Exploitation of MODIS and MISR Surface Albedos in Support of SVAT Models

1. Exploitation of MODIS and MISR Surface Albedos in Support of SVAT Models Bernard Pinty, T. Lavergne, T. Kaminski, O. Aussedat, N. Gobron and M. Taberner with contributions from R. Giering, M. M. Verstraete, M. Vossbeck and J-L. Widlowski EC-JRC Institute for Environment and Sustainability, Ispra, Italy FastOpt, Hamburg, Germany LANDFLUX meeting, Toulouse, May 28-31, 2007 JRC – Ispra

2. Complex land-surface RT effects on short term climate: the snow case with ECMWF/NCEP Ref: Viterbo and Betts, 1999, JGR “…weather forecasts significantly underestimated air temperatures over boreal, sometimes by as much as 10-15 C…” Ref: http://eobglossary.gsfc.nasa.gov/

3. Complex land-surface RT effects on short term climate: the snow case with ECMWF/NCEP “…—the BOREAS team found that the models were overestimating albedo (the amount of light reflected by the surface). …” Ref: Viterbo and Betts, 1999, JGR Ref: http://eobglossary.gsfc.nasa.gov/

4. How does radiation redistribute energy between the atmosphere and the biosphere? Scattered Fluxes by the surface Absorbed Fluxes in Vegetation Absorbed Fluxes in Soil

5. What do we measure at global scale that we should model as well? Albedo of the surface in the VIS and NIR (MODIS and MISR) Absorbed Flux by green Vegetation in the VIS (FAPAR)

7. January 2001

8. MISR low & MODIS high MISR high & MODIS low

9. January 2001

10. Ratio of the mean values

11. VIS VIS+NIR How do we model the absorbed fluxes in vegetation and soil ? Correct partitioning between the flux that is absorbed : 1- in the vegetation layer 2- in the background Assessment of the fraction of solar radiant flux that is scattered (albedo) by, transmitted through and absorbed in the vegetation layer Pinty etal., (2006): Journal of Geophysical Research, doi:10.1029/2005JD005952

12. Needs for Land surface Models • Update/improve the current Land Surface schemes describing the radiation transfer processes in vegetation canopies see 2-stream model by Pinty et al. JGR (2006).

13. Requirements from a 2-stream model • 3 (effective) state variables: • Optical depth: LAI • single scattering albedo : Leaf reflectance+ Leaf transmittance 3. asymmetry of the phase function Leaf reflectance/transmittance • 2 boundary conditions: • Top:Direct and Diffuse atmospheric fluxes (known) • Bottom :Flux from background Albedo (unknown) Pinty etal., (2006): Journal of Geophysical Research, doi:10.1029/2005JD005952

14. The concept of effective LAI Effects induced by internal variability of LAI

15. Structure factor The concept of effective LAI

16. Comparing/constraining or assimilating the radiation fluxes retrieved from RS against those generated by GCMs is not valid when using the true state variables in the GCMs simulations Pinty etal., (2004): Journal of Geophysical Research, doi:10.1029/2004JD005214

17. Needs for SVAT Models • Update/improve the current Land Surface schemes describing the radiation transfer processes in vegetation canopies see 2-stream model by Pinty et al. JGR (2006). • Prepare for the ingestion/assimilation of RS flux products into Land Surface schemes Retrieve 2-stream model parameters from RS flux products

18. Retrievals of model Parameters for Land surface schemes The inverse problem can be formulated in order to find solutions optimizing all the available information i.e., inferring statistically the state of the system Towards an integrated system for the optimal use of remote sensing flux products

19. RS Flux products, e.g., Albedo Vis/NIR and/or FAPAR noted • Updated/benchmarked 2-stream model from Pinty • et al. JGR (2006) noted • A priori knowldege/guess on model parameters • noted uncertainty on the RS products is specified in the measurement set covariance matrix uncertainty associated the model parameter is specified via a covariance matrix INPUTS : prior knowledge

20. Model parameters Parameter knowledge 2-stream model measurements Uncertainty measurements Uncertainty parameters • Computer optimized Adjoint and Hessian model of cost function from automatic differentiation technique • Assume Gaussian theory • Posterior uncertainties on retrieved parameters are estimated from the curvature of Optimization/assimilation scheme Pinty etal., (2007): Journal of Geophysical Research, in press

21. OUTPUTS: posterior knowledge • PDFs of all 2-stream model parameters: a posteriori uncertainty covariance matrix • Assessement of all fluxes predicted by the 2-stream model and their associated uncertainty: Pinty etal., (2007): Journal of Geophysical Research, in press

22. a priori ‘green’ leaves in case snow occurs prior knowledge on model parameters Pinty etal., (2007): Journal of Geophysical Research, in press

23. Application over BOREAS NSA-OBS

24. Application over BOREAS: Measurements MODIS Snow indicator (MOD10A2)

25. Application over NSAOBS: model parameters

26. Application over NSAOBS: model parameters MODIS /Terra FAPAR MISR /Terra FAPAR

27. Application over NSAOBS: model parameters

28. Application over NSAOBS: radiant fluxes

29. Application over NSAOBS: radiant fluxes a priori ‘green’ leaves

30. Application over NSAOBS: radiant fluxes MODIS FAPAR MERIS FAPAR JRC-FAPARSeaWiFS MISR FAPAR a priori ‘green’ leaves

31. current inversion based on TERRA albedos Application over NSAOBS: radiant fluxes MERIS FAPAR a priori ‘green’ leaves

32. Agriculture Semi-desert

33. Shrubland-woodland Deciduous broadleaf forest

34. Shrubland-woodland Deciduous GREEN broadleaf

35. Deciduous needleleaf forest Evergreen needleleaf forest

36. Concluding remarks 1.Computer efficient inversion package has been designed and tested : assessment of uncertainty on all retrievals 2. This integrated package can be used for various purposes : retrieval of parameters from RS products, validation of RS products, assimilation of RS products into Land surface schemes. 3. Capability to generate global surface model parameters ensuring full consistency with measured (uncorrelated) fluxes from various sources: spectral albedos from MODIS-MISR (and any other sources) and FAPAR from SeaWiFS/MERIS. 4. Estimating radiant flux and surface parameters in the presence of snow .