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Te = 100* Transpiration / evapotranspiration
Transpiration Efficiency is highly
dependent on climate, soil and crop types.
Because irrigation alters the thermal structure
of the mixed layer by cooling the canopy air
space, Transpiration Efficiency varies with
time during the irrigation process.
•At any time of the day; irrigation is more
efficient at the beginning of the irrigation
process than at the end
•Irrigation is more efficient during
morning rather than afternoon hours
•Furthermore, irrigation is more efficient
during spring than summer when the
evaporative demand is high .
This is an important result that can be used to
optimize productivity and reduce water use.
Along with the minimum physiological
water requirement, several efficiencies apply
when computing the total water use for
irrigation.
•Transport (Conveyance) Efficiency
•Transpiration Efficiency
For this North African region, the
conveyance efficiency for all transport types
has degraded since 2000, due to the aging of
the irrigation system, leading to transport
efficiency values around 50% for 2006 .
Irrigation Requirement Estimation Using MODIS Vegetation Indices and Inverse Biophysical Modeling
Lahouari Bounoua1, Marc L. Imhoff1, Ping Zhang1,2 , Arnon Karnieli3, Mohammed Messouli4
Email: [email protected]
1. NASA Goddard Space Flight Center, Greenbelt, MD 20771 2. Earth Resources Technology Inc. , Laurel, MD 3. Ben-Gurion University of the Negev 4. Univeristy of Cadi Ayyad, Marrakech
Introduction
We combine remote sensing data from Landsat and MODIS and an inverse biophysical modeling methodology to detect irrigated agricultural lands in arid and semi arid regions and to quantify the amount of irrigation
water required for agricultural production as influenced by climate, crop type, soil characteristics and irrigation efficiencies. This satellite-supported inverse modeling approach will provide unique information on the
minimum physiological water requirement for each crop type and climate conditions and may be used for an priori planning of cropping as function of soil characteristics and water supply. Furthermore, the
methodology also applies different irrigation efficiencies and takes into account soil salinity to estimate the total water allocated to irrigation.
MODIS data are used to estimate the seasonal cycle of LAI, FPAR and other biophysical
parameters of a crop canopy. The inverse modeling approach consists of comparing the
carbon and water flux modeled under both equilibrium (in balance with prevailing
climate) and non-equilibrium (irrigated) conditions.
We postulate that the degree to which irrigated lands vary from equilibrium conditions is
related to the amount of irrigation water used.
Climate drivers input
Biophysical attributes input
Run the biophysical model
and determine the amount of
water in the root zone (W2)
Compute the water stress
function F(W2)
F(W2) ≤ Fcr
Transpiration
Te
Add water
Output Transport Te
0
0.2
0.4
0.6
0.8
1
Str
ess
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Irrigation
Stress without Irrigation Stress with Irrigation Irrigation (mm.hr-1)
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
0
0.2
0.4
0.6
0.8
1
Soil
Mois
ture
exp1 surface layer exp1 root layer
control surface layer control root layer
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Surface and root layer soil moisture for the control and exp1 (see text for details) expressed as a
fraction of saturation for July
1.025
days
0
0.2
0.4
0.6
0.8
1
1 47 93 139 185 231 277 323 369 415 461 507 553 599 645 691
So
il M
ois
ture
Surface layer Root layer
Control Surface layer Control Root Layer
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
0
0.2
0.4
0.6
0.8
1
1 47 93 139 185 231 277 323 369 415 461 507 553 599 645 691
Wa
ter
Str
es
s
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Irri
ga
tio
n
Stress without Irrigation Stress with Irrigation Irrigation (mm.hr)
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
1.9
days
Algorithm Results
The use of the drip irrigation method
reduced both the canopy and ground
interception compared to spray irrigation.
This water delivery method results in less
frequent irrigation events (about every 48
hours) with an average water requirement
amount of about 0.6 mm per occurrence,
that's about 43% of that simulated for spray
irrigation.
Since water is added directly on top of the
canopy, it first saturates the canopy
interception store, fills the surface layer
and then infiltrates into the root zone. The
water content in the first layer almost
mirrors the irrigation pattern. As water is
added however, the moisture content in the
root zone slowly builds up and maintains
values significantly higher than those
obtained during the control simulation.
Control and Drip Control and Spray
Field data and Validation
Irrigation Efficiency
Transportation Efficiency: 100 * amount of delivered / amount of diverted water.
Transpiration Efficiancy at 10:00AM
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
10:00 10:08 10:16 10:24 10:32 10:40 10:48 10:56
B
14-Apr 17-May
21-Jun 8-Jul
31-Aug 21-Sep
Transpiration Efficiancy for May 25th
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0min 10min 20min 30min 40min 50min
A
7:00 AM
13:00 PM
Transpiration Efficiency at 10 AM
Transpiration Efficiency on May 25th
Transportation Efficiency
Transpiration Efficiency (Te)
We believe the methodology is a good first order
algorithm to estimate the total water used for irrigation in dry
lands. However, it still needs further refinements and
validations. Most importantly it is amenable to satellite data
and can be expanded to compute global estimates of
irrigation water.
We will refine the model estimate of the minimum
physiological water requirement for observed agricultures
and further develop it to quantify the total amount of water
used for irrigation, including losses to transport and
transpiration efficiencies, soil salinity and delivery methods.
The transport efficiencies vary with farming practices and
regions and will be determined through field campaigns. We
will validate the methodology over multiple local sites over
the different regions and apply the validated algorithms over
large scale agricultural areas in the arid and semi-arid
regions.
The following is a sample field campaign report
collected from a country in North Africa (Algeria). This
report can provide detailed information of crop types and
density, soil salinity, crop phenology as well as irrigation type
and amount.
Temperature: 15C -25C
Soil salinity: 2-3 mmhos/cm
PH: 5.5-6.8
• Emergence
• Flowering
• maturation
Phenology
Temperature: 15-21C
Soil salinity: 3-5 mmhos/cm
PH: 5.8-6.5
• Emergence
• Flowering
• maturation
Phenology
Temperature: 18C -27C
Soil salinity: 3-5 mmhos/cm
PH: 5.6-6.9
• Emergence
• Flowering
• Maturation
Phenology