GEOGG142 GMESCalibration & 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

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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:

• 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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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/a000000/a002300/a002317/index.html

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Issues in NDVI calibration

• 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?

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Aerosols

Material from J. Morley

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Empirical mode decomposition (EMD)

http://glcf.umiacs.umd.edu/data/gimms/description.shtml

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Sensor intercomparison?

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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

• 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 – (but how good are models…?)

Fig from MOD17 ATBD

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Ecosystem measurements: FLUXNET

http://daac.ornl.gov/FLUXNET/

Fig from MOD17 ATBD

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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.cfmhttp://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

http://daac.ornl.gov/FLUXNET/

Some distribution of biome types, but clearly biased in locationEven considering only limited biomes

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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

Turner et al. (2005)

Not such good agreement as for flux tower (not surprisingly)

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Comparison of MODIS NPP with flux data

Turner et al. (2005)

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)

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DAO PAR, VPD?

Turner et al. (2005)

Clearly some sites better agreement than othersPAR generally good (relatively easy to measure)VPD less so e.g. SEVI (desert grassland site) VPDOther issues?

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MODIS-estimated v BigFoot FPAR

Turner et al. (2005)

How do you measure FPAR even on the ground??Requires models to interpret measurements of radiation

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MODIS-estimated v BigFoot LUE (light use efficiency)

Turner et al. (2005)

LUE inferred from flux dataAgain, hard to even measure this on the ground…..

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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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From Running et al. (2004) MOD17 ATBDBiome-BGC model predicts the states and fluxes of water, carbon, andnitrogen in the system including vegetation, litter, soil, and the near-surface atmosphere i.e. daily PSN

Process models: how do we test/validate?

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Process models: how do we test/validate?

Fig from MOD17 ATBDhttp://www.ntsg.umt.edu/models/bgc/

49Canadell et al. 2000

Data-ModelFusion

[Using multiplestreams of datasets withparameter optimization]

C stock and flux measurementsInventory analysesProcess-based informationClimate dataRemote sensing informationCO2 column from space

Inverse modelingProcess-based modelingRetrospective and forward analyses

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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: validationNPP• 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• Morisette et al. (2002) A framework for the validation of MODIS Land products, RSE, 83, 77-96.• 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

· 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 0.680.34 Gt C/yr.

- From Myneni et al. PNAS, 98(26),14784-14789

http://cybele.bu.edu/biomass/biomass.html

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Dominant Controlswater availability 40%

temperature 33%solar radiation 27%

Total vegetated area: 117 M km2

Limiting factors

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Bottom line

- 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.

Since the early 1980s about,

These changes are due to easing of climatic constraints to plant growth.

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Example: MODIS core val sites

http://landval.gsfc.nasa.gov/coresite_gen.htmlJustice et al. (1998) http://eospso.gsfc.nasa.gov/eos_observ/5_6_98/p55.htmlPrivette et al. (2002) and RSE 83, 1-2, 1-359