UCL
DEPARTMENT
OF GEOGRAPHY
UCL DEPARTMENT
OF GEOGRAPHY
Earth Observation for estimating terrestrial C:
issues and uncertainties
Mat Disney, University College London (UCL), Geography and NERC
National Centre for Earth Observation (NCEO)
TRUDAT/Amazonas Workshop 20/5/2013
UCL DEPARTMENT OF GEOGRAPHY
Outline
• What EO can and can‟t do for estimating C
• LAI and uncertainty
– MODIS and JRC-TIP approaches
• Modelling and uncertainty
– Data assimilation
• New observations
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UCL DEPARTMENT OF GEOGRAPHY
What EO can and can’t measure
• Direct, or nearly direct; calibrated with well-characterised
uncertainty
• Easy physical interpretation BUT mostly far removed from
ecological parameters
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UCL DEPARTMENT OF GEOGRAPHY
What EO can and can’t measure
• Far-removed from direct measurements, require
additional models, assumptions, ancillary information
• Consequently poorly-characterised uncertainty
• Hard to interpret/validate physically BUT direct ecological
meaning
• Uncertainty generally only via „validation‟ BUT…..
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UCL DEPARTMENT OF GEOGRAPHY
Uncertainty: what do we mean?
• Direct (eg TOA): calibration, geometric precision/accuracy
• Moderate: (eg TOC) above + atmos. correction, &
assumptions of models
• Indirect: (LAI, GPP, NPP) above + assumptions of
additional models and ancillary data
etotal = f (eobs , emod , éëeancillary ùû)
• f is essentially posterior PDF, so how do we get?
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UCL DEPARTMENT OF GEOGRAPHY
Uncertainty: what do we mean?
• Pragmatic solutions?
– „Validation‟ against independent obs preferably with uncorrelated
errors i.e. different method/assumptions
• More formal for model + obs(or multi obs fusion) eg via
data assimilation (DA)
– Copes with errors in obs and models to give optimal model + data
estimation
– BUT still requires some estimate of model AND obs uncertainty
etotal = f (eobs , emod , éëeancillary ùû)
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UCL DEPARTMENT OF GEOGRAPHY
LAI
• LAI as driver of GPP
– ΔCannual = NPP = Σ GPP - (Rh + Ra)
• Simple to define, hard to measure
• MODIS LAI/fAPAR eg MCD15A3
– ONLY physically-based global algorithm – uses 3D radiative
transfer model over 8 biomes inverted by a look-up-table (LUT)
– Uncertainty (c5 only) is mean of acceptable solutions to inverse
problem i.e. pragmatic BUT not whole story
– Doesn‟t address uncertainty in reflectance, model assumptions…
https://lpdaac.usgs.gov/products/modis_products_table/mcd15a3
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UCL DEPARTMENT OF GEOGRAPHY
LAI
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Three methods
Three answers
Which is “right”?
None?
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Pfeifer et al. (2012) Global Ecol. Biogeogr., 21, 603–624
UCL DEPARTMENT OF GEOGRAPHY
LAI: ‘effective’ parameter
Same LAI, arranged differently with diff. RT properties
Widlowski et al. (2011) JGR 116, G02019, doi:10.1029/2010JG001511
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UCL DEPARTMENT OF GEOGRAPHY
LAI validation
• Comparison of MODIS, VGT, CYCLOPES and LAIeffective
• EO comparisons better (r2 0.58) than MODIS v field (r2 0.2)
Fang et al. (2012) Rem. Sens. Environ., 119, 43-54
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UCL DEPARTMENT OF GEOGRAPHY
LAI: validation
Grass, cereal crop
• Big variation from c4
to c5
• Variance here at peak
values eg 10- 30%
• Dep. on q of obs
• For C getting it wrong
at min doesn‟t matter
• Does at max eg
summer
Decid broadleaf
Broadleaf crop
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http://modis.gsfc.nasa.gov/sci_team/meetings/c5meeting/pres/day1/shabanov.pdf
UCL DEPARTMENT OF GEOGRAPHY
LAI: other approaches
• eg Pinty et al. 2-stream LAI/fAPAR from MODIS
albedo
– Same assumption for retrieval as many GCMS (and C
models) use for energy budget
– Effective LAI so not „measurable‟ per se but wellquantified uncertainty AND consistent
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UCL DEPARTMENT OF GEOGRAPHY
LAI: other approaches
• Another approach designed to be self-consistent
for applications in large-scale models
– eg Pinty et al. 2-stream LAI/fAPAR from MODIS albedo – the JRCTIP approach
– 1D RT model can provide a solution for 3D canopy IF parameters
are effective
– Effective LAI so not „measurable‟ but well-quantified uncertainty
AND consistent
– Same assumption for retrieval as many GCMS (and C models) use
for energy budget
LAIeffective from MODIS (red), MISR (blue) NIR albedo,
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UCL DEPARTMENT OF GEOGRAPHY
Uncertainty: DA framework
• Developed from numerical weather prediction
– Treat uncertainty explicitly
– Obs operator H(x) is a non-linear RT model that maps state vector
x to EO signal R i.e. R = H(x)
– Estimate x (params) from observed R via minimisation of cost
function
J ( x) = Jobs ( x ) + J prior ( x) + Jmod ( x ) +
– Eg EO-LDAS – use TOC reflectance to estimate LAI, chlorophyll,
water, dry matter, leaf structure, soil
– Constraint based on a prior estimate of the state vector, a linear
dynamic model (tomorrow same as today)
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Lewis et al. (2012) Rem. Sens. Environ., 120, 219-235.
UCL DEPARTMENT OF GEOGRAPHY
Uncertainty: DA framework
• Model uncertainty unknown –
estimated via cross-validation
• 1st order constraint does good
job of smoothing obs
• (dotted lines are true values)
• Little increase in uncertainty
even w sparse obs (eg due to
cloud)
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Lewis et al. (2012) Rem. Sens. Environ., 120, 219-235.
UCL DEPARTMENT OF GEOGRAPHY
Fluorescence
• New(ish) observation, closely related to GPP
• GPP ~ Fp = I x f x εp
– i.e. IPAR x fAPAR x photosynthetic efficiency
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•
•
•
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By analogy, Fs about 1% of absorbed = I x f x εs
Small signal (about 1% of absorbed, 2-3% reflected)
So GPP = Fp = Fs x (εp/εs)
Decreases under stress (heat dissipation increases)
Correlated to GPP, particularly from stressed veg
Can possibly get from passive reflected, but better in
absorption lines: GOSAT
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UCL DEPARTMENT OF GEOGRAPHY
Fluorescence
• Fs estimated from GOSAT, July 2009 (Robert Simmon, using data
from GOSAT http://visibleearth.nasa.gov/view.php?id=51121)
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UCL DEPARTMENT OF GEOGRAPHY
Fluorescence
• GOSAT Fs & GPP v water stress (r2 = 0.75)
• FS model v FS GOSAT r2 = 0.79 for FIXED LAI i.e. environment more
important than chlorophyll, leaf
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Lee et al. (2013) Proc. R. Soc. B 2013 280, 20130171
UCL DEPARTMENT OF GEOGRAPHY
LIDAR
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Saatchi et al. (2011) PNAS, 108 (24),9899-9904.
UCL DEPARTMENT OF GEOGRAPHY
Conclusion: obs
• Optical EO limited by saturation at high LAI > 5, &
uncertainty increases with LAI
• High-res infrequent optical v useful for disturbance
to augment low res/high temporal
• Use prior/ancillary information if at all possible
– Time series of prior obs – BEST estimate so far
– New obs eg fluorescence
– Lidar for stocks via height/allometry
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UCL DEPARTMENT OF GEOGRAPHY
Conclusion: models
• Model uncertainty unknown in general
– Estimate via driver and parameter sensitivity analysis
– Estimate via cross-validation (leave one out xvalidation): leave out data points, predict, calculate
error/residuals
– Variants eg Jacknifing/bootstrapping
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UCL DEPARTMENT OF GEOGRAPHY
Thanks
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