Transcript GEOGG142 GMES Global vegetation parameters from EO Dr. Mat Disney [email protected] Pearson Building room 113 020 7679 0592 www.geog.ucl.ac.uk/~mdisney.

GEOGG142 GMES
Global vegetation parameters from EO
Dr. Mat Disney
[email protected]
Pearson Building room 113
020 7679 0592
www.geog.ucl.ac.uk/~mdisney
More specific parameters of interest
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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
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–
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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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Caveats: when is LAI not LAI? Always
• LAI inferred from EO (or ground-based indirect) is a function of radiative
transfer (RT) approach used to retrieve it
• So LAI (3D RT) ≠ LAI (1D RT) ≠ LAI (field) ≠ LAI (real)
• Eg JRC-TIP LAI retrieval uses 1D RT model (Pinty et al. 2011)
– Consistent with large-scale climate and Earth system models
– Can operate on albedo, at large scales (i.e. operational)
– NO requirement for other assumptions e.g. biome type
– http://lpvs.gsfc.nasa.gov/PDF/Pinty_validation_TIP_RSE2011.pdf
– http://www.fastopt.com/references/rsens.html
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Caveats: when is LAI not LAI? Always
• TIP retrieval results in ‘effective’ LAI i.e. LAIeff = LAI z
• Where LAI is true domain-averaged LAI, in 1D RT case, ζ
is structural term (clumping), reduces LAIeff
• TIP-derived fAPAR consistent with LAI (and albedo) AND
uncertainty is meaningful
• MODIS LAI: biome-specific 3D RT solution i.e. ζ implicit in
biome & model retrieval, uncertainty is … uncertain
• SO not a like-for-like comparison
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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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Overview LAI/fapar Space Products
Projects/Institution
Sensors/Period
Input data
Output product
Retrieval Method
References
JRC-FAPAR
SeaWiFS
ESA MERIS
(07/97-04/12)
Top of Atmosphere (TOA)
BRFs in blue, red and nearinfrared bands
Daily Instantaneous green
FAPAR based on direct
incoming radiation
Optimization
Formulae
based on Radiative Transfer
Models
Gobron et al
(2000, 2006,
2008)
NASA
MODIS LAI/FPAR
(00-on going)
Surface reflectance in 7
spectral bands and land
cover map.
8-days FAPAR with direct
and
diffuse
incoming
radiation
Inversion of 3D Model versus
land cover type with backup
solution based on NDVI
relationship)
Knyazikhin et al.
(1998b)
NASA
MISR LAI/FPAR
(00-on going)
Surface products BHR, DHR
& BRF in blue, green, red
and near-infrared bands
+ CART
8-days FAPAR with direct
and
diffuse
incoming
radiation.
Inversion of 3D Model versus
land cover type with backup
solution based on NDVI
relationship)
Knyazikhin et al.
(1998a)
GLOBCARBON
Surface reflectance red, near
infrared, and shortwave
infrared
Instantaneous
(Black leaves)
FAPAR
Parametric relation with LAI
as function as Land cover
type.
Plummer et al.
(2006)
CYCLOPES
VEGETATION
Surface reflectance in the
blue, red, NIR and SWIR
bands
FAPAR at 10:00 solar local
time
Neural network based on 1D
model
Baret et al (2007)
JRC-TIP
MODIS/MISR
(00-On going)
Broadband Surface albedo in
visible and near-infrared
bands.
8-(16) days Standard
Inversion
of
two-stream
model using the Adjoint and
Hessian codes of a cost
function.
Pinty et al. (2007)
GEOLAND2/GLS
VEGETATION/PRO
BA-V
(99-2012/on going)
Normalized surface
reflectance in red and nearinfrared bands
FAPAR at 10:00 solar local
time
Neural network based on
CYCLOPES and MODIS
products
Baret et al (2010)
FAPAR or/& Green
FAPAR for direct or/&
diffuse incoming radiation
• 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 multiangular
• BUT heritage of data since 1981
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Future?
– NOAA Suomi NPP (National Polar-orbiting
Partnership)
• Suomi launched 2011-10-28
• MODIS-lite VIIRS (Visible Infrared Imaging Radiometer
Suite) 3000km swath, 750m spatial, 9 land bands
– ESA
• Sentinel 2: ~2014 2 platforms, MSI 10-60m spatial, 13
bands, 300km swath, repeat 2-5 days – much higher than
SPOT/Landsat
• Sentinel 3: ~2014 SLSTR, OLCI 21 bands, 300m spatial,
repeat 2-3 days
• P-band RADAR? Biomass decision soon
NPP: http://npp.gsfc.nasa.gov/mission_details.html
ESA Sentinels:
http://www.esa.int/Our_Activities/Observing_the_Earth/GMES/Sentinel-2
http://www.esa.int/Our_Activities/Observing_the_Earth/GMES/Sentinel-3
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LAI/fAPAR
 direct quantification of amount of (green) vegetation
 structural quantity
 uses:
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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

CEOS WGCV:
http://lpvs.gsfc.nasa.gov/PDF/CEOS_LAI_PROTOCOL_Aug2014_v2.0.1.pdf
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LAI
 1-sided leaf area (m2) per m2 ground area
 full canopy structural definition (e.g. for RS)
requires
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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
 Must be calibrated against field data
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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
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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
so for fixed 
fAPAR NDVI
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
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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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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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From Running et al.
(2004) MOD17
ATBD
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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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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.
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
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/
 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
CEOS Cal/Val Land Producst: lhttp://lpvs.gsfc.nasa.gov/
JRC/FastOpt: http://www.fastopt.com/topics/publications.html
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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)
greenup
•Dynam. global
veg. models driven
by phenology
maturity
•This phenol.
Based on NDVI
trajectory....
DOY 0
senescence
dormancy
DOY 365
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NPP
1km over W. Europe, 2001.
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