MODIS Net 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-NPP Production 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

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

Incident Photosynthetically Active Radiation (PAR) Absorbed Photosynthetically Active Radiation (APAR) depends on incident PAR and canopy cover... Incident Photosynthetically Active Radiation (PAR)

Absorbed Photosynthetically Active Radiation (APAR) Absorbed Photosynthetically Active Radiation (APAR) depends on incident PAR and canopy cover... Absorbed Photosynthetically Active Radiation (APAR)

Fraction of Photosynthetically Active Radiation absorbed by the canopy Absorbed Photosynthetically Active Radiation (APAR) depends on incident PAR and canopy cover... Fraction of Photosynthetically Active Radiation absorbed by the canopy APAR PAR = FPAR

Depends on canopy structure Absorbed Photosynthetically Active Radiation (APAR) depends on incident PAR and canopy cover... Depends on weather APAR = PAR × FPAR Depends on canopy structure

Potential Radiation Use Efficiency (emax) is modified by biophysical environment... emax × STair × SVPD = e Reductions due to... 1 STair SVPD T2 T1 VPD2 VPD1 Low air temperature (Tair) High vapor pressure deficit (VPD)

Gross Primary Production (GPP) Algorithm: GPP = PAR × FPAR × emax × STair × SVPD Depends on… MODIS-FPAR PAR, air temperature, and VPD (from DAO) Parameters defined for each vegetation type MODIS Landcover

MODIS-GPP Biome-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

Example of detailed ecophysiological parameterization Rubisco limited 28° C RuBP regen limited 18° C 10° C

Global Biome-BGC simulations 1 km Landcover from “continuous fields” AVHRR product: Ruth DeFries and Matt Hansen, University of Maryland

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

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

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