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![Page 1: MODIS Net Primary Productivity (NPP) Theory, algorithm development, and example applications Peter E. Thornton Numerical Terradynamic Simulation Group.](https://reader035.fdocuments.in/reader035/viewer/2022070413/5697bfe21a28abf838cb4623/html5/thumbnails/1.jpg)
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
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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)
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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
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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
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Radiation Productionx =
Radiation Use Efficiency ()
(MJ m-2 day-1) (gC m-2 day-1)(gC MJ-1)
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Absorbed Photosynthetically Active Radiation (APAR)
depends on incident PAR and canopy cover...
Incident Photosynthetically Active Radiation
(PAR)
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Absorbed Photosynthetically Active Radiation (APAR)
depends on incident PAR and canopy cover...
Absorbed Photosynthetically Active Radiation
(APAR)
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Absorbed Photosynthetically Active Radiation (APAR)
depends on incident PAR and canopy cover...
APAR
PAR= FPAR
Fraction of Photosynthetically Active Radiation absorbed by the
canopy
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Absorbed Photosynthetically Active Radiation (APAR)
depends on incident PAR and canopy cover...
APAR = PAR FPAR
Depends on weather
Depends on canopy
structure
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• 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
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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
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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
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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
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Example of detailed ecophysiological parameterization
10° C
18° C
28° C Rubisco limited
RuBP regenlimited
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Global Biome-BGC simulations
1 km Landcover from “continuous fields” AVHRR product:
Ruth DeFries and Matt Hansen, University of Maryland
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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
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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
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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
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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
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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
-
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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)
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Example MODIS-NPP output
Subset of results from first global implementation of the algorithm
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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
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Example application using Daymet surface weather inputs
Western Montana, northern Idaho, eastern Oregon and Washington,
USA
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