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: Lahouari.Bounoua-1@nasa.gov

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