26 May 2015

Towards the simulation of global crop productivity in

IMPACT with geospatial data

Michael MarshallWorld Agroforestry CentreUnited Nations Ave Gigiri

PO Box 30677-00100Nairobi Kenya

+254207224244mmarshallcgiarorg

Global Crop Model Approaches

Local-scale models (eg DSSAT and APSIM) aggregated via multiple-site calibration and averagingminus Complexminus High-input data requirementminus Locally accurateminus Site specificity (over-fitting)minus Non-linearity

Large-area models (eg GLOPEM and GLAM)minus Simpleminus Low-input data requirementminus Locally less accurateminus Generalizable

ldquoEssentially all models are wrong but some are usefulrdquo

Global crop model for IMPACT

Opti-LU

Opti-WU

εmax=006 molmol-1 (C4)

Model Calibration Sensitivity Analysis and Validation

Identify most important parameters remove redundant or insignificant parameters and potentially integrate new parametersminus Model performance statisticsminus Monte Carlo Simulationminus Residual analysis

Multiple scales of validationminus Plot (eddy covariance flux tower data)minus Field (non-destructive biomass transects)minus Landscape rarr global (high moderate and

coarse resolution remote sensing)

Eddy Covariance Flux Towers

Three towers (2009-2014) Alfalfa rice and

citrus orchard 30-minute energy

balance and meteorological data

FAPAR (MODIS subset tool httpdaacornlgov)

Daily GPP RECO and NPP

Transects

60 m

10 quadrats per frame (2011-2012) Alfalfa cotton

maize and rice

Biophysical data

Ground spectra

Destructive aboveground wet biomass

Empirical (non-destructive) model-building

CIMIS

1

2

3

5

4

6

10

7

8

9

Marshall and Thenkabail (2015)

Tasseled Cap Transformation

Weighted linear combinations of satellite reflectanceminus Brightnessminus Greennessminus Wetness

Fraction of vegetation bare soil and water

Density

Kauth and Thomas (1976)

Index of Agreement (yellow = perfect match)

Biomass

Dark Soil

Light Soil

PlantsPlants

Reference Data

AVHRR (5 km) MODIS (500 m) Landsat (30 m) IKONOS (4 m) WorldView (185 m) and GeoEye

(165 m)

Tasseled Cap

Biomass Estimation for Validation at Remote Sensing Scales

Muchow (1990)

GPP rarr Crop Yield

Prince et al (2001) expressed yield in terms of GPP

Where SOS is start of season EOS is end of season RS is the root-to-shoot ratio HI is the harvest index and m0 is grain moisture content

RS HI and m0 are constant by crop type

(Xin et al 2013)

OR

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Global Crop Model Approaches

Local-scale models (eg DSSAT and APSIM) aggregated via multiple-site calibration and averagingminus Complexminus High-input data requirementminus Locally accurateminus Site specificity (over-fitting)minus Non-linearity

Large-area models (eg GLOPEM and GLAM)minus Simpleminus Low-input data requirementminus Locally less accurateminus Generalizable

ldquoEssentially all models are wrong but some are usefulrdquo

Global crop model for IMPACT

Opti-LU

Opti-WU

εmax=006 molmol-1 (C4)

Model Calibration Sensitivity Analysis and Validation

Identify most important parameters remove redundant or insignificant parameters and potentially integrate new parametersminus Model performance statisticsminus Monte Carlo Simulationminus Residual analysis

Multiple scales of validationminus Plot (eddy covariance flux tower data)minus Field (non-destructive biomass transects)minus Landscape rarr global (high moderate and

coarse resolution remote sensing)

Eddy Covariance Flux Towers

Three towers (2009-2014) Alfalfa rice and

citrus orchard 30-minute energy

balance and meteorological data

FAPAR (MODIS subset tool httpdaacornlgov)

Daily GPP RECO and NPP

Transects

60 m

10 quadrats per frame (2011-2012) Alfalfa cotton

maize and rice

Biophysical data

Ground spectra

Destructive aboveground wet biomass

Empirical (non-destructive) model-building

CIMIS

1

2

3

5

4

6

10

7

8

9

Marshall and Thenkabail (2015)

Tasseled Cap Transformation

Weighted linear combinations of satellite reflectanceminus Brightnessminus Greennessminus Wetness

Fraction of vegetation bare soil and water

Density

Kauth and Thomas (1976)

Index of Agreement (yellow = perfect match)

Biomass

Dark Soil

Light Soil

PlantsPlants

Reference Data

AVHRR (5 km) MODIS (500 m) Landsat (30 m) IKONOS (4 m) WorldView (185 m) and GeoEye

(165 m)

Tasseled Cap

Biomass Estimation for Validation at Remote Sensing Scales

Muchow (1990)

GPP rarr Crop Yield

Prince et al (2001) expressed yield in terms of GPP

Where SOS is start of season EOS is end of season RS is the root-to-shoot ratio HI is the harvest index and m0 is grain moisture content

RS HI and m0 are constant by crop type

(Xin et al 2013)

OR

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Global crop model for IMPACT

Opti-LU

Opti-WU

εmax=006 molmol-1 (C4)

Model Calibration Sensitivity Analysis and Validation

Identify most important parameters remove redundant or insignificant parameters and potentially integrate new parametersminus Model performance statisticsminus Monte Carlo Simulationminus Residual analysis

Multiple scales of validationminus Plot (eddy covariance flux tower data)minus Field (non-destructive biomass transects)minus Landscape rarr global (high moderate and

coarse resolution remote sensing)

Eddy Covariance Flux Towers

Three towers (2009-2014) Alfalfa rice and

citrus orchard 30-minute energy

balance and meteorological data

FAPAR (MODIS subset tool httpdaacornlgov)

Daily GPP RECO and NPP

Transects

60 m

10 quadrats per frame (2011-2012) Alfalfa cotton

maize and rice

Biophysical data

Ground spectra

Destructive aboveground wet biomass

Empirical (non-destructive) model-building

CIMIS

1

2

3

5

4

6

10

7

8

9

Marshall and Thenkabail (2015)

Tasseled Cap Transformation

Weighted linear combinations of satellite reflectanceminus Brightnessminus Greennessminus Wetness

Fraction of vegetation bare soil and water

Density

Kauth and Thomas (1976)

Index of Agreement (yellow = perfect match)

Biomass

Dark Soil

Light Soil

PlantsPlants

Reference Data

AVHRR (5 km) MODIS (500 m) Landsat (30 m) IKONOS (4 m) WorldView (185 m) and GeoEye

(165 m)

Tasseled Cap

Biomass Estimation for Validation at Remote Sensing Scales

Muchow (1990)

GPP rarr Crop Yield

Prince et al (2001) expressed yield in terms of GPP

Where SOS is start of season EOS is end of season RS is the root-to-shoot ratio HI is the harvest index and m0 is grain moisture content

RS HI and m0 are constant by crop type

(Xin et al 2013)

OR

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Model Calibration Sensitivity Analysis and Validation

Identify most important parameters remove redundant or insignificant parameters and potentially integrate new parametersminus Model performance statisticsminus Monte Carlo Simulationminus Residual analysis

Multiple scales of validationminus Plot (eddy covariance flux tower data)minus Field (non-destructive biomass transects)minus Landscape rarr global (high moderate and

coarse resolution remote sensing)

Eddy Covariance Flux Towers

Three towers (2009-2014) Alfalfa rice and

citrus orchard 30-minute energy

balance and meteorological data

FAPAR (MODIS subset tool httpdaacornlgov)

Daily GPP RECO and NPP

Transects

60 m

10 quadrats per frame (2011-2012) Alfalfa cotton

maize and rice

Biophysical data

Ground spectra

Destructive aboveground wet biomass

Empirical (non-destructive) model-building

CIMIS

1

2

3

5

4

6

10

7

8

9

Marshall and Thenkabail (2015)

Tasseled Cap Transformation

Weighted linear combinations of satellite reflectanceminus Brightnessminus Greennessminus Wetness

Fraction of vegetation bare soil and water

Density

Kauth and Thomas (1976)

Index of Agreement (yellow = perfect match)

Biomass

Dark Soil

Light Soil

PlantsPlants

Reference Data

AVHRR (5 km) MODIS (500 m) Landsat (30 m) IKONOS (4 m) WorldView (185 m) and GeoEye

(165 m)

Tasseled Cap

Biomass Estimation for Validation at Remote Sensing Scales

Muchow (1990)

GPP rarr Crop Yield

Prince et al (2001) expressed yield in terms of GPP

Where SOS is start of season EOS is end of season RS is the root-to-shoot ratio HI is the harvest index and m0 is grain moisture content

RS HI and m0 are constant by crop type

(Xin et al 2013)

OR

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Eddy Covariance Flux Towers

Three towers (2009-2014) Alfalfa rice and

citrus orchard 30-minute energy

balance and meteorological data

FAPAR (MODIS subset tool httpdaacornlgov)

Daily GPP RECO and NPP

Transects

60 m

10 quadrats per frame (2011-2012) Alfalfa cotton

maize and rice

Biophysical data

Ground spectra

Destructive aboveground wet biomass

Empirical (non-destructive) model-building

CIMIS

1

2

3

5

4

6

10

7

8

9

Marshall and Thenkabail (2015)

Tasseled Cap Transformation

Weighted linear combinations of satellite reflectanceminus Brightnessminus Greennessminus Wetness

Fraction of vegetation bare soil and water

Density

Kauth and Thomas (1976)

Index of Agreement (yellow = perfect match)

Biomass

Dark Soil

Light Soil

PlantsPlants

Reference Data

AVHRR (5 km) MODIS (500 m) Landsat (30 m) IKONOS (4 m) WorldView (185 m) and GeoEye

(165 m)

Tasseled Cap

Biomass Estimation for Validation at Remote Sensing Scales

Muchow (1990)

GPP rarr Crop Yield

Prince et al (2001) expressed yield in terms of GPP

Where SOS is start of season EOS is end of season RS is the root-to-shoot ratio HI is the harvest index and m0 is grain moisture content

RS HI and m0 are constant by crop type

(Xin et al 2013)

OR

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Transects

60 m

10 quadrats per frame (2011-2012) Alfalfa cotton

maize and rice

Biophysical data

Ground spectra

Destructive aboveground wet biomass

Empirical (non-destructive) model-building

CIMIS

1

2

3

5

4

6

10

7

8

9

Marshall and Thenkabail (2015)

Tasseled Cap Transformation

Weighted linear combinations of satellite reflectanceminus Brightnessminus Greennessminus Wetness

Fraction of vegetation bare soil and water

Density

Kauth and Thomas (1976)

Index of Agreement (yellow = perfect match)

Biomass

Dark Soil

Light Soil

PlantsPlants

Reference Data

AVHRR (5 km) MODIS (500 m) Landsat (30 m) IKONOS (4 m) WorldView (185 m) and GeoEye

(165 m)

Tasseled Cap

Biomass Estimation for Validation at Remote Sensing Scales

Muchow (1990)

GPP rarr Crop Yield

Prince et al (2001) expressed yield in terms of GPP

Where SOS is start of season EOS is end of season RS is the root-to-shoot ratio HI is the harvest index and m0 is grain moisture content

RS HI and m0 are constant by crop type

(Xin et al 2013)

OR

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Tasseled Cap Transformation

Weighted linear combinations of satellite reflectanceminus Brightnessminus Greennessminus Wetness

Fraction of vegetation bare soil and water

Density

Kauth and Thomas (1976)

Index of Agreement (yellow = perfect match)

Biomass

Dark Soil

Light Soil

PlantsPlants

Reference Data

AVHRR (5 km) MODIS (500 m) Landsat (30 m) IKONOS (4 m) WorldView (185 m) and GeoEye

(165 m)

Tasseled Cap

Biomass Estimation for Validation at Remote Sensing Scales

Muchow (1990)

GPP rarr Crop Yield

Prince et al (2001) expressed yield in terms of GPP

Where SOS is start of season EOS is end of season RS is the root-to-shoot ratio HI is the harvest index and m0 is grain moisture content

RS HI and m0 are constant by crop type

(Xin et al 2013)

OR

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Index of Agreement (yellow = perfect match)

Biomass

Dark Soil

Light Soil

PlantsPlants

Reference Data

AVHRR (5 km) MODIS (500 m) Landsat (30 m) IKONOS (4 m) WorldView (185 m) and GeoEye

(165 m)

Tasseled Cap

Biomass Estimation for Validation at Remote Sensing Scales

Muchow (1990)

GPP rarr Crop Yield

Prince et al (2001) expressed yield in terms of GPP

Where SOS is start of season EOS is end of season RS is the root-to-shoot ratio HI is the harvest index and m0 is grain moisture content

RS HI and m0 are constant by crop type

(Xin et al 2013)

OR

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Muchow (1990)

GPP rarr Crop Yield

Prince et al (2001) expressed yield in terms of GPP

Where SOS is start of season EOS is end of season RS is the root-to-shoot ratio HI is the harvest index and m0 is grain moisture content

RS HI and m0 are constant by crop type

(Xin et al 2013)

OR

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Need for systems simulation models in Agroforestry Systems

Lloyd et al (1990)

Agroforestry systems large payoffs but complex

Systems simulation minus Can handle

complex systemsminus Provide targeted

and effective intervention

minus Tunable for ldquofuturerdquo climates

Not widely used in SSA because of data scarcity

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

GPP and crop yield model validated at multiple spatial scales in California

minus Thomas Gumbricht MODIS-era GPP modeling tool for California

minus Patricia Masikate Agro-forestry module (APSIM) and estimates of RS HI and m0

minus Erick Okuto SOS and EOS simulation

Next year (2016) Global GPP and crop yield model development calibration and validation

minus 5 km resolution 1982 ndash 2014 (+30 years)

Summary

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You

Thank You

• Towards the simulation of global crop productivity in IMPACT wi
• Slide 2
• Global crop model for IMPACT
• Model Calibration Sensitivity Analysis and Validation
• Eddy Covariance Flux Towers
• Transects
• Tasseled Cap Transformation
• Slide 8
• Slide 9
• Slide 10
• Slide 11
• Thank You