1. Dublin - 18 June 2010 –CEOs LPV Phenology1 MERIS FAPAR: Towards Phenology Products Nadine Gobron, Jan Musial, Michel Verstraete and Wolfgang Knorr.

2. Dublin - 18 June 2010 –CEOs LPV Phenology2 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/

3. Dublin - 18 June 2010 –CEOs LPV Phenology3 from remote sensing

4. Dublin - 18 June 2010 –CEOs LPV Phenology4 Harvard Ref: Turner et al. 2005 FAPAR ≈ 1.-exp(-0.58 ) from PCA_LICOR Advanced procedure for spatio-temporal changes of local LAI conifer/broad-leaf forest Dahra Ref: Fensholt et al. 2004 FAPAR ≈ 1.-exp(-G(µ0) /µ0) from PCA_LICOR semi-arid grass savannah Validation of JRC- FAPAR

5. Dublin - 18 June 2010 –CEOs LPV Phenology5 Two Roads FAPAR Phenological Parameters Process Model Phenological Indicators Data Analysis

6. 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)

7. Dublin - 18 June 2010 –CEOs LPV Phenology7 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 [LAI max ] - additional parameters with no impact on FAPAR [Q10]

8. Dublin - 18 June 2010 –CEOs LPV Phenology8 The selected sites x x x x x 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.

9. Dublin - 18 June 2010 –CEOs LPV Phenology9 Assessment of Phenology FAPAR Phenological Parameters Phenological Indicators Process Model Data Analysis

10. Dublin - 18 June 2010 –CEOs LPV Phenology10 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

11. Dublin - 18 June 2010 –CEOs LPV Phenology11 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.

12. Dublin - 18 June 2010 –CEOs LPV Phenology12 Gap-filling and noise reduction Hainich Site -10 days - 1 x 1 pixel Musial et al., 2010, ‘Comparison of algorithms for gap-filling and noise reduction of a noisy, unevenly sampled time series ’, in preparation. oops

13. Dublin - 18 June 2010 –CEOs LPV Phenology13 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.

14. Dublin - 18 June 2010 –CEOs LPV Phenology14 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 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.

15. Dublin - 18 June 2010 –CEOs LPV Phenology15 Comparing results from different sensors Results for Moorreesburg, South Africa, in 2003. SeaWiFSMERISMODIS Start date1-10 Jun21-31 May11-20 May End date1-10 Dec21-30 Nov1-10 Dec Length193 (16-34)194 (15-33)214 (14-34) MERIS MODIS SeaWiFS 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.

16. Dublin - 18 June 2010 –CEOs LPV Phenology16 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

17. Dublin - 18 June 2010 –CEOs LPV Phenology17 Decade Max FAPAR 2003

18. Dublin - 18 June 2010 –CEOs LPV Phenology18 Decade Max FAPAR 2004

19. Dublin - 18 June 2010 –CEOs LPV Phenology19 Decade Max FAPAR 2005

20. Dublin - 18 June 2010 –CEOs LPV Phenology20 Decade Max FAPAR 2006

21. Dublin - 18 June 2010 –CEOs LPV Phenology21 Decade Max FAPAR 2007

22. Dublin - 18 June 2010 –CEOs LPV Phenology22 Decade Max FAPAR 2008

23. Dublin - 18 June 2010 –CEOs LPV Phenology23 Decade Max FAPAR 2009

24. Dublin - 18 June 2010 –CEOs LPV Phenology24 Anomaly in Decade Max FAPAR 2009 -20 -15 -10 -5 0 5 10 15 20

25. Dublin - 18 June 2010 –CEOs LPV Phenology25 Tropical Climatic Zone

26. Dublin - 18 June 2010 –CEOs LPV Phenology26 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).

27. Dublin - 18 June 2010 –CEOs LPV Phenology27 MERIS Global Scale

28. Dublin - 18 June 2010 –CEOs LPV Phenology28 FAPAR from MERIS data Satellite DATA JRC-FAPAR Algorithm based on physics MERIS Govaerts, Y. et. al. (1999) 'Designing Optimal Spectral Indices: A Feasibility and Proof of Concept Study', International Journal of Remote Sensing, 20, 1853-1873. Gobron, N. et. al. (1999) 'The MERIS Global Vegetation Index (MGVI): Description and Preliminary Application', International Journal of Remote Sensing, 20, 1917-1927.

29. Dublin - 18 June 2010 –CEOs LPV Phenology29 Daily FAPAR product Satellite DATA JRC-FAPAR Algorithm based on physics MERIS Govaerts, Y. et. al. (1999) 'Designing Optimal Spectral Indices: A Feasibility and Proof of Concept Study', International Journal of Remote Sensing, 20, 1853-1873. Gobron, N. et. al. (1999) 'The MERIS Global Vegetation Index (MGVI): Description and Preliminary Application', International Journal of Remote Sensing, 20, 1917-1927.

30. Dublin - 18 June 2010 –CEOs LPV Phenology30 1 st global results: Difference posterior - prior JAN APR JUL OCT W. Knorr et al, 2010, ‘Carbon cycle data assimilation with a generic phenology Model’, JGR, revised.

31. Dublin - 18 June 2010 –CEOs LPV Phenology31 Methodology In practice, when analyzing FAPAR decadal (10 days) records, The start of the growing season is deemed to occur, within a given 12-month period, on the first decade with valid FAPAR observations within the moving window period for which the absolute value of the model amplitude parameter a is maximal and the slope b (time derivative of FAPAR) is positive, and The end of the growing season is deemed to occur, within a given 12-month period, on the last decade with valid FAPAR observations within the moving window period for which the absolute value of the model amplitude parameter a is maximal and the slope b (time derivative of FAPAR) is negative. 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.

32. Dublin - 18 June 2010 –CEOs LPV Phenology32 Regional results Growing season length and cumulative FAPAR value over the growing season, for Romania and Bulgaria, in 2000 (worst year) and 2004 (best year) 20002004 100 km Mixed and coniferous forests Non-irrigated arable land 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.

33. Dublin - 18 June 2010 –CEOs LPV Phenology33 Reduction in Uncertainty of NPP Prior: without FAPAR assimilation Posterior: after FAPAR assimilation NPP: Net Primary Productivity W. Knorr et al, 2010, ‘Carbon cycle data assimilation with a generic phenology Model’, JGR, revised.

34. Dublin - 18 June 2010 –CEOs LPV Phenology34 1 st global results: Fit to atmospheric CO 2 W. Knorr et al, 2010, ‘Carbon cycle data assimilation with a generic phenology Model’, JGR, revised.

35. Dublin - 18 June 2010 –CEOs LPV Phenology35 Gap-filling and noise reduction Over CarboEurope sites Musial et al., 2010, ‘Comparison of algorithms for gap-filling and noise reduction of a noisy, unevenly sampled time series ’, in preparation.