GEOGG142 GMES
Calibration & validation of EO products
Dr. Mat Disney
mdisney@geog.ucl.ac.uk
Pearson Building room 113
020 7679 0592
www.geog.ucl.ac.uk/~mdisney
Outline
 Calibration
 Example: AVHRR NDVI across time
 Multiple AVHRR (and different) sensors:
calibration, drift etc.
 Validation
 Example: MODIS NPP product
 Time, space, measurements?
 Scaling?
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Calibration & validation?
• Calibration:
– process of converting an instrument reading to a
physically meaningful measurement
– Particularly radiometric calibration
– i.e. from DN to radiance measurement
• Validation:
– experiments designed to verify instrument
measurements using independent measurements
• Both essential to scientific remote sensing
Material from J. Morley
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Example: calibration of AVHRR NDVI
• Calibration:
– We observe a known target, and relate output DNs
to target radiance
– Known targets:
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prelaunch, lab targets (e.g. AVHRR)
on-board lamps (e.g. CZCS)
astronomical objects (Sun, Moon, space E.g., SeaWIFS)
‘invariant’ surfaces (e.g. deserts)
Material from J. Morley
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Material from J. Morley
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Material from J. Morley
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Example: calibration of AVHRR NDVI
• Normalised Difference Vegetation Index
(NDVI):
– Simple to compute value, based on radiances in
red and near infrared spectral regions
– NDVI = (L_NIR – L_R) / (L_NIR + L_R)
– Value range = -1 to +1
– EMPIRICALLY related to vegetation amount due to
spectral response of plant leaves (‘red edge’)
Material from J. Morley
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Example: MODIS EVI
GLobal EVI winter/spring 2001
http://svs.gsfc.nasa.gov/vis/a00
0000/a002300/a002317/index.
html
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Issues in NDVI calibration
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The biggest issue is the atmosphere
Particularly:
– Rayleigh scattering
– ozone
– water vapour
– aerosols
See van Leeuwen et al., 2006
Different versions of NDVI product (c4 NOT comparable w c5)
– Saleska et al. (2005) Amazon Forests Green-Up During 2005 Drought,
Science
– Samanta et al. (2010) Amazon forests did not green‐up during the 2005
drought, GRL
– ???
Material from J. Morley
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Rayleigh scattering
• Scattering of light by gas molecules in atmos.
• Biased towards the short visible wavelength & adds
radiance to the red channel
• Quite easily calculated based on surface altitude
(hence surface pressure)
• Reference values for Rayleigh optical depths for
standard pressure and temperature conditions are
available
• Vegetated areas have low red reflectance, so
Rayleigh scat. can substantially decrease NDVI
Material from J. Morley
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Ozone and water vapour absorption
• Optical bands weakly affected by ozone absorption.
• Water vapour absorption bands near 0.9 μm and 1.1
μm -> NIR is considerably affected.
• Water vapour reduces the observed NIR & hence
NDVI
• The longer path length from the sun - to the surface to the satellite, greater effect of water vapour has
– Off-nadir views more affected
• Difference in products when corrections introduced
Material from J. Morley
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Ozone and water vapour absorption
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Aerosols
• Effects vary depending on particle size e.g. difference
between volcanic and forest fire aerosols
• Note particularly El Chichon and Mount Pinatubo
eruptions left aerosol in atmos. for ~2 years each
• Need better spectral resolution for correction, e.g.
MODIS, or modelling
Material from J. Morley
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AVHRR?
Material from J. Morley
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Aerosols
Material from J. Morley
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Material from J. Morley
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Material from J. Morley
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Material from J. Morley
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Material from J. Morley
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Material from J. Morley
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Material from J. Morley
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Empirical mode decomposition (EMD)
http://glcf.umiacs.umd.edu/data/gimms/description.shtml
Material from J. Morley
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Material from J. Morley
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Material from J. Morley
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Material from J. Morley
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Sensor intercomparison?
Material from J. Morley
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Validation example: MODIS NPP
 Productivity recap: Net Primary Productivity
(NPP)
 annual net carbon exchange
 quantifies actual plant growth
 Conversion to biomass (woody, foliar, root)
– i.e. not just C02 fixation (GPP)
– NPP = GPP – Ra (plant respiration)
• MODIS product example used here
– MOD17 GPP/NPP ATBD
• ntsg.umt.edu/MOD17
• http://neo.sci.gsfc.nasa.gov/Search.html
– Turner et al (2005)
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Productivity recap
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GPP/NPP from MODIS
Requirements?
MOD17 ATBD
Running et al. (2004)
Turner et al. (2005)
Zhao et al. (2005)
Heinsch et a. (2006)
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MODIS GPP/NPP + QC??
http://secure.ntsg.umt.edu/projects/index.php/ID/ca2901a0/fuseaction/projects.detail.htm
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MOD17 validation approach
 Need to address time (days to years) and
space (local to global)
 Permanent network of ground validation sites
 Quantify seasonal and interannual dynamics of
ecosystem activity (cover time domain)
 EO to quantify heterogeneity of biosphere
 Quantify land cover, land cover change dynamics
 Models to:
 Quantify, understand unmeasured ecosystem
 Provide predictive capability (in time AND space)
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How on earth…..????
• …can we “validate” an EO-derived estimate of
something that depends on soil, climate, land cover
etc.?
• Given that it requires various models to go from a
satellite observation (radiance), to reflectance, to
LAI/FAPAR, to PSN, to GPP to NPP
• At 500m-1km pixels. Globally.
• And how do you even “measure” NPP on the
ground??
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So, how might we validate?
• Need to consider scale
• Relate measurements at the
small scale to 1km pixels??
• Flux tower approach
• Eg BIGFOOT approach,
FLUXNET etc.
• Measurements and
validation at many scales
• Models to bridge time/space
scales
Fig from MOD17 ATBD
– (but how good are models…?)
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Ecosystem measurements: FLUXNET
Fig from MOD17 ATBD
http://daac.ornl.gov/FLUXNET/
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Ecosystem measurements: FLUXNET 1999
http://daac.ornl.gov/FLUXNET/
http://earthobservatory.nasa.gov/Features/Fluxnet/
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Ecosystem measurements: FLUXNET 2009
http://daac.ornl.gov/FLUXNET/
http://www.fluxnet.ornl.gov/fluxnet/graphics.cfm
http://earthobservatory.nasa.gov/Features/Fluxnet/
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Ecosystem measurements: FLUXNET
http://daac.ornl.gov/FLUXNET/
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Ecosystem measurements: FLUXNET by biome
Some distribution of biome types, but clearly biased in location
Even considering only limited biomes
http://daac.ornl.gov/FLUXNET/
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BigFoot approach to validating MODIS NPP
 E.g. Turner et al. (2005), 6 sites spanning range of
vegetation and climate
 Crops, forest, tundra, grassland
 5 x 5 km site at each plot (25 MODIS pixels)
 Flux tower & 100 (25x25m) sample plots within each area,
seasonally measured for LAI and above-ground (A)NPP (from
harvested leaf and wood material)
 Land cover from high res EO
 Use measured data at sample plots to calculate NPP, GPP
 Spatially distribute across site using (vegetation-calibrated)
BiomeBGC model
 Requires daily met data, land cover, LAI
 Gives measured estimate from ground AND flux tower
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BigFoot v flux tower GPP
Turner et al. (2005)
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BigFoot v MODIS GPP
Not such good agreement as for flux tower (not surprisingly)
Turner et al. (2005)
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Comparison of MODIS NPP with flux data
Differences due to Ra (autotrophic i.e. plant respiration)?
PAR, VPD differences between those from DAO and actual?
(VPD = deficit between the amount of moisture in the air and how much
moisture the air can hold when it is saturated)
Turner et al. (2005)
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DAO PAR, VPD?
Clearly some sites better agreement than others
PAR generally good (relatively easy to measure)
VPD less so e.g. SEVI (desert grassland site) VPD
Other issues?
Turner et al. (2005)
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MODIS-estimated v BigFoot FPAR
How do you measure FPAR even on the ground??
Requires models to interpret measurements of radiation
Turner et al. (2005)
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MODIS-estimated v BigFoot LUE (light use
efficiency)
LUE inferred from flux data
Again, hard to even measure this on the ground…..
Turner et al. (2005)
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Zhao et al. (2005)
Heinsch et al. (2006)
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Process/SVAT (soil-veg-atm-transport) models
Fig from MOD17 ATBD
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Process models: how do we test/validate?
From Running et al.
(2004) MOD17
ATBD
Biome-BGC model
predicts the states
and fluxes of water,
carbon, and
nitrogen in the
system including
vegetation, litter,
soil, and the nearsurface atmosphere
i.e. daily PSN
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Process models: how do we test/validate?
http://www.ntsg.umt.edu/models/bgc/
Fig from MOD17 ATBD
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Data-Model
Fusion
[Using multiple
streams of datasets with
parameter optimization]
C stock and flux measurements
Inventory analyses
Process-based information
Climate data
Remote sensing information
CO2 column from space
Inverse modeling
Process-based modeling
Retrospective and forward analyses
Canadell et al. 49
2000
Multi-level model/data validation
• MOD17 ATBD: Synergy of various carbon measurement programs
Fig from MOD17 ATBD
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Summary
 Calibration
 Needed to allow comparison data from multiple sensors of over
time with another, even for simple empirical NDVI
 Can be done on-board, or via sensor intercomparison etc.
 Validation example: NPP
 Far removed from EO measurement & spatially, temporally variable
 Requires: observation networks over time and space and
measurement of met. & biophysical data
 Models to interpolate spatially from ground-based, site-scale
measurements
 Testing and intercomparison of models
 Ideally: optimal combinations of models + data across scales (e.g.
via data assimilation)
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References: calibration
Ganguly et al. (2008a, b) Generating vegetation leaf area index earth system data record from
multiple Sensors, RSE, 112, 4318-4332 (Part II) and 4333-4343 (Part I)
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References: calibration
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References: calibration
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References: validation
NPP
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Running et al. (2004) A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production,
Bioscience 54(6), 547-560.
Ganguly et al. (2008a, b) Generating vegetation leaf area index earth system data record from multiple
Sensors, RSE, 112, 4318-4332 (Part II) and 4333-4343 (Part I)
Turner et al. (2005) Site-level evaluation of satellite-based global terrestrial gross primary production
and net primary production monitoring, Glob Change Biol, 11, 666-684.
Zhao et al. (2005) Improvements of the MODIS terrestrial net and gross primary production data sets,
RSE, 95, 164-176.
Heinsch et al. (2006) Evaluation of Remote Sensing Based Terrestrial Productivity From MODIS Using
Regional Tower Eddy Flux Network Observations, IEEE TGRS, 44(7), 1908-1925.
General validation
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Morisette et al. (2002) A framework for the validation of MODIS Land products, RSE, 83, 77-96.
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Disney et al. (2004) Comparison of MODIS broadband albedo over an agricultural site with
ground measurements and values derived from Earth observation data at a range of
spatial scales, IJRS, 25(23), 5297-5317.
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Other cal/val links
 NPP: http://daac.ornl.gov/NPP/npp_home.html
 Cal/val programs
 CEOS-WFGCV (Committee on EO Working Group on
Cal/Val)
 http://calvalportal.ceos.org/CalValPortal/welcome.do
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http://lpvs.gsfc.nasa.gov/
http://landval.gsfc.nasa.gov/
SAFARI2000: http://daac.ornl.gov/S2K/safari.html
VALERI: http://w3.avignon.inra.fr/valeri/
NCAVEO: http://www.ncaveo.ac.uk/
JAXA:
http://www.eorc.jaxa.jp/ALOS/en/calval/calval_index.htm
 Etc etc etc
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-Carbon sinks/sources
using AVHRR data to
derive NPP
-Carbon pool in woody
biomass of NH forests
(1.5 billion ha)
estimated to be 61  20
Gt C during the late
1990s.
- Sink estimate for the
woody biomass during
the 1980s and 1990s is
0.680.34 Gt C/yr.
-From Myneni et al. PNAS, 98(26),1478414789
http://cybele.bu.edu/biomass/biomass.html
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Limiting factors
Dominant Controls
water availability 40%
temperature 33%
solar radiation 27%
Total vegetated area: 117 M km2
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Since the early 1980s about,
- half the vegetated lands greened by about
11%
- 15% of the vegetated lands browned by
about 3%
- 1/3rd of the vegetated lands showed no
changes.
These changes are due to easing of climatic
constraints to plant growth.
Bottom line
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Example: MODIS core val sites
http://landval.gsfc.nasa.gov/coresite_gen.html
Justice et al. (1998) http://eospso.gsfc.nasa.gov/eos_observ/5_6_98/p55.html
Privette et al. (2002) and RSE 83, 1-2, 1-359
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