Spatial Model-Data Comparison Project Conclusions

• Forward models are very different and do not agree on timing or spatial distribution of C sources/sinks.

• Examination of NEE shows GPP is largest component of discrepancies.

• Why?

Tower-Based Spectral Measures of

LUE, GPP

Provide Independent Data at Meter-Scale and Minute-Scale Resolution

Adding Spectral Measures of Vegetation Light Use

Efficiency to Existing Towers and Tower Sites

Gap-Filler for Tower Measurements

Diagnostics For Process Models

Scaling to Landscape, Region

Globe??

Spectral Measures of Surface-Atmosphere Energy, Water, Carbon

Exchange

Latent heat

LE = Cp [e*- ea] gcga gc+ga

Models compute gc

gc = gc* x f([e*- ea] ,Ta, soil

moisture)0≤f≤ 1

gc* = max gc

•f is a non-linear function of soil moisture•Soil moisture is difficult to model

•Precipitation & soil properties variability•Soil Moisture diffcult to measure in the presence of dense vegetation.

Carbon Assimilation Rate Directly From Satellite

A = par x fapar x LUE radiative xfer NDVI PRI

gc = Ah/c

X

an

thop

hyll B

an

d 5

31

nm

Refe

ren

ce B

an

d 5

70

nm

PRI =

As LUE decreases and PRI decrease.

Photochemical Reflectance Index A Normalized Difference Reflectance Index

(NDRI)Gamon et al. 1992, 1993, 1997, Gamon & Surfus 1999

At the landscape scale are also sensitive to variations in other pigments and conditions other than LUE; beta-carotene, lutein, variations in soil, litter background.

a b

Figure 3: (a) A dual channel radiometer is mounted on the DF49 Fluxnet tower with a vertical zenith angle (VZA) of 62°. A motor moves the canopy sensor 360o every 15 minutes. Data are recorded every 5 seconds, year round. (b) The instrument on the Fluxnet Canada Douglas fir research site

Fluxnet Canada Doug FirBritish Columbia

Measured ComponentsFor Douglas Fir

Slope of PRI vs LUEDoug Fir

LUE ≈ (∂PRI/∂SF-0.21)/0.05

COMBINING MODEL, TOWER AND REMOTE SENSING MEASUREMENTS

• Remote sensing estimates of LUE, in combination with process model estimates and tower-based estimates form a powerful set of independent measures for cross-validation of these independent methodologies.

• Process models calculate – components of gross primary production, e.g. LUE, GPP – autotrophic and heterotrophic respiration, NEP

• Compare process model predictions to spectral and eddy covariance (EC) measures of same – compare model outputs of these quantities to the minute-scale tower and remote sensing-

inferred values– Spectral measures can help gap-fill tower EC flux data– flag and help diagnose problems in any of the methods when the diurnal or longer-term

values diverge. • Aircraft and satellite landscape-scale measures (available only under clear-sky conditions)

could be compared with similar estimates from process models– examine divergences over the landscape– flag and diagnose problems

• remote sensing algorithm• variables used by the process models such as soil texture, soil moisture, nutrient levels etc.

– data assimilation

What needs to be added to the existing Canadian, U.S., European and International

network of flux towers?

Scaling to LandscapeAircraft Spectral Measurements - Missions of opportunity? NASA AVIRIS, the Canadian CASI, EO-1, NASA LVIS Lidar, other Canadian Lidar flights and the NASA airborne SLICER instrument (an aircraft Lidar flown during BOREAS), ASAS, multi-angle along-track hyperspectral flown during BOREAS

Scaling to Region and Globe -- Not Global yet, but…Chris Proba, EO-1, MERIS MODIS

Tower Spectral Measurement InstrumentsCosts of sensors range from 25K$ as in the AMSPEC from Hilker et al, to a full spectrum CCD at about 6-10K$ - (350-1500 nm), to a simple PRI sensor using a light sensor and interference filter for about 2K$ (which could be handmade).