1. GEOGG142 GMES Calibration & validation of EO products
Dr. Mat Disney
Pearson Building room 113

2. Outline Calibration Validation Example: AVHRR NDVI across time
Multiple AVHRR (and different) sensors: calibration, drift etc.
Validation
Example: MODIS NPP product
Time, space, measurements?
Scaling?

3. Calibration & validation?
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

4. Example: calibration of AVHRR NDVI
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

5. Material from J. Morley

6. Material from J. Morley

7. 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

8. Example: MODIS EVI GLobal EVI winter/spring 2001

9. 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

10. 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

11. 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

12. Ozone and water vapour absorption

13. 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

14. AVHRR?
Material from J. Morley

15. Aerosols
Material from J. Morley

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22. Empirical mode decomposition (EMD)
Material from J. Morley

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25. Material from J. Morley

26. Sensor intercomparison?
Material from J. Morley

27. 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
Turner et al (2005)

28. 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)

29. MODIS GPP/NPP + QC??

30. 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)

31. 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??

32. 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

33. Ecosystem measurements: FLUXNET
Fig from MOD17 ATBD

34. Ecosystem measurements: FLUXNET 1999

35. Ecosystem measurements: FLUXNET 2009

36. Ecosystem measurements: FLUXNET

37. Ecosystem measurements: FLUXNET by biome
Some distribution of biome types, but clearly biased in location
Even considering only limited biomes

38. 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

39. BigFoot v flux tower GPP
Turner et al. (2005)

40. BigFoot v MODIS GPP
Not such good agreement as for flux tower (not surprisingly)
Turner et al. (2005)

41. 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)

42. 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)

43. 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)

44. 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)

45. Zhao et al. (2005)
Heinsch et al. (2006)

46. Process/SVAT (soil-veg-atm-transport) models
Fig from MOD17 ATBD

47. 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 near-surface atmosphere i.e. daily PSN

48. Process models: how do we test/validate?
Fig from MOD17 ATBD

49. 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. 2000

50. Multi-level model/data validation
MOD17 ATBD: Synergy of various carbon measurement programs
Fig from MOD17 ATBD

51. Summary Calibration Validation example: NPP
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)

52. References: calibration
Ganguly et al. (2008a, b) Generating vegetation leaf area index earth system data record from multiple Sensors, RSE, 112, (Part II) and (Part I)

53. References: calibration

54. References: calibration

55. References: validation
NPP
Running et al. (2004) A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production, Bioscience 54(6),
Ganguly et al. (2008a, b) Generating vegetation leaf area index earth system data record from multiple Sensors, RSE, 112, (Part II) and (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,
Zhao et al. (2005) Improvements of the MODIS terrestrial net and gross primary production data sets, RSE, 95,
Heinsch et al. (2006) Evaluation of Remote Sensing Based Terrestrial Productivity From MODIS Using Regional Tower Eddy Flux Network Observations, IEEE TGRS, 44(7),
General validation
Morisette et al. (2002) A framework for the validation of MODIS Land products, RSE, 83,
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),

56. 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)
SAFARI2000:
VALERI:
NCAVEO:
JAXA:
Etc etc etc

57. 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),

58. Total vegetated area: 117 M km2
Limiting factors
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In order to evaluate the world-wide significance of climatic changes in the context of limiting factors to plant growth, we derived a global map, shown here, of the relative influence of climate factors that regulate plant growth
(temperature, water and solar radiation) using long-term ( ) 0.5 x 0.5 grided monthly climate data from Leemans and Cramer .
We found that water availability acts as a dominant control over 40% of the Earth’s vegetated area of 117 M km2, followed by temperature (33%) and radiation (27%).
Often, more than one climatic factor regulates plant growth during the growing season. Plant growth is limited by
- temperature and radiation (cold winters and cloudy summers) over Eurasia, shown here in cyan,
- temperature and water (cold winters and dry summers) over western North America, shown here in magenta,
- and radiation and water (wet-cloudy and dry-hot periods induced by rainfall seasonality) in the tropics, shown here in yellow.
These limits vary by season; for example, high latitude regions are limited by temperature in the winter and by either water or radiation in the summer.
Dominant Controls
water availability 40%
temperature 33%
solar radiation 27%
Total vegetated area: 117 M km2

59. 11% Bottom line Since the early 1980s about, about 3%
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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Two questions.
By how much did the Earth green in the past 2 decades?
The answer is, about half the vegetated lands greened by about 11%, 15% of the vegetated lands browned by about 3%, and about a third of the vegetated lands showed no changes. Therefore, we conclude that
the earth greened by about 5%.
Why is the Earth greening?
The answer is, the climate changes of the past 20 years have eased climatic constraints to plant growth, that is, where temperature is critical to plant growth, there temperature has increased, and likewise with
water and solar radiation.
Thank You!
Bottom line

60. Example: MODIS core val sites
Justice et al. (1998)
Privette et al. (2002) and RSE 83, 1-2, 1-359