattuurree eelliimmiinnaattiioonn pprroocceedduurree.. TThhiiss mmeetthhoodd iiss aa sseeqquueennttiiaall
bbaacckkwwaarrdd sseelleeccttiioonn aapppprrooaacchh [[7777]] tthhaatt aavvooiiddss rreefifittttiinngg mmaannyy mmooddeellss aatt eeaacchh sstteepp ooff tthhee sseeaarrcchh bbyy
iinncclluuddiinngg aann iimmppoorrttaannccee--rraannkkiinngg ccrriitteerriioonn iinnsstteeaadd [[7755]].. TThhee rreeccuurrssiivvee ffeeaattuurree eelliimmiinnaattiioonn aallggoorriitthhmm
aavvaaiillaabbllee wwiitthh RR ssooffttwwaarree [[7788]] wwaass uusseedd ttoo iiddeennttiiffyy lleessss ppeerrttiinneenntt vvaarriiaabblleess ffrroomm tthhee oorriiggiinnaall ddaattaasseett..
IInn ththisisa laglogroirthitmhm,t,h ethrea nrdaonmdofmor efsotrfeusnt cftuionnct[i7o9n] w[7a9s] uwseads tuosceodm tpou tceotmhepuimtep othrtea nimcepoofrtthaencvea roiaf btlhese
(vi.aer.i,ambleeas n(i.dee.,c mreeaasne dinecarcecausera icny awcchuernacay wvahreianb ale viasripaebrlem ius tpeedr)mauntdedr)a nankdt hraenmk othveemr souvbesre stsu.bsTehtse.
pTrhoec epdruorcee’dsuoruet’ps uotuwtpituht vwaritihab vleasriraabnlkese drafnrokmedm froosmtt omleoasstt tiom lpeaosrtt ainmtpwoarstatnhte nweavsa tlhueante edvianluoardteedr tion
eolridmeirn atote eilnimteirnraetlae tiendteerxrpellaatneadt oerxypvlaanraiatobrleys v[1a5ri]a,bulseisn [g1t5h],e uVsainriga nthcee IVnaflraiatinocne FIancftlaotrio(Vn IFFa)citnodri c(VatIoFr)
(iEnqduicaattioorn ((E5q))u.aTtihoenl e(5a)s)t. iTmhpeo lretaasnt tivmaprioarbtalenst wvaitrhiaablVesIF w≥ith1 0a[ V80I]Fw ≥ e1r0e e[8li0m] iwneartee deloimneinbayteodn eo.nTeh biys
pornoec.e Tsshiws apsrorecpesesa twedasu rnetpileathteedV uIFntfiol rthaell VreImF faoinr ianlgl rveamriaaibnliensgw vaasribaeblloews w1a0s. below 10.
VVIIFF==11/⁄((cid:4666)11−−RR2²jj)(cid:4667) (5(5) )
where R²j is the correlation coefficient for the regression of each variable with the remaining ones.
whereR2jisthecorrelationcoefficientfortheregressionofeachvariablewiththeremainingones.
2.3.2. Rule-Based Regression Tree and Model Building
2.3.2. Rule-BasedRegressionTreeandModelBuilding
The classification and regression trees (CART) approach, also called decision trees, is a data
The classification and regression trees (CART) approach, also called decision trees, is a data
mining method used to identify patterns between a dependent variable and several independent
mining method used to identify patterns between a dependent variable and several independent
predictor variables [81]. CART models can be used for either classification or regression, where
predictor variables [81]. CART models can be used for either classification or regression, where
classification trees predict classes and regression trees predict a continuous response [79,82,83]. We
RemoteSens.2016,8,668 9of23
classificationtreespredictclassesandregressiontreespredictacontinuousresponse[79,82,83]. We
usedtheCubistprogram[78],providingrule-basedmodelsusingregressiontreesbecausethemodeled
herbaceousmasswascontinuous. Eachtreewasreducedtoasetofconditionalrulesretrievedfrom
themostimportantpredictorvariables[84]. Theserulespartitionedtheindependentvariables(FAPAR
metricsand/oragrometeorologicalproducts)intosmallergroupsandeachofwhichwaslinkedtoa
multiplelinearregressionmodelthatpredictedthedependentvariable(herbaceousmass)[75]. This
algorithm,basedonif/thenrules,iswellsuitedforSahelianecosystems,whicharecharacterizedby
highspatialheterogeneityintermsofsoiltypeandfertility,rainfallmediatedbyrun-off/run-onand
speciescomposition. Theinitialdatasetwasrandomlyseparatedintoa‘trainingset’anda‘verification
set’containing70%and30%ofthedatarespectively. Thenasimplecheckwasmadetoensurethat
all land cover classes are represented in the two sub-datasets. Model learning was done with the
trainingset,usingaboosting-likeschemecalled‘committees’[84],whereeachcommitteemember
correspondstooneregressiontree. Thenumberofcommitteeswastunedbyapplyingthecommonly
used10-foldcross-validationinthemodel’sperformanceestimation[85,86]. Allinstancesoftheinitial
datasetwereusedandthecross-validatedrootmeansquarederror(RMSE)appliedinordertoobtain
thebestvalueofthecommitteenumber. Itshouldbenotedthatforeachcommitteeafterlearning,
asubsequentmember(multiplelinearregressionmodel)adjusts,forinaccuracies,intheprediction
of the previous one and the final predicted value is a simple average of all predictions from the
variousmembers[75,81,84]. Forcomparativepurposes,threecubistmodelswereestablished: (i)the
VegetationIndex-model(VI-model)includingonlyFAPARmetrics;(ii)theAgrometeorological-model
(AGRO-model)computedwithagrometeorologicalvariables;and(iii)theVIAGRO-modelincluding
bothFAPARandagrometeorologicaldata.
2.3.3. ModelVerification,ErrorAnalysisandYieldAnomalyComputation
Modelaccuracyverificationwasperformedusinganindependentsetofsamplesthatwerenot
used(i.e.,hold-out)forthetrainingofthemodel.TheCubistmodelsweretrainedusingthetrainingset
(207samples)andthenusedtoestimatetheherbaceousyieldvaluesoftheverificationset(90samples).
The validation accuracy was retrieved using the observed and predicted herbaceous yield of the
verificationset. ThequalityofthemodelswasassessedbytheRMSEandmeanabsoluteerror(MAE).
Theanomalyvalueswerecalculatedpixel-wisebytheratio(inpercentage)ofthedifferencebetweenthe
actualandlong-termaverageoftheherbaceousyield(orrainfall)andthe15-yearlong-termaverage.
3. Results
3.1. VariableSelectionandModelDevelopment
After removing all the interrelated variables, a final dataset of 15 explanatory variables
remained and were used for model development (Figure 5c). Three models were developed for
estimatingherbaceousyieldsusing: (a)onlyFAPARmetrics(VI-model);(b)agrometeorologicaldata
(AGRO-model);and(c)bothFAPARmetricsandagrometeorologicaldata(VIAGRO-model). Theset
ofvariablesusedforeachmodelisshowninFigure5andthemodelperformanceisshowninFigure6.
Thecontributionoftheinputvariablesvariedwitheachmodel. FortheVI-model, thePEAK
variable was the most important, followed by the right derivative (RDERIV) and EOS (Figure 5a).
GSINTandSOSweretheleastpertinentvariablesasneitherwasusedinmodelconditionsorequations.
IntheAGRO-model,theSOSpandwaterdeficitduringripening(WDEFr)werethemostimportant
variables, whereas the least important were water deficit (WDEFv) and water surplus (WSURv)
duringthegrowthstage. ThemostimportantvariableintheVIAGRO-modelwasPEAK,followed
byRDERIV.TheSOShadlittleornoimportanceintheVI-modelandVIAGRO-model, unlikethe
SOSp, which played a major role in AGRO-model and VIAGRO-model. The validation statistics
ofthethreemodelsaregiveninFigure6. TheVIAGRO-modelshowedthebestperformancewith
R2=0.69,RMSE=483kg·DM/ha,MAE=355kg·DM/haandslope=0.66. TheVI-modelperformed
Remote Sens. 2016, 8, 668 10 of 23
RReemmoottee SSeennss.. 22001166,, 88,, 666688 1100 ooff 2233
performed less well with R² = 0.63; RMSE = 550 kg·DM/ha; MAE =388 kg·DM/ha; slope = 0.56,
performed less well with R² = 0.63; RMSE = 550 kg·DM/ha; MAE =388 kg·DM/ha; slope = 0.56,
lfeoslslowweelldw biyth thRe2 A=G0.R6O3;-RmModSEel,= R5²5 =0 0k.g55·D; RMM/ShEa; =M 5A85E k=g3·D88Mk/gh·aD; MM/AhEa ;=s l4o3p2e k=g·0D.5M6,/hfoal;l oslwopede =b y0.t5h1e.
followed by the AGRO-model, R² = 0.55; RMSE = 585 kg·DM/ha; MAE = 432 kg·DM/ha; slope = 0.51.
AGRO-model,R2=0.55;RMSE=585kg·DM/ha;MAE=432kg·DM/ha;slope=0.51.

Figure 5. Importance of the predictor variables for the three herbaceous yield estimation models: (a)
Figure 5. Importance of the predictor variables for the three herbaceous yield estimation models:
FVigIu-mreo d5.e Ilm; (pbo) rAtaGncReO o-fm thode eplr; e(dc)i cVtoIrA vGaRriOab-mleso dfoerl . thSien tghlree ev ahreiarbbalec eiomups oyritealndc ees itnimitiaatliloyn gmivoedne lass: (tah)e
(a)VI-model;(b)AGRO-model;(c)VIAGRO-model.Singlevariableimportanceinitiallygivenasthe
VmI-emanods eolf; t(hbe) pAerGcRenOta-mgeo odfe ul; s(ec )in V mIAodGeRl Oco-nmdoidtieoln. sS ainngdl ee qvuaartiiaobnles wimerpeo trhtaennc neo irnmitaialilzlyed g tiov esnu mas 1 0th0e%
meansofthepercentageofuseinmodelconditionsandequationswerethennormalizedtosum100%
mfoera neas cohf tmhoe dpeelr. centage of use in model conditions and equations were then normalized to sum 100%
foreachmodel.
for each model.

FFiigguurree6 6..A Accccuurraaccyya asssseessssmmeenntto offt htheed deevveeloloppeeddC Cuubbisisttm mooddeelsls::r reelalattiioonnsshhiipp bbeettwweeeenno obbsseerrvveedda anndd
Fppirgreeuddriceict et6ed. dAh hceecrurbbraaaccceeyoo uaussssy eyiseiseldmldfe ofnortr; ;o( a(fa) t)th htehe edV eVIv-Ime-mlooopddeeedl;l ;(C b(bu))bt hitshete Am AGoGdRReOlOs-:m- mroeoldadeteli;ol;(n c(sc)h)t ihtphe ebV eVItAIwAGeGeRnRO Oo-mb-msoeodrdveeel.dl. and
predicted herbaceous yield for; (a) the VI-model; (b) the AGRO-model; (c) the VIAGRO-model.
33.2.2.. SSppaattiioo--TTeemmppoorraallC Coommppaarrisisoonno offt htheeM Mooddeelsls’’O Ouutptpuutt
3.2. Spatio-Temporal Comparison of the Models’ Output
AAllll tthhee eessttaabblliisshheedd mmooddeellss pprroovviiddeeddh heerrbbaacceeoouuss yyiieelldd eessttiimmaattiioonnss wwiitthh vvaalluueess iinnccrreeaassiinngga alloonngg
aa nnoorArtthlhl- -stshooeuu tethsht gagrbralaiddsihieeenndtt miinno tdthheeels 22 0p00r00o0–v–2i2d001e1d55 phpeeerrribiooadcde (o(FFuiigsgu uyrireeel 7d7) ).e. sTTthihmeea VVtiIIo--mnmsoo wddeietllh hh vooawwlueeevvsee rir,n, gcgraaevvaeesi gngegenn aeelroraanlllglyy
allo onwwoerertrhe e-sssttoiimumtahatti iogonrnassdt thiheanannt t ithnhe ett htwwe oo2o 0ot0th0he–er2rm0 m1o5od dpeeelslrs.i.o PPdaa rr(tFtiicicuguullaarrery y7ee)a.a rrTss,h,s esuu VcchhI-a masso 22d000e02l2 h aaonnwdde 22v00e11r44, wwgahhveeenn g tthehenerereer awwllaayss
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aVV cIIAoAnGGsRiRdOOer--mamboloedd edelle..f TTichhitee i ynye ehaarerr 22b00a11c00e ohhuoosww meevvaeesrsr,, pwwraoasds cuchhcatairroaancct,t eewrriiezzreeedd r bebfyyle aac thheidigg hhw hehelelr rbibnaa cbceeoootuhus st hmmeaa VssssI -ppmrrooodddueulcc tatiinoodnn, ,
VwwIhAhiicGchhRw Owa-amssc oclledeaearlr.i iTnnht theh eyeA eAaGrG R2R0OO1-0-a ahnnoddwV VeIvIAAeGrG, RwROOa-s-m mcohodadreeallc eetsestrtiiimzmeaadtti iobonyns sa b bhuuitgt lhlee shssse srsoboa iincne tothhuees V VmII--ammssoo dpdereoll..d OOuvcvteeiroraanlll,l, ,
wtthhheeicV VhI -Iwm-maosod dcelelelua unr ndinde retehrseets iAmtimGatRaetOded-h aihgnihdg vhVa vIlAuaelGus,ReesOs, p-emescpoieadcleliyal leilnsyt itimhne atmthioeo nrmes obsorueut tslheoesurstn hsoree rignni otrhneges; iVothnI-isms; dtohrdaiesw l.db Oraacvwkerbwaalacls,k
the VI-model underestimated high values, especially in the more southern regions; this drawback
cworarse cctoerdreicntetdhe inV tIhAeG VRIOA-GmRoOd-eml.odel.
was corrected in the VIAGRO-model.