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MERIS FAPAR: Towards Phenology Products
Nadine Gobron, Jan Musial, Michel Verstraete and Wolfgang Knorr.
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MERIS/ENVISAT
• Passive optical instrument of Earth Observation
• Primary mission: Ocean productivity
• Secondary missions: Atmosphere and
land surface characterization• Ground segment support (up to L2)
• Global coverage: ≤ 3 days (depends on latitude)
• Swath: 1150 km
• Spatial resolution: ± 300 m (FR) & ± 1200 m (RR)
• Spectral band positions, widths and gains are programmable
• Radiometric and spectral calibration on-board mechanisms
(white & pink Spectralon, Fraunhofer lines)
Source: http://envisat.esa.int/instruments/meris/
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from remote sensing
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Harvard
Ref: Turner et al. 2005FAPAR ≈ 1.-exp(-0.58<LAI>)<LAI> from PCA_LICOR Advanced procedure for spatio-temporal changes of local LAI
conifer/broad-leaf forest
Dahra
Ref: Fensholt et al. 2004FAPAR ≈ 1.-exp(-G(µ0)<LAI>/µ0)
<LAI> from PCA_LICOR
semi-arid grass savannah
Validation of JRC- FAPAR
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Two Roads
FAPAR
Phenological Parameters
Process Model
Phenological Indicators
DataAnalysis
FastOpt
(1) Dept of Earth Science, University of Bristol, UK, (2) FastOpt GmbH, Hamburg, Germany, (3) European Commission, DG Joint Research Centre, Institute for Environment and Sustainability, Global Environment Monitoring Unit, Italy, (4)
European Space Agency, ESRIN, Frascati, Italy, (5) Seconded to the Earth Observation Directorate, ESA-ESRIN, Frascati, Italy
Carbon cycle data assimilation using satellite-derived FAPAR
and a revisited phenology scheme for global applications
Wolfgang Knorr (1), Thomas Kaminski (2), Marko Scholze (1),
Bernard Pinty (3,5), Nadine Gobron (3), Ralf Giering (2), and Pierre-Philippe Mathieu (5)
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Using Cycle Data Assimilation System
W. Knorr et al, 2010, ‘Carbon cycle data assimilation with a generic phenology Model’, JGR, revised.
Challenges:• Satellite FAPAR data through an appropriate model provide an indirect constraint on
carbon and water fluxes.• Predictive models of the terrestrial biosphere are needed that simulate FAPAR, water and
carbon fluxes. This requires a (sub-) model of leaf phenology of all major global biomes.• Challenge is to design a terrestrial model such that:
– its process parameters can be estimated by means of a gradient-based optimisation algorithm, which requires smooth dependency on process parameters
– it satisfies simultaneously multiple observational constraints • Revised original phenology scheme to render the model suitable for gradient-based optimisation
(e.g. avoid sudden changes of leaf status by allowing spatial variability within a grid cell).• Assimilation of MERIS FAPAR product at seven sites simultaneously.• A single set of process parameters to match observations over all sites composed of a mix of seven
Plant Functional Types (PFTs).• Optimization of : - 14 parameters related to phenology
- 24 related to photosynthesis - not all are PFT specific [LAImax]
- additional parameters with no impact on FAPAR [Q10]
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The selected sites
xxx
xx
x
Dotted: prior; solid line: posterior FAPAR; crosses with error bars: MERIS FAPAR.
W. Knorr et al, 2010, ‘Carbon cycle data assimilation with a generic phenology Model’, JGR, revised.
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Assessment of Phenology
FAPAR
Phenological Parameters
Phenological Indicators
Process Model
DataAnalysis
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Challenges
• Missing data in the FAPAR record– instrument problems– lack of solar radiation (at high latitude in the winter)– clouds– snow and ice
• Noise in remote sensing products– radiometric accuracy– navigation and pointing accuracy
• Unexpected or extreme events– fire
• Growing seasons (GS)– may start and end on quite different dates each year– may occur more than once per year– may straddle 2 calendar years (e.g., Southern Hemisphere)– may not be synchronous over large areas– may start before or end after the period of observation
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Gap-filling and noise reduction
Four gap-filling methods have been tested using 10-days FAPAR products:
1. Kondrashov and Ghil (2006) based on Singular Spectrum Analysis (SSA). 3 steps: decomposition, grouping and reconstructing
2. Smoothing Spline from Reinsch (1967) ‘provides nice curves from discrete, noisy data’ (Craven, Wahba, 1979).
3. Lomb-Scargle Periodogram Hocke and Kampfer (2009) to construct the complex Fourier spectrum (Hamming window).
4. Same as 3 but with Kaiser-Bessel spectral window.
Musial et al., 2010, ‘Comparison of algorithms for gap-filling and noise reduction of a noisy, unevenly sampled time series ’, in preparation.
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Gap-filling and noise reduction
Hainich Site -10 days - 1 x 1 pixelMusial et al., 2010, ‘Comparison of algorithms for gap-filling and noise reduction of a noisy, unevenly sampled time series ’, in preparation.
oops
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Challenges
• Missing data in the FAPAR record– instrument problems– lack of solar radiation (at high latitude in the winter)– clouds– snow and ice
• Noise in remote sensing products– radiometric accuracy– navigation and pointing accuracy
• Unexpected or extreme events– fire
• Growing seasons (GS)– may start and end on quite different dates each year– may occur more than once per year – may straddle 2 calendar years (e.g., Southern Hemisphere)– may not be synchronous over large areas– may start before or end after the period of observation
Verstraete M. M., et al. (2008) 'An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products', Advances in Space Research, Volume 41, Issue 11, Pages 1773-1783.
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Methodology
Choice of the S-shaped model:• Sine wave, Weibull distribution, logistic function, arc tangent
trigonometric function, sigmoid• Mathematical function must be
– easy to parameterize to allow ‘local’ fits with variable shapes– continuous and analytically derivable to facilitate use of
accelerated optimal fitting procedures– computationally cheap to evaluate (including derivative)
• Select the parametric sigmoid:
• where– a sets the total amplitude of the curve,– b sets the sign and strength of the slope of the curve,– c sets the horizontal offset of the curve, and– d sets the vertical offset of the curve
dcxb
ay
)](exp[1
Verstraete M. M., et al. (2008) 'An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products', Advances in Space Research, Volume 41, Issue 11, Pages 1773-1783.
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Comparing results from different sensors
• Results for Moorreesburg, South Africa, in 2003.
SeaWiFS MERIS MODIS
Start date 1-10 Jun 21-31 May 11-20 May
End date 1-10 Dec 21-30 Nov 1-10 Dec
Length 193 (16-34) 194 (15-33) 214 (14-34)
ME
RI
SM
OD
IS
Se
aW
iFS
Verstraete M. M., et al. (2008) 'An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products', Advances in Space Research, Volume 41, Issue 11, Pages 1773-1783.
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Using MERIS 10-days products at global scale from 2003-2009 ….
First Test:
retrieve decade for which FAPAR is maximum
Next steps:
apply GLS algorithm for retrieving start and end dates using different ‘shape’ functions and criteria to define statistical properties of phenology.
Preliminary Results
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Decade Max FAPAR 2003
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Decade Max FAPAR 2004
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Decade Max FAPAR 2005
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Decade Max FAPAR 2006
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Decade Max FAPAR 2007
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Decade Max FAPAR 2008
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Decade Max FAPAR 2009
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Anomaly in Decade Max FAPAR 2009
-20 -15 -10 -5 0 5 10 15 20
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Tropical Climatic Zone
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Summary and Perspective
MERIS FAPAR products are available since 04/2002
Comparisons of gap-filling algorithms have been done at local scale and can be done to 10-days global products.
Through CCDAS approach, a revised original phenology scheme has been proposed by Knorr el al (2010) and first global products show encouraging results.
Assessment of phenological parameters at global scale (apply GLS algorithm for retrieving start and end dates using different ‘shape’ functions and criteria to define statistical properties of phenology)
Results from these approaches can be compared and benchmarked against ground-based measurements (Collaboration welcome).