GEOGG142 GMESGlobal vegetation parameters from EO

Dr. Mat Disneymdisney@geog.ucl.ac.ukPearson Building room 113020 7679 0592www.geog.ucl.ac.uk/~mdisney

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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

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.• Where 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??

Overview LAI/fapar Space Products

Projects/InstitutionSensors/Period

Input data Output product Retrieval Method References

JRC-FAPAR SeaWiFSESA MERIS(07/97-04/12)

Top of Atmosphere (TOA) BRFs in blue, red and near-infrared 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 FAPAR (Black leaves)

Parametric relation with LAI as function as Land cover type.

Plummer et al. (2006)

CYCLOPESVEGETATION

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-TIPMODIS/MISR(00-On going)

Broadband Surface albedo in visible and near-infrared bands.

8-(16) days Standard FAPAR or/& Green FAPAR for direct or/& diffuse incoming radiation

Inversion of two-stream model using the Adjoint and Hessian codes of a cost function.

Pinty et al. (2007)

GEOLAND2/GLSVEGETATION/PROBA-V(99-2012/on going)

Normalized surface reflectance in red and near-infrared bands

FAPAR at 10:00 solar local time

Neural network based on CYCLOPES and MODIS products

Baret et al (2010)

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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?  – 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 soonNPP: http://npp.gsfc.nasa.gov/mission_details.htmlESA Sentinels: http://www.esa.int/Our_Activities/Observing_the_Earth/GMES/Sentinel-2http://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:

· 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· 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· 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

day

fAPARIPARAPAR

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APARPSN

APARNPP

· 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

NDVIfAPAR

so for fixed

dayIPARPSN

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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 :mggross YYf

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mggross YYf

· 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 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

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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

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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.· 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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ReferencesMyneni 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. 14784-14789Myneni 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.orgCEOS Cal/Val Land Producst: lhttp://lpvs.gsfc.nasa.gov/JRC/FastOpt: http://www.fastopt.com/topics/publications.html

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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

• 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?

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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

• This phenol. Based on NDVI trajectory....

greenup maturity

senescence dormancy

DOY 0

DOY 365

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NPP

1km over W. Europe, 2001.