1. GCM 8/12/05: Retrieval of biophysical (vegetation) parameters from EO sensors
Dr. Mat Disney
Chandler House room 216

2. More specific parameters of interest
vegetation type (classification) (various)
vegetation amount (various)
primary production (C-fixation, food)
SW absorption (various)
temperature (growth limitation, water)
structure/height (radiation interception, roughness - momentum transfer)

3. Vegetation properties of interest in global change monitoring/modelling 
components of greenhouse gases
CO2 - carbon cycling
photosynthesis, biomass burning
CH4
lower conc. but more effective - cows and termites!
H20 - evapo-transpiration
(erosion of soil resources, wind/water)

4. Vegetation properties of interest in global change monitoring/modelling 
also, influences on mankind
crops, fuel
ecosystems (biodiversity, natural habitats) soil erosion and hydrology, micro and meso-scale climate

5. Explicitly deal here with
LAI/fAPAR
Leaf Area Index/fraction Absorbed Photsynthetically active radiation (vis.)
Productivity (& biomass)
PSN - daily net photosynthesis
NPP - Net primary productivity - ratio of carbon uptake to that produced via transpiration. NPP = annual sum of daily PSN.
BUT, other important/related parameters
BRDF (bidirectional reflectance distribution function)
albedo i.e. ratio of outgoing/incoming solar flux
Disturbance (fires, logging, disease etc.)
Phenology (timing)

6. definitions:
LAI - one-sided leaf area per unit area of ground - dimensionless
fAPAR - fraction of PAR (SW radiation waveband used by vegetation) absorbed - proportion

7. Appropriate scales for monitoring
spatial:
global land surface: ~143 x 106 km
1km data sets = ~143 x 106 pixels
GCM can currently deal with 0.25o - 0.1o grids (25-30km - 10km grid)
temporal:
depends on dynamics
1 month sampling required e.g. for crops
Maybe less frequent for seasonal variations?
Instruments??

8. optical data @ 1 km EOS MODIS (Terra/Aqua) 250m-1km
fuller coverage of spectrum
repeat multi-angular

9. optical data @ 1 km EOS MISR, on board Terra platform
multi-view angle (9)
275m-1 km
VIS/NIR only

10. optical data @ 1 km ENVISAT MERIS AVHRR 1 km
good spectral sampling VIS/NIR - 15 programmable bands between 390nm an 1040nm.
little multi-angular
AVHRR
> 1 km
Only 2 broad channels in vis/NIR & little multi-angular
BUT heritage of data since 1981

11. Future? production of datasets (e.g. EOSDIS)
e.g. MODIS products
NPOESS follow on missions
P-band RADAR??
cost of large projects (`big science') high
B$7 EOS
little direct `commercial' value at moderate resolution
data aimed at scientists, policy ....

12. LAI/fAPAR direct quantification of amount of (green) vegetation
structural quantity
uses:
radiation interception (fAPAR)
evapo-transpiration (H20)
photosynthesis (CO2) i.e. carbon
respiration (CO2 hence carbon)
leaf litter-fall (carbon again!)
Look at MODIS algorithm
Good example of algorithm development
see ATBD:

13. LAI 1-sided leaf area (m2) per m2 ground area
full canopy structural definition (e.g. for RS) requires
leaf angle distribution (LAD)
clumping
canopy height
macrostructure shape

14. LAI
preferable to fAPAR/NPP (fixed CO2) as LAI relates to standing biomass
includes standing biomass (e.g. evergreen forest)
can relate to NPP
can relate to site H20 availability (link to ET)

16. fAPAR
Fraction of absorbed photosynthetically active radiation (PAR: nm).
radiometric quantity
more directly related to remote sensing
e.g. relationship to RVI, NDVI
uses:
estimation of primary production / photosynthetic activity
e.g. radiation interception in crop models
monitoring, yield
e.g. carbon studies
close relationship with LAI
LAI more physically-meaningful measure

17. Issues empirical relationship to VIs can be formed
but depends on LAD, leaf properties (chlorophyll concentration, structure)
need to make relationship depend on land cover
relationship with VIs can vary with external factors, tho’ effects of many can be minimised

19. Estimation of LAI/fAPAR
initial field experiments on crops/grass
correlation of VIs - LAI
developed to airborne and satellite
global scale - complexity of natural structures

20. Estimation of LAI/fAPAR
canopies with different LAI can have same VI
effects of clumping/structure
can attempt different relationships dept. on cover class
can use fuller range of spectral/directional information in BRDF model
fAPAR related to LAI
varies with structure
can define through
clumped leaf area
ground cover

21. Estimation of LAI/fAPAR
fAPAR relationship to VIs typically simpler
linear with asymptote at LAI ~6
BIG issue of saturation of VI signal at high LAI (>5 say)
need to define different relationships for different cover types

22. MODIS LAI/fAPAR algorithm
RT (radiative transfer) model-based
define 6 cover types (biomes) based on RT (structure) considerations
grasses & cereals
shrubs
broadleaf crops
savanna
broadleaf forest
needle forest

23. MODIS LAI/fAPAR algorithm
have different VI-parameter relationships
can make assumptions within cover types
e.g., erectophile LAD for grasses/cereals
e.g., layered canopy for savanna
use 1-D and 3D numerical RT (radiative transfer) models (Myneni) to forward-model for range of LAI
result in look-up-table (LUT) of reflectance as fn. of view/illumination angles and wavelength
LUT ~ 64MB for 6 biomes

24. Method preselect cover types (algorithm)
minimise RMSE as fn. of LAI between observations and appropriate models (stored in look-up-table – LUT)
if RMSE small enough, fAPAR / LAI output
backup algorithm if RMSE high - VI-based

29. Productivity: PSN and NPP
(daily) net photosynthesis (PSN)
(annual) net primary production (NPP)
relate to net carbon uptake
important for understanding global carbon budget -
how much is there, where is it and how is it changing
Hence climate change, policy etc. etc.

30. PSN and NPP C02 removed from atmosphere
photosynthesis
C02 released by plant (and animal)
respiration (auto- and heterotrophic)
major part is microbes in soil....
Net Photosynthesis (PSN)
net carbon exchange over 1 day: (photosynthesis - respiration)

31. PSN and NPP Net Primary Productivity (NPP) annual net carbon exchange
quantifies actual plant growth
Conversion to biomass (woody, foliar, root)
(not just C02 fixation)

32. Algorithms - require to be model-based
simple production efficiency model (PEM)
(Monteith, 1972; 1977)
relate PSN, NPP to APAR
APAR from PAR and fAPAR

33. PSN = daily total photosynthesis
NPP, PSN typically accum. of dry matter (convert to C by assuming DM 48% C)
 = efficiency of conversion of PAR to DM (g/MJ)
equations hold for non-stressed conditions

34. to characterise vegetation need to know efficiency  and fAPAR:
so for fixed 

35. Determining  herbaceous vegetation (grasses): woody vegetation:
av gC/MJ for C3 plants
higher for C4
woody vegetation:
gC/MJ
simple model for  :

36. gross- conversion efficiency of gross photosyn. (= 2.7 gC/MJ)
f - fraction of daytime when photosyn. not limited (base tempt. etc)
Yg - fraction of photosyn. NOT used by growth respiration (65-75%)
Ym - fraction of photosyn. NOT used by maintainance respiration (60-75%)

39. NPP
1km over W. Europe, 2001.

40. Issues? Need to know land cover Ideally, plant functional type (PFT)
Get this wrong, get LAI, fAPAR and NPP/GPP wrong
ALSO
Need to make assumptions about carbon lost via respiration to go from GPP to NPP

41. MODIS LAI/fAPAR land cover classification
UK is mostly 1, some 2 and 4 (savannah???) and 8.
Ireland mostly broadleaf forest?
How accurate at UK scale?
At global scale?
0 = water; 1 = grasses/cereal crops; 2 = shrubs; 3 = broadleaf crops; 4 = savannah; 5= broadleaf forest; 6 = needleleaf forest; 7 = unvegetated; 8 = urban; 9 = unclassified

42. Compare/assimilate with models
Dynamic Global Vegetation Models
e.g. LPJ, SDGVM, BiomeBGC...
Driven by climate (& veg. Parameters)
Model vegetation productivity
hey-presto - global terrestrial carbon Nitrogen, water budgets.....
BUT - how good are they?
Key is to quantify UNCERTAINTY

43. land use change (HUGE impact on dynamics) Impact on us more direct
Why carbon?
CO2, CH4 etc.
greenhouse gases
Importance for understanding (and Kyoto etc...)
Lots in oceans of course, but less dynamic AND less prone to anthropgenic distrubance
de/afforestation
land use change (HUGE impact on dynamics)
Impact on us more direct

44. 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

45. Carbon: how??
Measure fluxes using eddy-covariance towers

46. MODIS Phenology 2001 (Zhang et al., RSE)
Dynam. global veg. models driven by phenology
This phenol. Based on NDVI trajectory....
greenup
maturity
DOY 0
DOY 365
senescence
dormancy

47. 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),

48. Total vegetated area: 117 M km2
Limiting factors
---
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

49. 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.
---
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

50. EO data: current Global capability of MODIS, MISR, AVHRR...etc.
Estimate vegetation cover (LAI)
Dynamics (phenology, land use change etc.)
Productivity (NPP)
Disturbance (fire, deforestation etc.)
Compare with models
AND/OR use to constrain/drive models (assimilation)

51. EO data: future?
BIG limitation of saturation of reflectance signal at LAI > 5
Spaceborne LIDAR, P-band RADAR to overcome this?
Use structural information, multi-angle etc.?
What does LAI at 1km (and lower) mean?
Heterogeneity/mixed pixels
Large boreal forests? Tropical rainforests?
Combine multi-scale measurements – fine scale in some places, scale up across wider areas….
EOS era (MODIS etc.) coming to an end ????

52. References http://cybele.bu.edu http://www.globalcarbonproject.org
Cox et al. (2000) Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408,
Dubayah, R. (1992) Estimating net solar radiation using Landsat Thematic Mapper and Digital Elevation data. Water resources Res., 28:
Monteith, J.L., (1972) Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol, 9:
Monteith, J.L., (1977). Climate and efficiency of crop production in Britain. Phil. Trans. Royal Soc. London, B 281:
Myneni et al. (2001) A large carbon sink in the woody biomass of Northern forests, PNAS, Vol. 98(26), pp
Running, S.W., Nemani, R., Glassy, J.M. (1996) MOD17 PSN/NPP Algorithm Theoretical Basis Document, NASA.