MODISNet Primary Productivity (NPP)

Theory, algorithm development, and example applications

Peter E. Thornton

Numerical Terradynamic Simulation Group

School of Forestry, University of Montana, Missoula, MT

Outline

• Background and theory (radiation use efficiency)

• Parameterization (global simulations with Biome-

BGC)

• Example applications (regional, continental, and

global)

• Future developments (meteorology, landcover,

complete carbon budget)

MODIS-NPP Objectives

• Global estimate of productivity each week at 1km resolution

• Algorithm driven mainly by remote sensing inputs

• Include biophysical variables that can be produced globally at appropriate resolution

• Biome-specific parameterization

MODIS-NPPProduction Algorithm Summary

• Incident radiation (PAR)...

• Scaled by canopy cover (FPAR)...

• Converted to carbon (radiation use efficiency)...

• Modified by temperature and humidity...

• Different parameters for each landcover

Radiation Productionx =

Radiation Use Efficiency ()

(MJ m-2 day-1) (gC m-2 day-1)(gC MJ-1)

Absorbed Photosynthetically Active Radiation (APAR)

depends on incident PAR and canopy cover...

Incident Photosynthetically Active Radiation

(PAR)

Absorbed Photosynthetically Active Radiation (APAR)

depends on incident PAR and canopy cover...

Absorbed Photosynthetically Active Radiation

(APAR)

Absorbed Photosynthetically Active Radiation (APAR)

depends on incident PAR and canopy cover...

APAR

PAR= FPAR

Fraction of Photosynthetically Active Radiation absorbed by the

canopy

Absorbed Photosynthetically Active Radiation (APAR)

depends on incident PAR and canopy cover...

APAR = PAR FPAR

Depends on weather

Depends on canopy

structure

• Low air temperature

(Tair)

• High vapor pressure

deficit (VPD)

Potential Radiation Use Efficiency (max)

is modified by biophysical environment...

Reductions due to...

max STair SVPD

0

1

1

0

STair

SVPD

T2T1

VPD2VPD1

Gross Primary Production (GPP) Algorithm:

GPP = PAR FPAR max STair SVPD

Depends on…

• MODIS-FPAR

• PAR, air temperature, and VPD (from DAO)

• Parameters defined for each vegetation type

• MODIS Landcover

MODIS-GPPBiome-specific parameterization

• All parameters are derived from global-

scale simulations using the Biome-BGC

terrestrial ecosystem process model

• Detailed landcover information is used to

translate Biome-BGC results to aggregated

MODIS landcover classes

Fractional veg coverSoils

Daily weather data

Biome-BGCGPP, NPP, g s, etc

LAI / fPAR

Translation codeDaily weather

data

Biome PropertiesLook-up Table

(BPLUT)

MOD-17LAI / fPARLandcover

DAO daily surface weather

Daily NPPAnnual NPP

Biome-BGC MOD-17

AlgorithmCalibration

Example of detailed ecophysiological parameterization

10° C

18° C

28° C Rubisco limited

RuBP regenlimited

Global Biome-BGC simulations

1 km Landcover from “continuous fields” AVHRR product:

Ruth DeFries and Matt Hansen, University of Maryland

1 kmspectral data

woody

ENF

DBF

EBF

C3

C4

gra

ss

ENF DBF EBF C3C4

Biome-BGC

outputs(GPP, NPP,

etc.)

inputs(ecophysiology,

climate,etc.)

input remotesensing data

first split betweenwoody/nonwoody

fractional cover forfundamental types

process model

analysisby type weighted average for

gridcellor

Use of fractional vegetation cover in Biome-BGC

ENF DBF EBF C4 C3

Global Biome-BGC simulations

1x1 degree simulations for 14 years driven with daily weather data from

Steve Piper and C.D. Keeling, Scripps Institute of Oceanography

Fractional veg coverSoils

Daily weather data

Biome-BGCGPP, NPP, g s, etc

LAI / fPAR

Translation codeDaily weather

data

Biome PropertiesLook-up Table

(BPLUT)

MOD-17LAI / fPARLandcover

DAO daily surface weather

Daily NPPAnnual NPP

Biome-BGC MOD-17

AlgorithmCalibration

Several more steps to go from GPP to NPP...

• Maintenance respiration costs - depend on

tissue N concentration and temperature

• Growth respiration costs - depend on

amount of new growth

• Allometric relationships relate annual leaf

area growth to stem and root growth

maxTmin , VPD

GPP

LAI SLA

fine rootmass

allometry

leaf mass

Q10, Tavg MR

DailyNPP*

MRindex

MOD-17Daily NPP*

Photosynthesis

Maintenance Respiration

*does not include growth respiration orlive wood maintenance respiration costs

leafmass

Daily Outputs

FPAR Rnet

PAR x

-

Leafmass

MRindex

DailyNPP*

max

Annual sumDaily NPP*

Annual sumMR index

allometryAnnual averagelive wood mass

MR scalarAnnual sum

live wood MR

leaflongevity

Annualleaf growth

allometryAnnual

fine root andwood growth

GR scalarAnnual sum

GR

AnnualNPP

-

-

Annual maxleaf mass

MOD-17Annual NPP

MOD-17 Daily Outputs (Annual Inputs)

Example MODIS-NPP output

Subset of results from first global implementation of the algorithm

Some problems that we know about...

• Coarse resolution surface weather data from DAO leaves a noticeable imprint on weekly output (probably on annual output also)

• Use of discreet landcover makes parameterization from Biome-BGC difficult

• Geographic variation of parameters within biomes

Example application using Daymet surface weather inputs

Western Montana, northern Idaho, eastern Oregon and Washington,

USA