MODIS Sensor Data For Crop Monitoring

MODIS Sensor Data For Crop MonitoringMODIS Sensor Data For Crop Monitoring

Guilherme Martin Torres

The biggest producer of sugar and alcohol in Brazil and one of the biggest in the worldThe biggest company to export alcohol and the second in sugar in the world

The companyGroup COSAN

605 thousand Hectares

18 producing unities

2 refineries

2 portuary terminals

43 thousand employees

Grinding more than 40 millon tons of sugar cane, produce 3.1 millon tons of sugar

and 1,5 billon liters of alcohol.

Usina Ipaussu,Ipaussu (SP)

Usina Diamante,Jaú (SP)

Usina Barra,Barra Bonita (SP)

Usina Dois Córregos,Dois Córregos (SP)

Usina Costa Pinto,Piracicaba (SP)

Usina Santa Helena,Rio das Pedras (SP)

Usina São Francisco,Elias Fausto (SP)

Usina Rafard,Rafard (SP)

Usina Bom Retiro,Capivari (SP)

Usina Serra,Ibaté (SP)

Usina Junqueira,Igarapava (SP)

Usina Bonfim,Guariba (SP)

Usina Tamoio,Araraquara (SP)

UnitiesGroup COSAN

Usina Univalem,Valparaíso (SP)

Usina Gasa,Andradina (SP)

Usina Destivale,Araçatuba (SP)

Usina Mundial,Mirandópolis (SP)

Usina BenálcoolBento de Abreu (SP)

Unities spacial distributionGroup COSAN

Spectral analysis

Soil cover classification

Elaboration of thematic maps

Supervize biometrics measurements

Developed ActivitiesGroup COSAN

Application of MODIS spectral data to:

a) Monitoring sugar cane development through the season.

b) Relation to bio-physics parameters.

c) Relation with yield.

ObjectivesProject

Brazil`s Sugar and Alcohol Agribusiness Importance Introduction

Moves: $ 19 billion

Generate: 4 millon jobs

Involves: 72 mil farmers

Process: 420 millon tons of sugar cane

Produce: 30 millon tons of sugar and 17 billon liters of alcohol

Export: 19 millon tons of sugar and 3 billon liters of alcohol

Collect: $ 6 billon in taxes and fees

Invest: $ 3 billon per year

About 85% of Brazil`s sugar and ethanol production is concentrated at the Center-

South region.

Brasil is leader on sugar and ethanol export

Source: PROCANA – SF 2006/2007

General CharacteristicsSugar Cane

Semi-perene

C4 Plant

Optimun Temperature: 71 to 86°F

Sugar acummulation in the stem

Latitudes 35°N to 30°S

Sugar, alcohol and eletric energy

CO2 absortion

Solar radiation

Photoperiod: 10 to 14 hours

LAI

colheita

amadurecimento

Crescimentovegetativo

Source: Adapted from ALFONSI et al. (1987).

Sugar CaneEvolutive Cycle

1 year crop

1.5 year crop

Planting Veg. growth Maturation Harvest

Maturation

HarvestVeg. growthReduced growth

ARM

Water Balance, Evapotranspiration And The Effect On Plant GrowthWater balance: what it is?

Rain

Capilar Movement Drainage

Evapotranspiration

Source: File from class – Meteorologia Agrícola – Prof. Sentelhas

Effect of water defict:

Limits Foliar area

Limits number of leaves

Reduce new leaves emission

Foliar Abscission

Reduce size and growth

Reduce yield

Composition, morphology and internal structure

Health aspects

Climatics conditions

Genetics characteristics

Spectral BehaviorLeaves

Pigments contentand physiologic structure

Age and maturation

Leaf Thickness

Senescence

Spectral Behavior

Spectral profile of a typical healthy green leaf.

Leaves

Source: Adapted from Swain, P.H. and Davis S.M (1978)

Chlorophyll AbsorptionWater Absorption

Reflectance curve of corn leaves with different water contents.

Spectral BehaviorLeaves

Source: Material from Comportamento Espectral de Alvos, INPE (2002).

Wave lenth

Instruments aboard of Terra satellite (1999 ) and Aqua satellite (2002) (NASA).

Objective: continued global monitoring of the earth surface.

Range of spatial coverage: 2.330 km of width

Atmospheric corrections and image georreferencing

Spectral resolution:

Bands 1-7 : terrestrial applications;

Bands 8-16 : oceanics observations

Bandas 20-36 (exception to band 26) : spectral termal portion

Characteristics MODIS (Moderate Resolution Imaging Spectroradiometer)

Spacial Resolution : 250m 500m and 1km

Temporal Resolution : revisit time

Modis (Moderate Resolution Imaging Spectroradiometer)Características

source: NASA website.

Fonte: Adaptada de Schowengerdt (1997).

Resolution (m)BlueGreenRedNIRMIR

Interval (days)System Passage at the Equador

Optimize the vegetation signal

Higher sensibility in regions with dense biomass

Reduction from atmospheric influencies

Sensible to varitaions at the canopy structre, LAI

L is a soil adjust factor; C1 and C2 are coefficients to adjust the effect of atmopheric aerosols

EVI (Enhanced Vegetation Index)

Images from MODIS sensor : resolution 250m, frequency 16 days

Central Coordenates: 25°09’ S and 49°76’ W (tile h13v11) - SP

Period : january 2004 to march 2008

Projection system: UTM, datum WGS 84

Softwares :

TNT mips

Ldope (QA)

MRT ( Modis Reprojection Tool)

Criation of masks : clouds and cities

Extraction of spectral data

Images Treatment Materials and Methods

Pixel Reliability.

VI Quality

Material and MethodsQuality Assurance

4 meso-regions

Araraquara, Araçatuba, Jaú and Piracicaba

Meteorological data:

Unesp/ Ilha Solteira

Unesp / Jaboticabal

Instituto Agronômico de Campinas / Jaú

Esalq / USP

Calculate evapotranspiration : Penman-Monteith and Thornthwaite

Studied area : cultivated sugar cane in São Paulo State

Materials and MethodsStudy #1

Area of sugar cane cultivated in

São Paulo in 2007.


Fonte : CanaSat (INPE)

Vectors from 12 meso-regions over a EVI map using MODIS sensor


Study #1 Materials and Methods

Area of interest : regions of Araçatuba, Araraquara, Jaú and Piracicaba

Comercial fields from the regions of Piracicaba, Jaú and Araçatuba

Unities :

Costa Pinto,Bom Retiro, Santa Helena, Rafard, São Francisco

Barra, Dois Córregos, Diamante,

Destivale, Mundial, Gasa e Univalem

Biometrics: parameters (stem and cabbage lenth and weight, number of plant/m)

Ton/ha (TCH – Biometrics)

Relation :

EVI vs. Yield

EVI * H cana vs. Yield

Materials and MethodosStudy #2

Study Area


Materials and MethodosStudy #2

Interest area in details: own sugar cane fields – region of Araçatuba, Jaú and Piracicaba

Crop year : 2004/2005, 2005/2006, 2006/2007 and 2007/2008

2000

2500

3000

3500

4000

4500

5000

5500

6000

EV

I

Data

Região de Araçatuba

2004 2005 2006 2007 2008

2000250030003500400045005000550060006500

EV

I

Data

Região de Araraquara

2004 2005 2006 2007 2008

2000

2500

3000

3500

4000

4500

5000

5500

6000

EV

I

Data

Região de Jaú

2004 2005 2006 2007 2008

2000

2500

3000

3500

4000

4500

5000

5500

6000

EV

I

Data

Região de Piracicaba

2004 2005 2006 2007 2008

Results and discutionStudy# 1 – Multi-temporal spectral analysis

-2000

-1000

0

1000

2000

3000

4000

5000

6000

-50

0

50

100

150

1-ja

n

17-j

an

2-fe

v

18-f

ev

6-m

ar

22-m

ar

7-a

br

23-a

br

9-m

ai

25-m

ai

10-j

un

26-j

un

12-j

ul

28-j

ul

14-a

go

30-a

go

16-s

et

1-o

ut

17-o

ut

2-n

ov

18-n

ov

4-d

ez

20-d

ez

1-ja

n

17-j

an

2-fe

v

18-f

ev

6-m

ar

22-m

ar

7-a

br

23-a

br

9-m

ai

25-m

ai

10-j

un

26-j

un

12-j

ul

28-j

ul

14-a

go

30-a

go

16-s

et

1-o

ut

17-o

ut

2-n

ov

18-n

ov

4-d

ez

20-d

ez

mm

Balanço hídrico x EVI - 2004 a 2005 Excedente hídrico Deficiência hídrica EVI

-2000

-1000

0

1000

2000

3000

4000

5000

6000

-50

0

50

100

150

1-ja

n

17-j

an

2-fe

v

18-f

ev

6-m

ar

22-m

ar

7-a

br

23-a

br

9-m

ai

25-m

ai

10-j

un

26-j

un

12-j

ul

28-j

ul

14-a

go

30-a

go

16-s

et

1-o

ut

17-o

ut

2-n

ov

18-n

ov

4-d

ez

20-d

ez

1-ja

n

17-j

an

2-fe

v

18-f

ev

6-m

ar

22-m

ar

7-a

br

23-a

br

9-m

ai

25-m

ai

10-j

un

26-j

un

12-j

ul

28-j

ul

14-a

go

30-a

go

16-s

et

1-o

ut

17-o

ut

2-n

ov

18-n

ov

4-d

ez

20-d

ez

mm


-2000

-1000

0

1000

2000

3000

4000

5000

6000

-50

0

50

100

150

1-ja

n

17-j

an

2-fe

v

18-f

ev

6-m

ar

22-m

ar

7-a

br

23-a

br

9-m

ai

25-m

ai

10-j

un

26-j

un

12-j

ul

28-j

ul

14-a

go

30-a

go

16-s

et

1-o

ut

17-o

ut

2-n

ov

18-n

ov

4-d

ez

20-d

ez

1-ja

n

17-j

an

2-fe

v

18-f

ev

6-m

ar

22-m

ar

7-a

br

23-a

br

9-m

ai

25-m

ai

10-j

un

26-j

un

12-j

ul

28-j

ul

14-a

go

30-a

go

16-s

et

1-o

ut

17-o

ut

2-n

ov

18-n

ov

4-d

ez

20-d

ez

mm


-2000

-1000

0

1000

2000

3000

4000

5000

6000

-50

0

50

100

150

1-ja

n

17-j

an

2-fe

v

18-f

ev

6-m

ar

22-m

ar

7-a

br

23-a

br

9-m

ai

25-m

ai

10-j

un

26-j

un

12-j

ul

28-j

ul

14-a

go

30-a

go

16-s

et

1-o

ut

17-o

ut

2-n

ov

18-n

ov

4-d

ez

20-d

ez

1-ja

n

17-j

an

2-fe

v

18-f

ev

6-m

ar

22-m

ar

7-a

br

23-a

br

9-m

ai

25-m

ai

10-j

un

26-j

un

12-j

ul

28-j

ul

14-a

go

30-a

go

16-s

et

1-o

ut

17-o

ut

2-n

ov

18-n

ov

4-d

ez

20-d

ez

mm


Relation EVI vs. Water Balance.

Factors affecting EVI

Cultivation effect

Maturation

Increasing number of senescent leaves

Increasing straw residue

Lower photossinthetic activity

Period of low precipitaion during

winter (April to August)

y = 0,002x - 35,57R² = 0,926

0

20

40

60

80

100

120

140

30000 40000 50000 60000 70000 80000

TCH

Bio

met

ria

EVI acumulado

Piracicaba - EVI x TCH Biometria

y = 5E-06x + 17,68R² = 0,892

0

20

40

60

80

100

120

140

500000 5500000 10500000 15500000 20500000 25500000

TCH

Bio

met

ria

EVI *h

Piracicaba - EVI*h x TCH Biometria

y = 4E-06x + 25,59R² = 0,590

0

20

40

60

80

100

120

500000 5500000 10500000 15500000 20500000

TCH

Bio

met

ria

EVI *h

Araçatuba - EVI*h x TCH Biometria

Resultados e discussionStudy #2 – EVI, EVI*H and yield

y = 3E-06x + 48,57R² = 0,616

0

20

40

60

80

100

120

500000 5500000 10500000 15500000 20500000 25500000

TCH

Biom

etria

EVI *h

Jaú - EVI*h x TCH Biometria

y = 0,001x + 27,75R² = 0,665

0

20

40

60

80

100

120

25000 35000 45000 55000 65000 75000

TCH

Biom

etria

EVI acumulado

Jaú - EVI x TCH Biometria

EVI

y = 0,001x - 5,123R² = 0,444

0

20

40

60

80

100

120

30000 35000 40000 45000 50000 55000 60000 65000 70000

TCH

Biom

etria

EVI acumulado

Araçatuba - EVI x TCH Biometria

EVI

EVI

y = 0,001x + 9,515R² = 0,600

20

40

60

80

100

120

140

20000 30000 40000 50000 60000 70000 80000

TCH

EVI acumulado

EVI acumulado x produtividade

y = 4E-06x + 34,40R² = 0,673

0

20

40

60

80

100

120

140

0 5000000 10000000 15000000 20000000 25000000

TCH

EVI*h cana

EVI*h cana x produtividade

Relation between EVI and EVI*height and yield for all data collected.

Results and discussionStudy# 2 – EVI, EVI*H and yield

Correlation between spectral data and sugar cane yield

Monitoring the crop evoluion on a regional scale

Satisfactories R² (EVI vs. Productivity)

Reduced number of samples

Limited pixel resolution

Influence of other types of vegetation and others elements present at the images

Revisit period

Clouds

Cost = zero

Conclusion

Obrigado

MODIS Sensor Data For Crop Monitoring

Documents

Transcript of MODIS Sensor Data For Crop Monitoring