Integrating Remote Sensing, Flux Measurements and Ecosystem Models

Integrating Remote Sensing, Integrating Remote Sensing, Flux Measurements and Flux Measurements and

Ecosystem ModelsEcosystem ModelsFaith Ann HeinschFaith Ann Heinsch

Numerical Terradynamic Simulation Group (NTSG)Numerical Terradynamic Simulation Group (NTSG)University of MontanaUniversity of Montana

NCAR ASP 2007 ColloquiumNCAR ASP 2007 ColloquiumRegional BiogeochemistryRegional Biogeochemistry

June 12, 2007June 12, 2007

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GPP = Light X Conversion Efficiency

GPP = f (PAR) X

VPD

Temperature

PAR

GPP

GPP = Light X Conversion Efficiency

GPP = f (PAR) X

VPD

Temperature

PAR

GPPGPP

MODIS GPP (MOD17)MODIS GPP (MOD17)

ε = εmax * m(Tmin) * m(VPD)

Stress Scalars for Light Use Efficiency

VPDTemperature

Light Use EfficiencyLight Use Efficiency

Land Cover (Land Cover (MOD12Q1MOD12Q1))– Biome TypeBiome Type– Annual, 1-kmAnnual, 1-km

8-Day FPAR/LAI (8-Day FPAR/LAI (MOD15A2MOD15A2))– FPAR and living biomassFPAR and living biomass– 8-day, 1-km8-day, 1-km

Daily Meteorological Data (Daily Meteorological Data (DAODAO))– Environmental conditionsEnvironmental conditions– Driving forcesDriving forces– Daily, 1.00Daily, 1.00 x 1.25 x 1.25

GPP/NPPGPP/NPP((MOD17A2/A3MOD17A2/A3))

Inputs to the MOD17 GPP/NPP AlgorithmInputs to the MOD17 GPP/NPP Algorithm

MOD17 BPLUT – v. 4.8MOD17 BPLUT – v. 4.8

MOD17 BPLUT – v. 4.8MOD17 BPLUT – v. 4.8

MODIS GPP MODIS GPP

Comparison of GPP from Terra-MODIS and Comparison of GPP from Terra-MODIS and AmeriFlux Network TowersAmeriFlux Network Towers

Biome types used in comparison: forests (evergreen needleleaf, deciduous broadleaf, and mixed species), oak savanna, grassland, tundra, and chaparral.

Calibration / Validation TestsCalibration / Validation Tests

VEGETATION: Forests, Grass, Shrubs, and Crops

CLIMATE: Cold-Dry, Cold-Wet, Warm-Dry, and Warm-Wet

GEOGRAPHIC PATTERNSGEOGRAPHIC PATTERNS

GROWING SEASON (Start and End)

STRESS (Mid-Summer Water Stress, ColdTemperatures, High Vapor Pressure Deficits)

SEASONAL PATTERNSSEASONAL PATTERNS

FLUX MAGNITUDEFLUX MAGNITUDE

Location of the AmeriFlux network sitesLocation of the AmeriFlux network sites

AmeriFlux: http://public.ornl.gov/ameriflux/

Fluxnet: http://www.fluxnet.ornl.gov/fluxnet/index.cfm

Uncertainties in the MOD17 Uncertainties in the MOD17 (GPP/NPP) Algorithm(GPP/NPP) Algorithm

1. Meteorological DAO IPAR, Temperature, VPD

2. Radiometric MODIS FPAR and LAI

3. Ecological MOD17 representation of plant physiology

(BPLUT)Accurate mapping of landcover type

Each of these requires a different mode of validation.




3. Ecological MOD17 representation of plant physiology

(BPLUT)Accurate mapping of landcover type


Non-linear interpolation of DAONon-linear interpolation of DAOA B

Methods of DAO SmoothingMethods of DAO Smoothing

The non-linear distancesThe non-linear distances

The weighted valuesThe weighted values

The interpolated DAO The interpolated DAO variablesvariables

Climate – Niwot Ridge, COClimate – Niwot Ridge, CO

Heinsch et al. IEEE 44: 1908-1925, 2006

Climate – Tonzi Ranch, CAClimate – Tonzi Ranch, CA


Global Daily Surface Meteorology vs Fluxtowers across 9 biomes

From D.P.Turner et al. Remote Sensing of Env. 102:282-292. 2006




3. Ecological Accurate mapping of landcover typeMOD17 representation of plant physiology

(BPLUT)


MODIS LAI vs. Tower GPP for 15 Ameriflux Sites





3. Ecological Accurate mapping of landcover typeMOD17 representation of plant physiology

(BPLUT)


Blodgett Forest, CABlodgett Forest, CA

1 = ENF 5 = Mixed Forest

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Gainesville, FL (Austin-Carey)Gainesville, FL (Austin-Carey)

1 = ENF 2 = EBF 5 = MF 8 = Woody Savanna 12 = Cropland

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Uncertainties from Land Cover (MOD12Q1)Uncertainties from Land Cover (MOD12Q1)

4 = Deciduous Broadleaf Forest (DBF)5 = Mixed Forest8 = Woody Savannas

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Daily GPP by Biome Type, July 20~27, 2001Daily GPP by Biome Type, July 20~27, 2001

Credit: Sinkyu Kang, NTSG

r = 0.859 0.173% Error = 19%

MODIS GPP vs. Tower GPP (DAO met.)

r = 0.792 0.206% Error = -2%

MODIS GPP vs. Tower GPP (Tower met.)

Metolius (P. pine)

Sylvania(dbf)

Tonzi Ranch (oak savanna)

Niwot Ridge (subalpine fir)

MODIS GPP/NPPMODIS GPP/NPP vs. Flux Towers vs. Flux Towersacross 9 Biomesacross 9 Biomes

From D.P. Turner et al. Remote Sensing of Env 102:282-292. 2006

Summary of ResultsSummary of Results MODIS GPP follows the general trend, capturing onset of leaf MODIS GPP follows the general trend, capturing onset of leaf

growth, and in many cases, leaf senescence, while tending to over-growth, and in many cases, leaf senescence, while tending to over-estimate total tower GPP.estimate total tower GPP.

The MODIS GPP algorithm effectively captures the effects of stress The MODIS GPP algorithm effectively captures the effects of stress events, such as late-summer dry-down, on canopies.events, such as late-summer dry-down, on canopies.

Substituting tower meteorological data in the MODIS algorithm Substituting tower meteorological data in the MODIS algorithm leads to GPP values which are very similar to tower GPP, leads to GPP values which are very similar to tower GPP, suggesting the algorithm adequately estimates site GPP.suggesting the algorithm adequately estimates site GPP.

If DAO meteorology and tower meteorology are similar, MODIS GPP If DAO meteorology and tower meteorology are similar, MODIS GPP is comparable to tower GPP. But, if the coarse-resolution DAO data is comparable to tower GPP. But, if the coarse-resolution DAO data is not representative of the site, MODIS GPP can differ greatly from is not representative of the site, MODIS GPP can differ greatly from tower GPP.tower GPP.

Comparisons of site data that have been received are weighted Comparisons of site data that have been received are weighted heavily towards forest biomes. Other sites need to be studied to heavily towards forest biomes. Other sites need to be studied to determine if results are similar in other ecosystems.determine if results are similar in other ecosystems.

Integrating Ecosystem Process Integrating Ecosystem Process Models (e.g., Biome-BGC)Models (e.g., Biome-BGC)

Integrating Ecosystem Process Integrating Ecosystem Process ModelsModels

Does the MODIS GPP contain enough Does the MODIS GPP contain enough information regarding water stress?information regarding water stress?– VPD is sole water stress scalarVPD is sole water stress scalar– Soil water stress??Soil water stress??

Test by comparing with Biome-BGCTest by comparing with Biome-BGC– U.S.A.U.S.A.– ChinaChina

Mu et al., JGR, 2007

Integrating Ecosystem Process Integrating Ecosystem Process ModelsModels

WaterWaterStressStressScalarsScalars(Growing(GrowingSeason)Season)


Correlation between water stress scalars Correlation between water stress scalars in Biome-BGC and MOD17in Biome-BGC and MOD17

(Growing Season)(Growing Season)


Correlation between water stress Correlation between water stress scalars in Biome-BGC and MOD17scalars in Biome-BGC and MOD17

(Monthly)(Monthly)


Correlation between Biome-BGC and Correlation between Biome-BGC and MOD17 GPP EstimatesMOD17 GPP Estimates

(Monthly)(Monthly)

Does MOD17 Capture Water Stress?Does MOD17 Capture Water Stress? Water not strongly limiting for most of the wetter

areas of China and the conterminous USA– m(VPD) reflects full water stress from air & soil as determined

by Biome-BGC

Using only VPD underestimates the water stress in dry regions & in areas with strong monsoons– Western China, the northeast China plain, the Shandong

peninsula, and the central and western United States– MOD17 overestimates GPP; add soil water stress?– Need for improved precipitation data to include soil moisture

VPD alone reflects interannual variability in most areas, – Current MOD17 adequate for global studies.

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Integrating Remote Sensing, Flux Measurements and Ecosystem Models