gcm 12/12/06: retrieval of biophysical (vegetation) parameters from eo sensors dr. mat disney...

GCM 12/12/06: Retrieval of biophysical (vegetation) parameters from EO sensorsDr. Mat Disney

mdisney@geog.ucl.ac.uk

Pearson Building room 216

020 7679 0592

www.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 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)

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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) 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: http://modis.gsfc.nasa.gov/data/atbd/land_atbd.html

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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 (link to ET)

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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

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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 ~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

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 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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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

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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/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

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

44Canadell et al. 2000

Data-ModelFusion

[Using multiplestreams of datasets withparameter optimization]

C stock and flux measurementsInventory analysesProcess-based informationClimate dataRemote sensing informationCO2 column from space

Inverse modelingProcess-based modelingRetrospective and forward analyses

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Carbon: how??

• Measure fluxes using eddy-covariance towers

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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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-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 0.680.34 Gt C/yr.-From Myneni et al. PNAS, 98(26),14784-14789http://cybele.bu.edu/biomass/biomass.html

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Dominant Controlswater availability 40%

temperature 33%solar radiation 27%

Total vegetated area: 117 M km2

Limiting factors

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Bottom line

- 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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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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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 ????

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References• 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

• Running, S.W., Nemani, R., Glassy, J.M. (1996) MOD17 PSN/NPP Algorithm Theoretical Basis Document, NASA.

• http://cybele.bu.edu• http://www.globalcarbonproject.org