Remo e Sens. 2015, 7, 14458-14481; doi:10.3390/ s71114458
emo e sensing
ISSN 2072-4292
www.mdpi.com/jou nal/ emo esensing
A icle
Using RPAS Mul i-Spec al Image y o Cha ac e ise Vigou ,
Lea De elopmen , Yield Componen s and Be y Composi ion
Va iabili y wi hin a Vineya d
Cla a Rey-Ca amés 1, Ma ía P. Diago 1, M. Pila Ma ín 2, Agus ín Lobo 3
and Ja ie Ta daguila 1,*
1 Ins i u o de Ciencias de la Vid y del Vino, Uni e si y o La Rioja, Finca La G aje a, Ca e e a de
Bu gos Km 6, Log oño 26007, Spain; E-Mails: [email p o ec ed] (C.R.-C.);
[email p o ec ed] (M.P.D.)
2 Ins i u o de Economía, Geog a ía y Demog a ía. Cen o de Ciencias Humanas y Sociales, CSIC,
Albasanz 26–28, Mad id 28037, Spain; E-Mail: [email p o ec ed]
3 Ins i u e o Ea h Sciences Jaume Alme a, ICTJA-CSIC, Lluis Sole Saba is s/n, Ba celona 08028,
Spain; E-Mail: [email p o ec ed]
* Au ho o whom co espondence should be add essed; E-Mail: [email p o ec ed];
Tel.: +34-941-894-980.
Academic Edi o s: Mu lu Ozdogan, Yoshio Inoue and P asad S. Thenkabail
Recei ed: 1 Sep embe 2015 / Accep ed: 26 Oc obe 2015 / Published: 30 Oc obe 2015
Abs ac : Implemen a ion o p ecision i icul u e echniques equi es he use o eme ging
sensing echnologies o assess he ineya d spa ial a iabili y. This wo k shows he
capabili y o mul ispec al image y acqui ed om a emo ely pilo ed ae ial sys em (RPAS),
and he de i ed spec al indices o assess he ege a i e, p oduc i e, and be y composi ion
spa ial a iabili y wi hin a ineya d (Vi is ini e a L.). Mul i-spec al image y o 17 cm
spa ial esolu ion was acqui ed using a RPAS. Classical ege a ion spec al indices and wo
newly de ined no malised indices, NVI1 = (R802 − R531)/(R802 + R531) and
NVI2 = (R802 − R570)/(R802 + R570), we e compu ed. Thei spa ial dis ibu ion and
ela ionships wi h g ape ine ege a i e, yield, and be y composi ion pa ame e s we e
s udied. Mos o he spec al indices and ield da a a ied spa ially wi hin he ineya d, as
showed h ough he a iog am pa ame e s. While he co ela ions we e signi ican bu
mode a e among he spec al indices and he ield a iables, he kappa index showed ha he
spa ial pa e n o he spec al indices ag eed wi h ha o he ege a i e a iables
(0.38–0.70) and mean clus e weigh (0.40). These esul s p o ed he u ili y o he
OPEN ACCESS
Remo e Sens. 2015, 7 14459
mul i-spec al image y acqui ed om a RPAS o delinea e homogeneous zones wi hin he
ineya d, allowing he g apeg owe o ca y ou a speci ic managemen o each suba ea.
Keywo ds: p ecision i icul u e; emo e sensing; emo ely pilo ed ae ial sys em; spec al
indices; kappa index
1. In oduc ion
Mode n and sus ainable i icul u e equi es objec i e and con inuous moni o ing o key pa ame e s o
a ional and di e en ia ed ag onomic managemen o ineya ds based on he spa io- empo al a iabili y
o g ow h, yield, and g ape composi ion wi hin he plo . The di e en ial managemen wi hin he ineya d
and he concep o being spa ially a iable a e co e ed by a discipline known as p ecision i icul u e. This
new app oach allows he g ape-g owe o op imize c op p oduc ion and p o i abili y by educing inpu s
such as machine y, labou , chemicals, wa e , ene gy, e c. I also con ibu es o diminishing he
en i onmen al impac o pes icides, nu ien s leaching, and ossil uels, among o he s [1,2].
One o he ools ha has demons a ed i s capabili y o help in c op managemen , and mo e
speci ically, in s udying he wi hin- ield a iabili y o assess spa ial and empo al changes in soil
mois u e, canopy g ow h, and plan wa e s a us, is emo e sensing. Se e al wo ks ha e shown he
sui abili y o emo e sensing o p ecision i icul u e pu poses du ing he las wo decades [3]. Hence,
his echnique has been used o app aise he ineya d spa ial a iabili y o wa e s a us [4,5], chlo ophyll
and ca o enoid con en [6,7], ineya d canopy s uc u e [8,9], g ape colou and phenols con en [10,11],
as well as g ape quali y [6,12]. Remo e sensing in ol es he acquisi ion o spec al da a om se e al
pla o ms, such as sa elli es, ai c a s, and emo ely pilo ed ae ial sys ems (RPAS).
Vineya ds a e no a con inuous c op, unlike ce eals. Thus, spec al mixing be ween indi idual ines
and ow soil backg ound is one o he main d awbacks o ope a ional applica ion o emo e sensing da a
o ineya d moni o ing [13]. E en o e y high- esolu ion space bo ne senso s such as Pleiades
Cons ella ion (h p://smsc.cnes. /PLEIADES/), he highes spa ial esolu ion a ailable is 50 cm/pixel,
which could s ill be oo coa se o moni o some common ine aining sys ems whe e c op canopies a e
e y na ow (i.e., 40–60 cm in he case o e ical shoo posi ioned ineya ds), and a mixed signal o
lea es, shoo s, shadow, and soil is commonly ound in mos pixels. Senso s on boa d manned ai bo ne
ehicles allow hese limi a ions o be o e come by acqui ing image y o inc eased spec al, spa ial, and
empo al esolu ion compa ed o hose moun ed on sa elli es, al hough hei ope a ional cos s a e e y
high. RPAS is an in e es ing al e na i e in p ecision i icul u e o o he pla o ms such as helicop e s,
ai bo ne, and sa elli e, ega ding mainly he spa ial esolu ion and he cos o acquisi ion [14]. RPAS
allow o he acquisi ion o image y o e y high spa ial esolu ion, o en a a sub-decime e esolu ion, a
much lowe cos han adi ional ai bo ne emo e sensing. Fu he mo e, i has been p o en ha esul s
ob ained om a RPAS o ag icul u al applica ion may yield simila es ima ions in e ms o accu acy and
p ecision o hose de i ed om adi ional ai bo ne pla o ms [15]. These ac s, oge he wi h hei lowe
cos and highe empo al lexibili y, explain he inc eased use o RPAS o ag icul u al pu poses [16]. As
a ma e o ac , a e y ecen epo on he economic impac o unmanned ai c a s in he Uni ed S a es
(US) [17] e ealed ha he use o RPAS in ag icul u e in 2015 in he US would yield $2096.5 and an
Remo e Sens. 2015, 7 14460
expec ed c ea ion o 21,565 jobs. Among he applica ions o RPAS in i icul u e, he assessmen o
wi hin- ineya d a iabili y s ands ou [18], as he spa ial and empo al esolu ions a e key a ibu es
when he goal o he s udy is moni o ing and managing he ineya d [3].
Mos o he emo e sensing s udies in ag icul u e a e based on he use o spec al indices. These a e
a i hme ical combina ions o he o iginal spec al bands cap u ed by he senso , which educe he
complexi y o he da ase [3] and acili a e he analysis o a ious ege a ion pa ame e s ha a e di ec ly
ela ed o hei spec al esponse a gi en wa eleng hs. Vege a ion indices, like he no malised di e ence
ege a ion index (NDVI), ha e been ex ensi ely used o moni o c op s a us by ela ing isible and nea
in a ed spec al da a wi h bio-geophysical p ope ies o he ines such as igou and lea a ea [18].
O he spec al indices like he pho ochemical e lec ance index (PRI) combine in o ma ion om wo
na ow bands in he isible egion o moni o ege a ion s ess [19].
The p esen wo k aims o show he capabili y o mul ispec al image y acqui ed om a RPAS, and
he de i ed spec al indices, o assess and map he spa ial a iabili y o ege a i e and yield componen s,
as well as be y composi ion in a ineya d (Vi is ini e a L.) o p ecision i icul u e managemen .
2. Ma e ials and Me hods
2.1. S udy A ea
The s udy was conduc ed in a 3.0 ha Temp anillo (Vi is ini e a L.) ineya d loca ed in Na a a,
Spain (Figu e 1). G ape ines we e plan ed in 2004 in a sandy-clay soil (5% slope owa ds No h), using
a 41B oo s ock a 2.4 m × 1.6 m (in e - and in a- ow) wi h no h-sou h ow o ien a ion. Vines we e
ained o a e ically shoo -posi ioned (VSP), spu -p uned co don e aining 16 nodes pe linea
me e / ine. Vines we e uni o mly i iga ed wice ac oss he season.
Figu e 1. Expe imen al Temp anillo ineya d plo wi h he 72 sampling poin s (a). Schema ic
desc ibing he posi ion o he h ee adjacen ines comp ising a sampling poin (b).
Remo e Sens. 2015, 7 14461
2.2. Field Da a Acquisi ion o Vege a i e and Yield Componen s. Be y Composi ion Analysis.
A egula g id o 72 sampling poin s a 20 m in e als was gene a ed wi hin he ineya d using a
Leica Zeno 10 GPS (Hee b ug, S . Gallen, Swi ze land), wi h eal- ime kinema ic co ec ion, wo king
a <0.4 m p ecision. Each sampling poin consis ed o h ee adjacen ines (Figu e 1b). Vege a i e and
yield- ela ed da a we e measu ed in he ield om all sampling poin s. As indica o s o ine igou , main
and seconda y shoo leng h and p uning weigh we e measu ed. Rega ding igou - ela ed a iables,
main and seconda y lea a ea pe shoo we e de e mined. Seconda y shoo leng h and seconda y lea
a ea pe shoo we e measu ed o he h ee ines included in each sampling poin one week p io o he
RPAS ligh , on 13 Sep embe 2011. Fo each ine, wo shoo s we e andomly chosen and hei main shoo
leng h, as well as hose o la e als wi h mo e han h ee lea es, we e measu ed using a me e ape [20].
Main and seconda y lea a ea pe shoo we e es ima ed by de olia ing each shoo and weighing he lea es
wi h a po able scale o p ecision ±2 g (Ke n and Sohn GmbH, Balingen-F omme n, Ge many), using
he disc-lea me hod by Sma and Robinson [21]. The p uning weigh o each ine was manually
de e mined in he ield using a hanging scale wo mon hs a e he ligh , on 20 No embe 2011, p io
o ine p uning. Addi ionally, non-des uc i e measu emen o pho osyn he ic pigmen s in plan s we e
ob ained on 13 Sep embe 2011, using a Mul iplex™ (Mul iplex 3.0, Fo ce-A, O say, F ance), a
handheld luo escence-based p oximal senso ha allows he compu a ion o luo escence-based indices
ela ed o he chlo ophyll con en in lea es (SFR_RAD) and he ni ogen balance index (NBI_GAD). The
Mul iplex™ senso used o his s udy was a modi ied e sion o he de ice ecen ly desc ibed by Ben
Ghozlen e al. [22]. Mul iplex™ measu emen s we e pe o med on h ee main lea es (adaxial side) pe
sampled ine. F om hese measu emen s, he luo escence indices we e calcula ed as ollows:
𝑆𝐹𝑅_𝑅𝐴𝐷 = 𝐹𝑅𝐹_𝑅𝐴𝐷
𝑅𝐹_𝑅𝐴𝐷
(1)
𝑁𝐵𝐼_𝐺𝐴𝐷 = 𝐹𝑅𝐹_𝑈𝑉𝐴𝐷
𝑅𝐹_𝐺𝐴𝐷
(2)
whe e FRF_R and FRF_UV e e o he a ed luo escence emission exci ed by ed and UV ligh ,
espec i ely, and RF_R and RF_G e e o ed luo escence exci ed by ed ligh and g een ligh ,
espec i ely [23]. The subsc ip AD s ands o he adaxial side o he lea . SFR_RAD has been epo ed
as a p ecise indica o o he chlo ophyll con en in kiwi lea es [24] and in g ape ine lea es [25], and
NBI_GAD has also been shown as a eliable indica o o he ni ogen s a us o he g ape ine [25].
A ha es ime (17 Oc obe 2011), main yield componen s, such as clus e numbe pe ine, yield
pe ine, clus e weigh , and be y weigh , we e de e mined o each sampled ine. Each plan was
manually ha es ed, hei clus e s coun ed and weighed in he ineya d using a hanging scale, and he
a e age clus e weigh calcula ed. Two clus e s pe ine we e hen labelled, kep apa and aken o he
labo a o y o he Uni e si y o La Rioja in po able e ige a o s whe e hey we e manually des emmed.
Be ies om each clus e we e weighed (TE3102S, Sa o ius, Goe ingen, Ge many) and coun ed, and
he a e age be y weigh calcula ed.
To de e mine be y composi ion, om he be ies co esponding o each ine, a ep esen a i e subsample
o 50 be ies was aken and weigh ed. These be y subsamples we e hen s o ed ozen a −20 °C un il colou
and phenolic analyses. The emaining be ies o each ine we e p essed and he ob ained mus was
analysed o o al soluble solids concen a ion (TSS) and acidi y pa ame e s. TSS (exp essed in B ix)
Remo e Sens. 2015, 7 14462
was de e mined using a empe a u e-compensa ing digi al e ac ome e (A ago, Tokyo, Japan), while
i a able acidi y and pH we e de e mined ollowing he OIV me hods [26]. An hocyanins and o al
phenols we e analysed o each be y sub-sample applying he me hod o Iland e al. [27]. An hocyanin
concen a ions we e exp essed as mg/g o esh be y mass, whe eas o al phenols we e exp essed as
abso bance uni s (AU) a 280 nm/g o esh be y mass.
Fo all da a collec ed in he ield and ela i e o be y composi ion, he alues o he h ee ines pe sampling
poin we e a e aged and a mean alue was hen assigned o each sampling loca ion. The ege a i e a iables,
p uning weigh , main lea a ea and seconda y lea a ea, and he p oduc i e a iable, yield, we e ans o med o
su ace uni s (pe m2) by di iding hem by he ine spacing [28].
2.3. RPAS Mul i-Spec al Images
Mul i-spec al image y was acqui ed wi h a mul i-spec al came a, a mini mul iple came a a ay
(MCA 6, Te acam Inc, Cha swo h, CA, USA) moun ed on a RPAS Md4-1000 (Mic od ones GmbH,
Siegen, Ge many) on 20 Sep embe 2011 (Figu e 2). The RPAS Md4-1000 is a quad o o able o ca y
1.2 kg o payload mass and able o ly up o 88 minu es, wi h e ical ake-o and landing. The came a
consis ed o six independen image senso s and op ics wi h use -con igu able il e s. Image esolu ion
was 1280 × 1024 pixels wi h 10-bi adiome ic esolu ion and op ics ocal leng h o 9.6 mm. Fo his
s udy, he came a was equipped wi h six 25 mm-diame e bandpass il e s o 10 nm ull-wid h a
hal -maximum (FWHM) (Ando e Co po a ion, NH, U.S.), wi h cen e wa eleng hs a 531, 551, 570,
672, 701, and 802 nm, and bandwid h o ±9.31, 10.13, 9.29, 9.82, 9.47, and 10.11 nm, espec i ely. The
wa ebands we e selec ed in o de o ob ain he spec al bands equi ed o he calcula ion o he spec al
indices commonly used in ag icul u e, and, speci ically, i icul u e- ela ed li e a u e.
Figu e 2. Mul ispec al came a moun ed on he RPAS Md4-1000 and de ail o he mul iple
came a a ay.
The RPAS lew a 250 m heigh and allowed cap u ing images wi h a spa ial esolu ion o 17 cm. Images
we e aken a noon, be ween 11:15 a.m. and 12:15 p.m., unde s able wea he condi ions and clea sky (mean
wind eloci y o 1.39 m/s). A his ime, g ape be ies we e a he hal -way poin o hei ipening p ocess. A

Remo e Sens. 2015, 7 14463
o al o 6 scenes we e acqui ed (wi h an o e lapping a ea o 60%) in o de o moni o he whole ineya d
plo leng hwise. Each image co e ed a g ound su ace o app oxima ely 200 × 165 m. I was able o co e
he whole plo wi h one ligh due o he educed ex ension o he ineya d, he lying heigh , and he came a
weigh (700 g), which allowed he RPAS o ha e enough ligh ope a ional ime.
2.4. Image P ocessing
F om he 6 o e lapping images ob ained wi h he mul i-spec al came a, wo scenes ha co e ed he
whole ineya d plo we e selec ed o he p esen s udy. An ini ial p e-p ocessing o he images acqui ed
wi h he Mini MCA came a was ca ied ou using i s companion applica ion Pixel W ench 2 (PW2),
w i en and copy igh ed by Te acam Inc., o p oduce mul i-page Tagged Image Fo ma (TIF) iles. As
he mul i-spec al came a uses six di e en lenses (one o each spec al band), i is necessa y o con i m
ha he bands a e aligned among hem. Band alignmen is impo an o ensu e consis en spec al
in o ma ion, bu when his co ec ion is ca ied ou by PW2, i u ns ou o be insu icien acco ding o
Lalibe e e al. [29]. In ag eemen wi h hese indings, he esul s o he in e -band alignmen pe o med
wi h PW2 showed subs an ial e o s in his s udy and, he e o e, i was decided o skip he alignmen
p ocessed included in PW2 by se ing a null aligning ma ix and ca y ou a speci ic co ec ion. Band
alignmen was pe o med by applying he Fine Regis a ion algo i hm o he O eo Toolbox [30] by
using a e e ence band (band 2). This p ocess calcula es he local shi s in he x and y di ec ions ha
esul in he bes local co ela ion be ween he band o be p ocessed and he e e ence band, and c ea es
a de o ma ion ield ha is subsequen ly applied o he band o be p ocessed. A e a se o es s wi h a
ange o adii, he local windows o explo e and calcula e co ela ions we e se o 5 × 5 pixels o bands 1,
3 and 4, and 7 × 7 pixels o bands 5 and 6. In addi ion, as he displacemen wi h espec o he e e ence
band was la ge o bands 5 and 6, a igid ansla ion was applied in bo h x and y di ec ions p io o he
ine egis a ion p ocessing o hese wo bands.
Once he band alignmen co ec ions we e comple ed, he images we e geo e e enced o allow o he
co ela ion o image da a wi h ield measu emen s. This was done by using ca og aphic co-o dina es
om eigh e e ence a ge s (whi e discs o 30 cm o diame e ) loca ed a he bounda ies o he ineya d
plo . Those a ge s had been geo e e enced in he ield du ing image acquisi ion using a Leica Zeno 10
Global Posi ioning Sys em (GPS) (Hee b ug, S . Gallen, Swi ze land). An o hopho o o he a ea o
s udy p o ided by he Spanish Na ional P og am o Ae ial O opho og aphy (PNOA) wi h 25 cm spa ial
esolu ion was complemen a ily used o iden i y 45 g ound con ol poin s in each scene. These poin s
we e e enly dis ibu ed o e he image a ea including he bo de s, whe e displacemen e o s we e
highe because o he la ge dis ance om he image nadi . A second deg ee polynomial unc ion was
applied using a bilinea esampling me hod o ende he geome ically co ec ed image. Image
geo e e encing was ca ied ou wi h an accu acy o 0.62 and 0.35 m ( oo mean squa ed e o ).
In e ac i e loca ion o g ound con ol poin s, polynomial i , and in e pola ion we e pe o med using
ENVI 4.8 (Exelis, McLean, VA, USA).
Radiome ic calib a ion was pe o med using an image o a whi e calib a ion panel ha was acqui ed om
he g ound wi h he mini MCA (a a dis ance abou 1.5 m) p io o he ligh . A e lec ance spec um o he
same panel was measu ed a he En i onmen al Remo e Sensing and Spec oscopy Labo a o y (SpecLab)
using an ASDFieldSpec 3 spec o adiome e (ASD Inc., Boulde , CO, USA). Image DN (Digi al Numbe s)
Remo e Sens. 2015, 7 14464
om he calib a ion panel measu ed in he ield we e a e aged o each band o calcula e he obse ed
e e ence DN spec um. The e lec ance spec um measu ed in he labo a o y was in eg a ed o calcula e he
heo e ical e lec ance alues o he panel o each mini MCA band using he ansmission cu es o he
il e s (www.ando e co p.com). A ec o o ac o s was compu ed o ans o m he obse ed DN spec um
o he image in o he modelled e lec ance spec um. These ac o s we e hen applied o each band o he
image in o de o ob ain calib a ed e lec ance o each image pixel. Images we e calib a ed o appa en
e lec ance, i.e., e lec ance as i he came a had been ope a ed a 1.5 m. A mosphe ic co ec ion was no
pe o med, bu as he a ea was small and he a mosphe ic condi ions e y clea , he uni o m a mosphe ic
e ec e en ually p esen in he indices was aken in o accoun by he linea models be ween indices and ield
da a. The adiome ic calib a ion ope a ions we e conduc ed by means o speci ic unc ions w i en in R Co e
Team [31] and using packages gdal [32] and as e [33].
The nex s ep was he spa ial assembly o he images o build he inal mosaic, so he whole ineya d plo
i wi hin a single image ile. The mosaicking p ocess was pe o med using ENVI (Exelis Visual In o ma ion
Solu ions, Inc, Boulde , CO, USA) so wa e by combining he wo scenes ha included he whole ineya d.
We assigned, as base image, he scene ha co e ed mos o he plo , so he colou balance in he second
image was ca ied ou o ma ch he base image by using his og am ma ching. A dis ance o 40 pixels was se
o blend he wo images.
Using he co ec ed image mosaic, a o al o 11 spec al indices, selec ed om he li e a u e as being
he mos commonly used o cha ac e ise ege a ion s a us, we e calcula ed (Table 1 [34–42]). These
indices ha e been p oposed o es ima e a wide a ie y o ege a ion p ope ies including pigmen
con en s, lea a ea index, plan heal h s a us, nu ien s ess, e c., and some o hem ha e been widely
used in p ecision i icul u e. Some indices specially designed o minimize he e ec s o soil backg ound
on he ege a ion signal as he OSAVI and MSAVI we e also included in he analysis.
In addi ion o hese indices, a se o no malised indices we e calcula ed as all possible-combina ions
be ween e e y image band, ollowing he equa ion:
𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑠𝑒𝑑 𝑖𝑛𝑑𝑒𝑥= 𝐵𝑎𝑛𝑑1−𝐵𝑎𝑛𝑑2
𝐵𝑎𝑛𝑑1+𝐵𝑎𝑛𝑑2
(3)
2.5. S a is ical Analysis
Wi h he aim o es ablishing an empi ical ela ionship be ween image da a and ield measu emen s,
image pixels co esponding o he 72 sampling poin s we e iden i ied. Fo each sampling poin , he wo
mos cen ed pixels (6 pixels pe poin ) we e selec ed. Image alues we e ex ac ed om hose pixels,
he spec al indices we e compu ed and a e aged pe sampling poin o each band. P io o he s a is ical
analysis, he da a we e p e-p ocessed in o de o de ec po en ial ou lie s.
Desc ip i e s a is ics we e compu ed o all a iables (mean, minimum, maximum, s anda d de ia ion
and coe icien o a ia ion), as well as he sp ead (Equa ion (4)), exp essed in pe cen age, as an indica o
o he a iabili y in he sample [43].
𝑆𝑝𝑟𝑒𝑎𝑑= (𝑚𝑎𝑥𝑖𝑚𝑢𝑚−𝑚𝑖𝑛𝑖𝑚𝑢𝑚)
𝑚𝑒𝑑𝑖𝑎𝑛
(4)
Remo e Sens. 2015, 7 14465
To cha ac e ise he spa ial a iabili y o he a ious pa ame e s wi hin he ineya d, a iog ams o
he a iables we e calcula ed by he R package “gs a ” [44]. The pa ame e s o he a iog ams we e also
used o in e pola e all he a iables, using k iging echniques.
Finally, o quan i y he ag eemen be ween he maps ob ained om he spec al indices and he ones
de i ed om ield a iables, all he maps we e classi ied in h ee zones co esponding o low, medium,
and high alues applying an iso-clus e unsupe ised classi ica ion. C oss abula ion o he esul ed
classi ied maps be ween he spec al indices and he in- ield a iables was pe o med o measu e he
s abili y in he spa ial pa e ns using he Kappa index ollowing he equa ion p oposed by Hudson and
Ramm [45]. S a is ical and spa ial analysis we e ca ied ou using Mic oso O ice Excel 2013
(Mic oso Co po a ion, Washing on, USA), S a is ica 9.0 (S a So , Inc., Tulsa, OK, USA), and A cGIS
Desk op 10.3 (ESRI, Redlands, CA, USA).
Table 1. Spec al ege a ion indices commonly used in i icul u e s udies.
Spec al Index
Re e ence
No malized Di e ence Vege a ion Index (NDVI)
(Rouse e al. 1974) [34]
𝑁𝐷𝑉𝐼= 𝑅𝑁𝐼𝑅 −𝑅𝑅𝐸𝐷
𝑅𝑁𝐼𝑅 +𝑅𝑅𝐸𝐷
Modi ied Simple Ra io (MSR)
(Chen 1996) [35]
𝑀𝑆𝑅= (𝑅𝑁𝐼𝑅
𝑅𝑅𝐸𝐷−1)
[(𝑅𝑁𝐼𝑅
𝑅𝑅𝐸𝐷)0.5+1]
Modi ied T iangula Vege a ion Index (MTVI1)
(Haboudane e al. 2004) [36]
𝑀𝑇𝑉𝐼1=1.2×[1.2×(𝑅800−𝑅550)−2.5×(𝑅670−𝑅550)]
Reno malized Di e ence Vege a ion Index (RDVI)
RDVI= RNIR-RVIS
√RNIR+RVIS
(Roujean and B eon 1995)
[37]
G eenness Index (G)
-
𝐺= 𝑅𝐺𝑅𝐸𝐸𝑁
𝑅𝑅𝐸𝐷
Modi ied SAVI (MSAVI)
(Qi e al. 1994) [38]
𝑀𝑆𝐴𝑉𝐼= 1
2 [2× 𝑅800+1−√(2×𝑅800+1)2−8×(𝑅800+𝑅670)]
Op imized Soil Adjus ed Vege a ion Index (OSAVI)
(Rondeaux e al. 1996) [39]
𝑂𝑆𝐴𝑉𝐼 = (1+0.16)(𝑅𝑁𝐼𝑅 −𝑅𝑅𝐸𝐷)
(𝑅𝑁𝐼𝑅 +𝑅𝑅𝐸𝐷 +0.16)
Modi ied Cab Abso p ion in Re lec ance Index (MCARI)
(Daugh y e al. 2000) [40]
𝑀𝐶𝐴𝑅𝐼 =[(𝑅700+𝑅670)−0.2×(𝑅700−𝑅550)]×(𝑅700
𝑅670)
T ans o med CARI (TCARI)
(Haboudane e al. 2002) [41]
𝑇𝐶𝐴𝑅𝐼 = 3 × [(𝑅700–𝑅670)–0.2×(𝑅700−𝑅550)×(𝑅700
𝑅670)]
TCARI/OSAVI
(Haboudane e al. 2002) [41]
𝑇𝐶𝐴𝑅𝐼/𝑂𝑆𝐴𝑉𝐼 = 3×[(𝑅700–𝑅670)–0.2×(𝑅700−𝑅550)×(𝑅700 𝑅670
⁄ )]
(1+0.16)×𝑅800−𝑅670
𝑅800+𝑅670+0.16
Plan Cell Densi y Index (PCD) o Ra io Vege a ion Index (RVI)
(Jo dan 1969) [42]
𝑃𝐶𝐷=𝑅𝑉𝐼= 𝑅𝑁𝐼𝑅
𝑅𝑅𝐸𝐷
Remo e Sens. 2015, 7 14466
3. Resul s and Discussion
3.1. Spa ial Va iabili y o Spec al Indices, Field Va iables, and Be y Composi ion
Spec al indices selec ed om li e a u e (Table 1) and he no malised indices (Equa ion (3)) we e
co ela ed wi h ield (g ound u h) da a in o de o exclude om u he analysis hose indices ha
exhibi ed weak o no signi ican co ela ions. As a esul , co ela ions wi h a Pea son coe icien , “ ”,
smalle han 0.35 in absolu e alue we e disca ded. Fu he mo e, he spec al indices wi h a high
co ela ion coe icien we e also disca ded o a oid edundan in o ma ion. The spec al indices ha
showed he lowes co ela ion we e MTV1 o RDVI, which yielded mean co ela ion coe icien s wi h
in- ield a iables o a ound 0.2. The indices inally selec ed o u he analysis we e NDVI and G.
Two indices om he g oup o no malised indices we e also selec ed, which we e named as
No malised Vege a ion Index 1, NVI1 (R802 − R531/R802 + R531), and No malised Vege a ion Index 2,
NVI2 (R802 − R570/R802 + R570), bo h esul ing om he combina ion be ween NIR and g een e lec ance
alues, simila o he G een NDVI p oposed by Gi elson e al. [46].
Desc ip i e s a is ics o he spec al indices and ield a iables a e epo ed in Table 2. As i can be
obse ed, mos o hem exhibi ed conside able a iabili y wi hin he ineya d. On he basis o coe icien
o a ia ion (CV) and Sp ead pa ame e s, seconda y shoo leng h and seconda y lea a ea we e, by a ,
he ield a iables showing he la ges a iabili y (CV = 80%, Sp ead > 370%), ollowed by yield pe
ine (CV = 57%, Sp ead = 280%), and numbe o clus e s pe ine (CV = 51%, Sp ead = 250%). These
alues we e simila o hose obse ed by Baluja e al. [47] in Temp anillo in La Rioja (Spain) and
by B amley and Lamb [48] and B amley and Hamil on [49] in Aus alia, in o he a ie ies. The
luo escence index indica i e o lea chlo ophyll con en (SFR_RAD), he no malised indices NVI1
and NVI2, be y weigh , pH, TSS, and i a able acidi y showed a CV smalle han 10%. NBI has
been desc ibed as a good indica o o he ni ogen s a us o g ape ine lea es [24,50,51]. I s
calcula ion depends on he concen a ion o la onoids and chlo ophyll in he lea , so i s a iabili y
mus be highe han ha o chlo ophyll and la onoids alone [51]. Fu he mo e, he p esence o
wa e de ici due o e y wa m empe a u es ( he a e age o he mon h mean empe a u es du ing
he s udy yea exceeded be ween 1.5 °C and 3.2 °C he alue o he a e age o he his o ical se ies
mean empe a u es) and d yness ( o al ain all om July o Sep embe was 30.9% o a e age
his o ical ain all in his pe iod) as epo ed by he Na a a Go e nmen ’s me eo ological s a ion o
Es ella, may ha e induced a weak mine aliza ion o soil ni ogen, which in u n led o ni ogen
de iciency on he lea es o he ines in some a eas o he ineya d, hence con ibu ing o he la ge
a iabili y obse ed o he NBI_GAD index.
All o he expe imen al a iog ams o he a iables s udied we e i ed o a Sphe ical o Gaussian
model, wi h he excep ion o yield pe ine and he an hocyanin concen a ion, which showed an absence
o spa ial s uc u e as also ound by Ce o ic e al. [52]. The ange o he a iog am is he dis ance a
which he sill (maximum semi a iance) is achie ed and indica es whe he he samples a e spa ially
dependen (samples sepa a ed by dis ances close han he ange) o spa ially independen (samples
sepa a ed by dis ances la ge han he ange) [53]. The ange alue was a iable o all pa ame e s
s udied (Table 2). Main lea a ea, main shoo leng h, pH, and i a able acidi y showed a ange a ound
125 m. The spec al index, G, was he a iable wi h he highes ange alue (202 m). The lowes alues
o he ange ( om 38 o 65 m), ha is he lowes dis ance o spa ial dependence, we e ound o he
Remo e Sens. 2015, 7 14473
Figu e 6. Maps ob ained o g ape quali y pa ame e s (pH, o al soluble solids, Ti a able
acidi y, an hocyanins, and o al phenols) in a Temp anillo (Vi is ini e a L.) ineya d. Maps
we e ep esen ed by e ciles. AU: abso bance uni s.
Rega ding be y composi ion, pH and TSS ha e shown he highes alues on he Eas side o he plo and
he lowes alues on he Wes side (Figu e 6). Consequen ly, i a able acidi y showed he opposi e beha iou ,
wi h highe alues on he Wes side and lowe on he Eas side, as he spec al index G (Figu e 3). The
an hocyanin concen a ion has showed a andom pa e n ac oss he plo , while o al phenols yielded he
lowes alues a he cen e o he plo (Figu e 6), inc easing owa d he sides, simila o he spa ial pa e n
o NVI1 and NVI2 (Figu e 3).
The isual ma ch o he spa ial pa e ns be ween he spec al indices and he in- ield a iables and
be y composi ion was quan i a i ely assessed by ca ying ou a c oss- abula ion and he compu a ion o

Remo e Sens. 2015, 7 14474
he kappa index om each a iable 3-clus e -map. Table 4 shows he kappa index alue o he
c oss- abula ion be ween he spec al indices and he in- ield and be y composi ion a iables. The kappa
index yielded alues anging om –0.27 o 0.70, ha is, om poo o subs an ial ag eemen , ollowing
he classi ica ion p oposed by Landis and Koch [62].
Table 4. C oss- abula ion ou pu s and kappa index ob ained o he classi ied maps o
spec al indices (NDVI, G, NVI1, and NVI2), ege a i e pa ame e s (p uning weigh , main
shoo leng h, seconda y shoo leng h, main lea a ea pe shoo , and seconda y lea a ea pe
shoo ), luo escence-based indices o ni ogen balance index (NBI_GAD) and chlo ophyll
con en in lea es (SFR_RAD) o yield pa ame e s (clus e numbe pe ine, clus e weigh ,
yield pe ine, and be y weigh ) and o g ape composi ion a iables (pH, o al soluble
solids, i a able acidi y, an hocyanins, and o al phenols) in a Temp anillo (Vi is ini e a
L.) ineya d.
NDVI
G
NVI1
NVI2
Main shoo leng h
0.41
0.67
0.18
0.17
Seconda y shoo leng h
0.47
0.59
0.45
0.44
Main lea a ea
0.43
0.70
0.19
0.16
Seconda y lea a ea
0.47
0.57
0.46
0.45
P uning weigh
0.32
0.08
0.39
0.35
SFR_R
0.11
0.10
0.39
0.30
NBI_G
0.32
0.36
0.38
0.35
Clus e numbe
−0.26
−0.27
−0.17
−0.15
Mean clus e weigh
0.35
0.35
0.37
0.40
Yield
0.01
−0.14
0.01
−0.01
Mean be y weigh
0.12
0.11
0.13
0.12
pH
0.14
0.35
−0.05
−0.05
To al soluble solids
0.11
0.25
−0.06
−0.04
Ti a able acidi y
−0.16
−0.15
−0.24
−0.23
An hocyanins
−0.01
0.00
−0.04
−0.07
To al phenols
0.00
−0.02
−0.09
−0.08
These esul s show ha , spa ially speaking, he classical index G was he one showing a spa ial pa e n
ha bes ag eed wi h he spa ial pa e n o he ege a i e a iables, especially hose ega ding shoo leng h
and lea a ea, wi h alues anging om 0.59 o seconda y shoo leng h (mode a e ag eemen ) o 0.70 o
main lea a ea (subs an ial ag eemen ). P uning weigh , lea chlo ophyll con en , and ni ogen s a us be e
concu ed wi h he no malized index NVI1, yielding a ai ag eemen (0.40). On he o he hand, ega ding
he p oduc i e and be y composi ion a iables, mean clus e weigh and pH showed he highes kappa
index alues, especially wi h he no malized index NVI2 and G, espec i ely (0.40 and 0.35, ai
ag eemen ), while clus e numbe , yield, mean be y weigh , pH, o al soluble solids, i a able acidi y,
an hocyanins, and o al phenols showed he lowes kappa coe icien s wi h alues indica ing poo o sligh
ag eemen wi h he spec al indices, con i ming he poo co ela ions obse ed.
The ma ch o he spa ial pa e ns is showing ha , in gene al, he spec al indices de i ed om he
mul ispec al images aken om a RPAS a e use ul ools o he g ape-g owe o manage he spa ial
a iabili y o he ineya ds. These indices seem o be mo e eliable o he managemen o he ege a i e
Remo e Sens. 2015, 7 14475
a iables han o he p oduc i e and g ape composi ion pa ame e s, which is in ag eemen wi h he ac
ha he mul ispec al came a is e ie ing he da a om an o e head poin o iew, ha is, om he
canopy, no “seeing” he p oduc i e pa o he g ape ines loca ed below he canopy. The e o e, while
he mul ispec al senso is di ec ly sensing he ege a i e componen o he ineya d, he ela ion wi h
he p oduc i e componen o he ineya d emains indi ec . Mo eo e , depending on he me eo ological
condi ions in he season, mo e igo ous suba eas o a ineya d plo may exhibi a highe wa e demand
han less igo ous plan s in a d y yea , expe iencing wa e s ess ha may jeopa dize bo h g ape yield
and composi ion, while in coole and less d y seasons, highe yield alues a e expec ed in
igo ous plan s.
In summa y, he spec al indices ob ained om he mul ispec al images cap u ed om a RPAS ha e
been shown o signi ican ly co ela e wi h he ege a i e a iables and some o he yield a iables. Two
newly de ined spec al indices ha e also been p o ided ha ha e shown he bes co ela ions wi h
impo an ege a i e a iables such as p uning weigh , ni ogen s a us, o lea chlo ophyll con en . The
classical me hods o he measu emen o ege a i e and yield pa ame e s a e labo ious and gene ally
des uc i e. The e o e, he spec al indices could be a use ul ool o es ima e hese a iables o in e es
in a as and non-des uc i e way, a a ime ea lie han hey could be assessed by he classical me hods.
Fu he mo e, mul ispec al images and hei de i ed spec al indices p o ide he end-use wi h a
con inuous su ace o da a om he ineya d wi h high spa ial esolu ion; ha is, a la ge da abase ha
makes i possible o ake in o accoun he spa ial a iabili y o he ineya d s a us. This ac makes i
ope a ionally easible o ca y ou a segmen a ion o he ineya d in 2 o 3 homogeneous managemen
zones. In addi ion, compa ed o o he emo e sensing pla o ms, RPAS p o ide he g ape-g owe no
only wi h highe spa ial esolu ion, bu also wi h highe lexibili y o moni o hei ineya ds, especially
he small ineya ds p e ailing in Eu ope (a e age ineya d size is less han 5 ha). Ano he impo an
ac o o he g ape-g owe ha has no been p o usely add essed in li e a u e is he cos o applying
emo e sensing echnologies. A e y in e es ing pape has ecen ly been published by Ma ese e al. [14]
in which he au ho s ca y ou a cos compa ison among h ee di e en emo e sensing pla o ms:
sa elli e, ai c a , and RPAS. Thei esul s ha e shown ha , o small su aces (5 ha), as in he p esen
wo k, he use o RPAS is he mos cos -e ec i e solu ion due o he lowes acquisi ion cos s [14]. The
si ua ion changes wi h a highe su ace o s udy (50 ha), as mo e images a e needed o co e i , inc easing
he cos s o acquisi ion, p ep ocessing, and analysis o hese images. In his case, he sa elli e would be
a mo e cos -e ec i e choice, as i is able o co e a high su ace wi h one image, dec easing he
subsequen cos s. To summa ize, as shown by Ma ese e al. [14], each echnology has echnological,
ope a ional, and economic ad an ages and disad an ages and mus be selec ed acco ding o he wo k
planned. In his s udy, some incon eniences ega ding he ope a ional ac o s o he ligh we e e ealed.
Despi e g ea e independence and lexibili y o RPAS sys ems, one o hei disad an ages is ha hey
a e signi ican ly a ec ed by he wind, e en by ligh wind, gene a ing mo emen s o he pla o m ha
we e e lec ed in he images as geome ic dis o ions. In addi ion, a lowe lying heigh would esul in
a highe spa ial esolu ion o he images, which should educe he p oblem o he spec al mix u e,
p o iding pu e ine pixels. These ac s may lead o imp o ed co ela ions be ween spec al indices and
in- ield a iables.
Ou esul s ha e shown ha he h ee sub-zones delinea ed in he ineya d based on he spec al
indices ag eed wi h key ege a i e pa ame e s such as p uning weigh , main lea a ea, o main shoo
Remo e Sens. 2015, 7 14476
leng h, and e en wi h yield pa ame e s such as mean clus e weigh . I has been explo ed he e by he
applica ion o kappa index, a common index usually used o assess land co e changes [63] ha has
p o en e y use ul in assessing he ag eemen be ween he suba eas delinea ed wi h spec al indices and
he in- ield a iables. These a iables ha e been p o en o be empo ally s able [49,64], so i could be
use ul o ca y ou ege a i e managemen ac ions such as e ilisa ion, soil a ming (i.e., in e - ow co e
c opping o limi he plan igou in e y igo ous suba eas) and win e p uning in he ollowing yea s.
Fu he mo e, he possibili y o cus omizing he il e selec ion o he mul ispec al came a may allow
o collec ion o da a in he sho wa e in a ed egion o he spec um i il e s wi hin his egion a e
chosen, which could p o ide ele an in o ma ion on he plan wa e s a us and could also p o ide
in e es ing da a ega ding yield [65]. In his wo k, hese new echnologies made i possible o delinea e
h ee subzones o homogenous ege a i e s a us wi hin he ineya d, whe e he g ape-g owe could
de ine i s equi emen s and apply he app op ia e inpu s (wa e , ni ogen, e c.) o managemen p ac ices
o each subzone— ha is, he implemen a ion o p ecision i icul u e echniques in he ineya d.
4. Conclusions
The esul s p esen ed in his s udy con i m he po en ial o mul i-senso RPAS sys ems o he
assessmen o he spa ial a iabili y o he ineya d ege a i e and p oduc i e s a us in p ecision
i icul u e. The wo de eloped ege a ion spec al indices, NVI1 and NVI2, we e hose bes co ela ed
wi h ield ege a i e pa ame e s measu ed in sampling plo s.
Mo eo e , he kappa index has shown ha he spa ial pa e n o he spec al indices ag eed wi h ha
o he ege a i e and some o he p oduc i e a iables measu ed in he ineya d. In his case, he classical
spec al index G was he one showing he highes spa ial ag eemen wi h he ege a i e a iables. The
spec al indices enabled he delinea ion o h ee sub-zones o homogeneous managemen , wi hin he
ame o p ecision i icul u e, o op imize he managemen o inpu s (wa e , ni ogen, e c.), soil
managemen , and win e p uning.
These esul s a e a e y impo an ou come because hey p o ed ha he use o a mul ispec al senso
moun ed on a RPAS could become a eliable ool o he g ape-g owing indus y o manage he spa ial
a iabili y wi hin he ineya d. The possibili y o acqui ing “on-demand” low-cos images wi h
unp eceden ed high spa ial esolu ion will ce ainly p omo e i s ope a ional applica ion o ineya d
managemen in o de o imp o e c op p oduc i i y and sus ainabili y.
Acknowledgmen s
This s udy was ca ied ou as pa o he Tele i is p ojec (Re . ADER-2008-00187) unded by he
Agencia de Desa ollo Tecnológico de La Rioja (La Rioja, Spain). Special g a i ude goes o Ai es udio
Geoin o ma ion Technologies S. Cop. o he acquisi ion o images wi h he RPAS. Addi ionally, we
hank Ja ie Baluja and Vic o Sicilia o collec ing ield da a, and In i Luna o his wo k on he
image co ec ions.
Remo e Sens. 2015, 7 14477
Au ho Con ibu ions
MPD and JT designed he s udy and implemen ed he expe imen s. CRC, MPM and AL analysed he
da a. CRC and MPD w o e he manusc ip d a . All au ho s e iewed and app o ed he inal manusc ip .
Con lic s o In e es
The au ho s decla e ha his wo k was pa ially unded by Fo ce-A by he lending o he
senso s used.
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