INTERNATIONAL INSTITUE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

Modeling the Biosphere from Space

for Food Security

FoodSat in West-Africa

Objectives

Quantifying Production Levels: What (is grown)? Where (is it grown)?, and How (much is grown)?

With specific attention for: Biosphere & atmosphere interaction

(e.g CO2 assimilation) Timeliness (provide input into Land

Use Planning) Spatial explicitness

Methods and tools

Satellite imagery Geostationary: Meteosat-8 (archive

since launch), older weather satellites Polar-orbiters: MODIS, ASTER, etc.

Systems simulation: Agro-biotic models Plant functions Scaling matters!

Distributed data services: discovery and remote access

object oriented classification techniques for urban land-use mapping

What (is grown)?

May.Jun.Jul.’01

Nov.Dec.Jan

Aug.Sep.Oct.

Feb.Mar.Apr. ‘02

SPOT-4 VEGETATION decadal NDVI data over India at 1km2

resolution - April 1998 to April 2002 (147 images in all)

Where (is it grown)?

50

100

150

200

Feb-Mar-Apr’02 Nov-Dec-Jan’01-02 Aug-Sep-Oct’01 May-Jun-Jul’01

Where (is it grown)? (cont.)

50

100

150

200

Jun Ju

lAug Sep Oct Nov

Dec Jan

Feb Mar Apr

May

Water

Rabi Rice

Clo

ud Clo

ud

Wasteland

What (is grown)? (Cont.)

crop calendar

NDVI profiles

Crop calendars andtemporal NDVI profiles for urban areas

NDVI Class

R2

Pulses 7 0.86

Cotton 8 0.91

Sorghum 11, 12 0.86

GroundNut 15 0.87

PaddyRice 17 0.91

p

0.006

0.003

0.007

0.005

0.005

Where (is it grown)? (cont.)

First results in India

Knowing the future now: coupled crop-weather forecast models (teleconnections) Zimbabwe: explains more than 65%

Is the an African Oscillation?

Note! Forecast 2 wks before start growing

season

How (much will be harvested)?

How (much is grown)?

Modeling a Farmers’ reality

Challenge: making a complex world simple

How (much is grown)? cont.

A simple production function:

gains losses

ΔT < 0

ΔT > 0

Figure 4. Generic AMAX-to-temperature response curves (Versteeg and van Keulen, 1986). Legend: I = C3 crops in cool and temperate climates; II = C3 crops in warm climates; III = C4 crops in warm climates; IV = C4 crops in cool climates.

P,Y=(radiation, temperature, C3/C4, canopy heating)

How (much is grown)? cont.

Daily totals or daily averages are sufficient for describing some processes, but for others like CO2 assimilation:• the response is non-linear;• some factors interact and

enhance each other's effects. This can be partly solved by

modeling the diurnal progress of the weather conditions

Modeling the gains; radiation and temperature, and their influence on CO2 assimilation

How (much is grown)? cont.

Modeling the gains; temporal dynamics in radiation and its influence on CO2 assimilation

Daily totals or daily averages are insufficient

Geostationary satellites (MSG-1/2, Insat 3D, GMS-5, FY-2C) are the only source for diurnal observations

Radiation (J m-2 s-1)

0

100

200

300

400

500

600

700

800

900

1000

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Time

Modeled

Observed

[MJ/m2/d]Year Day Hour PAR:RgLAI Rg AMAX inst daily diff

2001 152 19,2 0,46 1,6 15,9 40 421,4 420,8 0,62001 152 19,2 0,46 5 15,9 40 679,5 659,1 20,42001 153 19,2 0,46 1,6 9,1 40 353,2 323,9 29,42001 153 19,2 0,46 5 9,1 40 554,1 497,5 56,6

[kg CO2/ha/d]

How (much is grown)? cont.

Modeling the gains; temporal dynamics in radiation and its influence on CO2 assimilation

First results from Dano, Burkina Faso (validation against CI-340 photosynthesis measurements):

Modeled vs. observed CO2 assimilation of bottom layer leaves for a C3 crop, Dano,

Burkina Faso 12-09-05

R2 = 0.8411

0

200

400

600

800

0 200 400 600 800

modeled

observed (ugr/m2/s)

u

How (much is grown)? cont.

Modeling the losses; canopy heating as a proxy for crop stress

ΔT < 0

ΔT > 0

Plant temperatures increase following reduced transpiration rates, which may be caused by:• a deficit of water (water

stress),• reduction of the number of

conducting vessels (by disease or insects),• high salinity in the soil water,• nutrient deficiencies and

toxicities. Plant temperatures can be

estimated through Thermal Infrared Satellite Imagery

Mean temperature difference (O c), August 2003

Assessing regional H2O deficits in crops

How (much is grown)? cont.

DSS in water management

Irrigationstrategy

Harvestable gain

Reduced stress

How (much is grown)? cont.

Modeling Farmers’ reality: China

PS-n: P,Y = f(light, temperature, C3/C4, canopy heating) to capture multiple yield-reducing factors

climate Quzhou difficult: to few clear sky observations with polar-orbiters

Satellite sensed canopy temperature values from single vs. multiple platforms

Hypothesis: improve robustness of estimates of the duration and severity of crop stress periods

Methodology

Single-source SVAT model: heat flux model coupled with a crop growth simulation model

Temperature difference phenomenon (TEMPDIFF): yields so-called ‘coefficient of water sufficiency’ (cfH2O) from multiple sensors

Relational diagram

CONSTANTS /

INITIAL VALUES

SYNOPTIC DAILY

DATA

AVHRR

VIS, NIR

1 km 2 Sp.

rsl

1 km 2

rsl

PS-n

MODEL

Instantaneous

Tcanopy data

(1 km 2 Sprsl

)

Instantaneous

Tcanopy data

(1 km 2 Sprsl

)

Instantaneous

Tcanopy data

(1 km 2 Sprsl

) Instantaneou

s

Tcanopy maps

Instantaneous

data

(1 km 2 Sprsl

)

Instantaneous

data

(1 km 2 Sprsl

)

Instantaneous

data

(1 km 2

) Instantaneou

s

Offset maps

split- window

coefficients

Cloud Mask

AVHRR

VIS, NIR

algorithm

NDVI

Σ

VISSR T7

Water vapor

AVHRR/VISSR T11, T12

split- window

Data and materials

Tools: ILWIS RS/GIS package, Unix, Windows, shell scripting Crop growth model: PS-n (Production Situation)

Data (crop season of 1999): Satellite data for GMS-5 were routinely processed and archived

under the GAME/Tibet (GEWEX Asian Monsoon Experiment) project (Koike et al., 1999)

NOAA/AVHRR images, obtained from the NOAA Satellite Active Archive WWW-site

Meteorological data and yield statistics from the China Agricultural University, Beijing

Study area: The North China Plain consists of flat terrain at 40 m.a.s.l with

uniform, re-washed loess (loam) soils. Located in these plains uniform Land Use Systems (>250 sq. km) were selected where experimental maize fields were set-up, within the administrative district Quzhou, People’s Republic of China.

Study area

Canopy temperatures over Quzhou, P.R. of China:

Figure 2. Instrument response curve for NOAA-14/AVHRR and

GMS-5/VISSR (after Yuichiroh, 2004).

Results

Combining multi-sensor data to capture the dynamics of a system:

an example for NOAA-14/AVHRR and GMS-5/VISSR

R2 = 0.86

n = 37

290.0

295.0

300.0

305.0

310.0

315.0

320.0

325.0

290.0

295.0

300.0

305.0

310.0

315.0

320.0

325.0

To NOAA-14/AVHRR (K)

To G

MS

-5/V

ISS

R (

K)

Figure 4. Scatter plot of estimated canopy temperature for NOAA-14/AVHRR and GMS-5/VISSR.

Results

Dry matter growth curves

Above: NOAA data aloneBelow: NOAA/GMS-5

combined

Duration first stress period:2 (1a) days instead of 6 (1b)

as indicated on the graphlower part of the Figure

1b 2b

1a 2a

Results

SOM (Storage Organ Mass) values can be determined from the new method with a higher degree of certainty as compared to the existing method

Validation: evaluation against SOM values as observed (8453 kg ha-1) at the experimental maize fields, the estimates are within an accuracy of about 150 kg ha-1, a relative error of less than 1.8% (from 250 kg ha-1)

To explain temporal dynamics of crop stress: also use observations from geo-stationary satellites as an additional data source because of their higher temporal resolution

Our Distributed Data Services

Distributed Data Services

Radiometric and atmospheric corrected reflectivity/albedo’s from Meteosat-8/SEVIRI over Dano, BF:

see ftp://ftp.itc.nl/pub/venus/Dano_View/ for the full animation entitled “example animation IDV Meteosat-8 HRV - 01.mov”

Questions&Answers