Early detection of drought stress in
potato (Solanum tuberosum L.) and grapevine
(Vitis vinifera L.) crops through multifractal
analysis applied to remotely sensed data
Abstract
0/80
80
0
400
60
500
600
700
8 dpt Wavelenght (nm)
Dh
20
800
40
500
600
700
800
0,8
h
1,2
7
Wavelenght (nm)
D50
D75
Ctrl
0,45
100,8 15 1,2 20
Days posthtreatment (dpt)
1b
0,4
1,6
D50
D75
Ctrl
0,0 0
1,6
25
5
10
15
20
Days post treatment (dpt)
16
Results and Discussion
14
12
10
15
20
25
Days post treatment (dpt)
2nd greenhouse experiment
20
0,5
2a
D50
D75
D75
D50
Ctrl
Ctrl
0,1
18
0,0
16
0
5
10
15
20
Days post treatment (dpt)
14
25
30
D75
D50
Ctrl
0,2
20
0,1
10
0
5
10
15
20
D50
Days post treatment (dpt)
D75
25
0
4
0,5
3a
g (mol H2O.m-2.s-1)
AN (µmol CO2.m-2.s-1)
AN (µmol CO2.m-2.s-1)
20
10
D50
D75
Ctrl
0
4
8
12
16
8 dpt.D50
8 dpt.D75
8dpt.Ctrl
(1st greenhouse experiment)
Value
Value
-0,01
800
-0,02
400
(1st greenhouse experiment)
8 dpt.D50
8 dpt.D75
8 dpt.Ctrl
1,0
0,00
8 dpt.Ctrl
8 dpt.D75
8 dpt.D50
500
600
700
Wavelenght (nm)
0,5
800
0,0
400
8 dpt.Ctrl
8 dpt.D75
8 dpt.D50
500
600
Wavelenght (nm)
700
0,8
0,6
0,0
0,4
0,8
h
5
10
15
20
Days post treatment (dpt)
16
20
1,2
1,6
Figure 2. Pre-processing of reflectance data. Notice that the observable differences in
the raw reflectance spectra do not allow a logical treatments discrimination. Neither the
observable differences in the second order derivative nor the calculation of light
absorbance, do demonstrate the drought levels as the multifractal singularity spectra
does.
0,4
0,3
0,2
D50
D75
Ctrl
0,1
0,0
0
4
8
12
16
20
Days post treatment (dpt)
0,3
0,2
D50
D75
Ctrl
0,1
0
4
8
12
16
20
Days post treatment (dpt)
0,35
D2
D1
Ctrl
1,0
20
0,9
16
0,8
12
0,7
0,6
8
0,5
Ctrl.27 dpt
D2.27 dpt
D1.27 dpt
4
0,4
D2
D1
Ctrl
0,30
0,25
0,20
0,15
0,10
0,05
0,00
0
0,3
500
600
700
800
900
0
4
8
0,0
12
0,2
16
0,4 20
0,6 24
0,8 281,0 321,2 361,4
0
1,6
Days post treatmenth (dpt)
4
8
12
16
20
24
28
32
36
Days post treatment (dpt)
Bibliography
D2. 0 dpt
D1. 0 dpt
Ctrl. 0 dpt
a
80
60
40
150
500
D2. 6 dpt
D1. 6 dpt
Ctrl. 6 dpt
b
Sap flow (g.m -2.s-1)
100
200
Sap flow (g.m -2.h-1)
Sap flow (g.m-2.h-1)
25
3b
The spectral vegetation indexes (SVI) tested did show an
inconsistent response, even those indexes specifically developed to
assess water content in plants.
Multifractal singularity spectra
1,5
0,01
20
(1st greenhouse experiment)
D(h)
(1st greenhouse experiment)
1,0
0
3b
Days post treatment (dpt)
120
Absorbance
0,1
1.
Experiments were carried out under outdoor conditions in Mallorca,
Spain, 2006. In potato, the treatments were Control (Ctrl), 100% of
the daily measured evapo-transpiration (dme), moderate-drought
(D75), 75% of dme, and severe-drought (D50), 50% of dme. Pots
were weighted every day to determine the amount of water available
with respect to the Control. Their relative water content (RWC) was
determined. Drought treatments in grapevine were induced by
stopping irrigation during 5 days per week, as 1) severe-drought (D1),
irrigation re-initiated the evening of day 6; 2) moderate-drought (D2),
irrigation re-initiated the morning of day 6; and Control (Ctrl), irrigated
at field capacity. Light reflectance measurements were taken at noon,
through a multispectral spectrometer. Gas exchange measurements
were performed using a portable infrared gas analyzer (Figure 1). Sap
flow measurements in grapevine were made through the thermo heat
balance method (THB).
2,0
Systems & the
Environment Division.
International Potato Center.
P.O. Box 1558, Lima 12, Peru
0,4
20
-2.s
-1.s
-2
) -1)
H2
g (molH
g (mol
O.m
2O.m
40
2Production
D75
D50
Ctrl
0,2
0,0
0
D(h)
AN (µmol CO2.m-2.s-1)
Reflectance %
60
12
Days post treatment (dpt)
24
27 dpt. D1
27 dpt. D2
27 dpt. Ctrl
8
25
0,3
0,5
3a
0,3
Growth chamber experiment
80
Materials and Methods
2nd derivative of reflectance
0,4
Growth chamber experiment
Figure 4. Reflectance (left), multifractal singularity spectra (centre) and daily
stomatal conductance (right) of plants from the grapevine experiment (Gray
bars indicate days of irrigation).
0,02
2b
Ctrl
30
5
10
15
20
Days post treatment (dpt)
0,2
0
A
Reflectance
D50
D75
Ctrl
0,1
0,3
The raw reflectance of plants did not discriminate among treatments
(Figure 2); a situation corrected with pre-processing and multifractal
analysis of data (Figure 3). In potato, discrimination was perceived
7 dpt (i.e. 2-4 days prior to the gas-exchange and RWC
measurements). In grapevine, it occurred 6 dpt, i.e. 2 days earlier
than the gas-exchange and sap flow measurements (Figures 4 and
5). Divided reflectance evidenced stressed plants at around 7 dpt
for both potato and grapevine plants. The main bands for detecting
drought stress were, from the best to the worst, the blue, followed
by NIR, red and finally the green region. 2a 2b
5
0,2
0
g (mol H2O.m-2.s-1)
-2.sH
-12) O.m-2.s-1)
g.m
(mol
AN (µmol CO2
AN (µmol CO2.m-2.s-1)
Ctrl
0
0,3
25
12
Figure 3. Passive reflectance of2nd greenhouse
potatoexperiment
plants (left) and
their
corresponding
0
5
10
15
20
25
Days
post)treatment
multifractal singularity spectra (centre
). Daily photosynthesis
,1a
and(dpt)
stomatal
20
0,5 (right
2a
2b
D75
conductance (right,1b).
D50
18
0,4
Wavelenght (nm)
Reflectance (%)
0,4
8dpt
0,6
400
D50
D75
Ctrl
7
0
1a
0,7
20
14
Group on Plants
under Mediterranean
Conditions. University of
Balearic Islands. Crrtra.
Valldemossa km.7.5, 07122,
Palma de Mallorca, Balearic
Islands, Spain
0,0
0,8
14
0
400
500
600
700
Wavelenght (nm)
1st greenhouse experiment
0,6
0,6/1,0
1,028
0,0
0,9
21
D50
D75
Ctrl
0
0
400
D50
D75
Ctrl
0,7
21
1Research
1b
0,4
g (mol H2O.m-2.s-1)
0,8
1a
g (mol H2O.m-2.s-1)
40
Dh
AN (µmol CO2.m-2.s-1)
Reflectance %
Reflectance (%)
D50
D75
Ctrl
20
40
0dpt
0,9
60
AN (µmol CO2.m-2.s-1)
0 dpt
100
60
28
1,0
Wavelenght (nm)
-1) H2O.m-2.s-1)
AN (µmol CO2.m-2g.s(mol
80
Availability of water is the most limiting factor in crop production. This
problem will be exacerbated with the imminent climate change. Even
if the rainfall levels are held constants, the risks of severe dryness
increases due to the rise of the evaporative atmospheric demand
caused by the global warming. Direct plant based measurements are
mainly limited to leaf water potential by pressure chamber, stomatal
conductance by gas-exchange, and porometry. These are timeconsuming and require a number of observations to characterize a
whole field. Non-destructive non-invasive remote sensing methods
emerge as effective alternatives for assessing the status of crops
through reflectance and imagery (1). The aim of this work was to test
remotely sensed reflectance as a revealing technique for retrieving
invisible changes caused by drought in live plants.
Figure 1. (A) Measuring physiological parameters in a potato plant (Solanum
tuberosum L.) using an infrared gas analyzer (IRGA) LI-6400. (B) Detail of the
IRGA’s chamber.
Chávez P.1,2
Ribas-Carbó M.1
Medrano H.1
Mares V.2
Posadas A.2
Yarlequé C.2
Quiroz R.2
Flexas J.1
1st greenhouse experiment
Introduction
80
Symposium
The reflectance spectrum was divided into blue, green, red and
near infrared sectors for calculating the percentage of reflectance
against time. The differences were determined using a repeated
measurements statistical analyses. The reflectance data were preprocessed applying a background correction, and then submitted to
the Continuous Wavelet Transform (CWT) and the wavelet
transform modulus maxima (WTMM) method (2) using as mother
wavelet analyzer the second derivative of the Gaussian function
(Mexican hat).
The importance of timely detection of drought stress in agricultural
crops is increasing due to the imminent climate change. Several
methodologies are being developed for assessing, monitoring, and
managing water availability, to supply the accurate water amount to
crops, while maintaining the highest WUE feasible. The objective of
this work was to determine the suitability of remote sensing as a
monitoring tool to detect drought stress in plants. Continuous
measurements of multispectral reflectance and derived vegetation
indices, taken from potato and grapevine crops, were analyzed, and
compared with simultaneous measurements of photosynthesis,
stomatal conductance and sap-flow. Multifractal analysis of reflectance
data did discriminate between the well irrigated and drought treatments
around 2 - 6 days earlier than physiological measurements. Vegetation
indices of discrete regions also provided early detection of drought.
Results evidenced that remotely sensed data might be useful as early
detectors of drought stress and that the use of multifractal analysis of
multispectral data might provide a more robust discrimination between
turgent and stressed plants
B
ISTRC
100
50
20
0
01:00 05:00 09:00 13:00 17:00 21:00
Time
400
c
D2. 8 dpt
D1. 8 dpt
Ctrl. 8 dpt
300
200
100
0
0
01:00 05:00 09:00 13:00 17:00 21:00
01:00 05:00 09:00 13:00 17:00 21:00
Time
Time
Figure 5. Continuous records of sap flow in grapevine. At the beginning of the
experiment, a and b indicate that there were no differences among treatments.
Differences among control and drought treatments are observable in c 8 days post
treatment and onwards.
Conclusions
• Conventional methods such as gas-exchange, relative water
content and sap flow measurements (considering both potato and
grapevine) evidenced drought stress 9 – 11 dpt. Multifractal
analysis of reflectance data did it 2 -4 days before. The challenge
now is to replicate the findings in commercial fields.
• Although splitting the reflectance into bands showed comparable
results, multifractal analysis seems more reliable since results
were more stable along the trial.
• For a more reliable estimation of drought in potato through gas
exchange, AN and g must be considered simultaneously.
• SVI are not a reliable method to retrieve water stress in plants.
2.
Chávez P., Yarlequé C., Piro O.,
Posadas. A., Mares V., Loayza H.,
Chuquillanqui C., Zorogastúa P.,
Flexas J., Quiroz R. 2009(b).
Applying multifractal analysis to
remotely sensed data for assessing
PYVV infection in potato (Solanum
tuberosum L.) crops. Remote
Sensing Journal. Under revision.
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J., and Gallagher P.T. 2007. The
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The
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Astronomical
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Astrophysical Journal, 662:691700.