GMES biophys params by HC120727183534

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									CEGEG044 GMES
Dec. 2009: Global vegetation parameters
from EO
Dr. Mat Disney
mdisney@geog.ucl.ac.uk
Pearson Building room 113
020 7679 0592
www.geog.ucl.ac.uk/~mdisney
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)


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Vegetation properties of interest in global
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)



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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



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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)


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definitions:




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



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




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




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• optical data @ 1 km
  – ENVISAT MERIS
    • 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


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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 ....


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LAI/fAPAR
  direct quantification of amount of (green) vegetation
  structural quantity
  uses:
       radiation interception (fAPAR)
       evapotranspiration (H20)
       photosynthesis (CO2) i.e. carbon
       respiration (CO2 hence carbon)
       leaf litter-fall (carbon again)
       Look at MODIS algorithm
          Good example of algorithm development
          ATBD:http://cybele.bu.edu/modismisr/atbds/modisatbd.pdf



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                            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




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                         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




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                               fAPAR

 Fraction of absorbed photosynthetically active
  radiation (PAR: 400-700nm).
 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


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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
   NDVI  1 – e-kLAI


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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




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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



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Estimation of LAI/fAPAR

  fAPAR relationship to VIs typically simpler
        linear with asymptote at LAI ~4-6
        BIG issue of saturation of VI signal at high LAI (>5 say)
 • need to define different relationships for
   different cover types




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MODIS LAI/fAPAR algorithm



   See ATBD: http://cliveg.bu.edu/index.html
   AND modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf -
   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


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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

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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




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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.



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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)



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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)




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Algorithms - require to be model-based

 simple production efficiency model (PEM)
  – (Monteith, 1972; 1977)
 relate PSN, NPP to APAR
 APAR from PAR and fAPAR


            APAR      IPAR  fAPAR
                     day

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               PSN              APAR

                  NPP     APAR


 PSN = daily total photosynthesis
 NPP, PSN typically accum. of dry matter (convert to C by
assuming dry matter (DM) ~ 48% C)
  = efficiency of conversion of PAR to DM (g/MJ)
 equations hold for non-stressed conditions
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to characterise vegetation need to know efficiency 
and fAPAR:


• Efficiency
• fAPAR
                     fAPAR NDVI
   so for fixed 
                    PSN         IPAR
                                 day

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Determining 



 herbaceous vegetation (grasses):
   av. 1.0-1.8 gC/MJ for C3 plants
   higher for C4
 woody vegetation:
   0.2 - 1.5 gC/MJ
• simple model for  :
                 gross  f  Yg  Ym
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                     gross  f  Yg  Ym


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%)




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Biome-BGC
model




            37
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




                       38
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




                       39
From Running et al.
(2004) MOD17
ATBD




                      40
41
NPP




      1km over W. Europe, 2001.
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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
   So how good is BiomeBGC model?



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                                                                             •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
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Compare with/assimilate into 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



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                         •MODIS
                         Phenology 2001
                         (Zhang et al., RSE)
                         •Dynam. global
                         veg. models driven
                         by phenology
greenup      maturity
                         •This phenol.
                         Based on NDVI
                         trajectory....


                          DOY 0




                                      DOY 365
senescence    dormancy
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How might we validate MODIS NPP?

 Measure NPP on the ground??
   Scale? Methods?
 Intercompare with Dynamic Global Vegetation
  Models??
   e.g. LPJ, SDGVM, BiomeBGC...
     • Driven by climate (& veg. Parameters)
  – how good are they?
     • Can we quantify UNCERTAINTY?
     • In both observations AND models
• Model-data fusion approaches
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Summary: 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)




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  Summary 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?
    NPOESS? http://www.ipo.noaa.gov/
    DESDyni? http://desdyni.jpl.nasa.gov/
 ESA Explorer & Sentinel missions (BIOMASS etc.)
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References
•   Myneni et al. (2007) Large seasonal changes in leaf area of Amazon rainforests.
    Proc. Natl. Acad. Sci., 104: 4820-4823, doi:10.1073/pnas.0611338104.
•   Cox et al. (2000) Acceleration of global warming due to carbon-cycle feedbacks
    in a coupled climate model, Nature, 408, 184-187.
•   Dubayah, R. (1992) Estimating net solar radiation using Landsat Thematic
    Mapper and Digital Elevation data. Water resources Res., 28: 2469-2484.
•   Monteith, J.L., (1972) Solar radiation and productivity in tropical
    ecosystems. J. Appl. Ecol, 9:747-766.
•   Monteith, J.L., (1977). Climate and efficiency of crop production in
    Britain. Phil. Trans. Royal Soc. London, B 281:277-294.
•   Myneni et al. (2001) A large carbon sink in the woody biomass of Northern
    forests, PNAS, Vol. 98(26), pp. 14784-14789
•   Myneni et al. (1998) MOD15 LAI/fAPAR Algorithm Theoretical Basis Document,
    NASA. http://cliveg.bu.edu/index.html &
    modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf
•   Running, S.W., Nemani, R., Glassy, J.M. (1996) MOD17 PSN/NPP Algorithm
    Theoretical Basis Document, NASA.
• http://www.globalcarbonproject.org


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