1. Biophysical variables estimates from Venµs, FORMOSAT2 & SENTINEL2 F. Baret, M. Weiss, R. Lopez, B. de Solan

2. Plan Introduction: biophysical variables Generic algorithms Adaptation to specific canopies Validation Conclusion

3. Introduction Biophysical variables are needed to: – Compress the available information (as Vis) – Be used as canopy state/type indicator – Be used within process models Variables accessible in the Vis-NIR (SWIR) – FCOVERgreen cover fraction – FAPARfraction of photosynthetically active radiation absorbed – LAIGreen Area Index – LAI.CabCanopy integrated chlorophyll content – LAI.CwCanopy integrated water content (SWIR) – BsSoil brightness – AlbedoAlbedo Generic/Specific products – Generic products (no ancillary information) – Dedicated products (when prior information is available ) Need estimates of associated uncertainties Starting from L2 Top of Canopy reflectance

4. Plan Introduction: biophysical variables Generic algorithms Adaptation to specific canopies Validation Conclusion

5. Generic products – Use simple (few input variables) RT model – Use neural networks Very computationally efficient Good performances when well trained Easy to update

6. RT model used 1D (SAIL) 2.5 D (GEOSAIL) … 3D (CLAMP) RMSE values associated to estimates of variables over GEOSAIL pseudo-observations (based on LUT techniques) More complex & realsitic models did not necessarily perform better when ancillary information is lacking

7. The training data base 41472 cases simulated R*( )=R( )(1+(MD( )+MI)/100)+AD( )+AI Reflectances contaminated by uncertainties 2% 0.01

8. Distribution of input variables (1/3) “realsitic” distributions of variables Tentative to get co-distributions with LAI

9. Cab: Feret et al. 2008 Cms: Feret et al. 2008 Cms: Literature Rs: Liu et al., 2002 Bs: Liu et al., 2002 Distribution of input variables (2/3)

10. LAI: Scurlock et al. 2002 LAIeff: VALERI 2000-2009 ALA: VALERI 2000-2009 hot: Lopez et al. 2010 Distribution of input variables (3/3)

11. Distribution of output reflectances

12. Distribution of target variables

13. Realism of simulations for LAI/FAPAR Good consistency between LAI/fAPAR

14. Typical architecture of the network 71 coefficients to adjust over 41472 x 2/3 cases The best of 5 initial guesses selected

15. Theoretical performances RMSEMode% LAI0.652.0 32.0 FAPAR0.0630.9 7.0 FVC0.0470.8 5.9 albedo0.0140.20 7.0 LAI.Cab37.5100 37.5 LAI.Cw0.0150.05 30.0 Bs0.471.0 47.0 Covers very different situations

16. Input & Output out of range 0010000000 0010000000 0110000000 0111000011 0111100111 0111111111 0111111100 0111111000 0111111000 1110000000 Input 2 Input 1 Min(Inuput1) Max (Inuput1) Min( Inuput 2) Max( Inuput 2) P tol min P tol max LAI-0.28.0 FAPAR-0.11.0 FVC-0.11.0 albedo0.030.30 LAI.Cab0450 LAI.Cw00.20 Bs0.53.5 Definition domain of Inputs (nD) Range of Outputs

17. Uncertainties model Adjust a NNT model with same inputs to describe theoretical uncertainties Performances of the uncertainties model

18. Plan Introduction: biophysical variables Generic algorithms Adaptation to specific canopies Validation Conclusion

19. Specific products Need to know crop/vegetation types 2 approaches – 3D RT modeling – Empirical approach Correction to “generic products” Calibration of specific transfer functions

20. Use of 3D RT models Need specific 3D (4D) models – Wheat – vineyard

21. Differences with 1D models: wheat case

22. Empirical approach Projet ADAM Roumanie - 2001 Indice foliaire estimé 1 2 3 4 5 6 Indice foliaire mesuré 654 321654 321 Temps Indice foliaire 3 2103 210 Bonne description de la dynamique

23. Several methods developed to estimate LAI/fAPAR at ground level Transmittance/ gap fraction – Hemispherical photos: CAN_EYE – Photos @ 57°: CAN_EYE – PAR@METER: suivi en continu en réseau communiquant (cultures) – @PAR: suivi en continu en réseau autonome (forets) – TRANSEPT: estimation instantanée à 57° (cultures)

24. 24/21 Start SetupImage selection Pre processing Classification End Processing & reporting CAN_EYE: Digital Hemispherical images

25. Photos @ 57°  to the rows RMSE=0.28 Calibrated over 4D wheat models

26. PAR@METER: continuous monitoring of FAPAR & PAI in web sensors PAR@METER systems - 7 transmitted sensors - 1 reflected - measurements every 5 minutes - 3 months autonomy (energy/memory) 26

27. @PAR: PAI autonomous monitoring network (no communication) incident transmittted Blue LED 57° orientation 3 m wire 6 sensors/system Temporal filtering for spatial consistency… or time integration

28. Plan Introduction: biophysical variables Generic algorithms Adaptation to specific canopies Validation Conclusion

29. Validation Campaigns in 2010 over – Barrax (Agriculture) (FORMOSAT/TM) – Crau/camargue (Gressland)(SPOT/TM) – Finland (pine forest) (SPOT) – Poland (agriculture) (SPOT) – France (Deciduous forest) (SPOT)

30. Plan Introduction: biophysical variables Generic algorithms Adaptation to specific canopies Validation Conclusion

31. L2 generic products development… – Probably not too much margins for improvements – Temporal smoothing of L2 products  L3 products L2 specific products – Need for automatic classification!!! – Need either empirical calibration Specific 3D (4D) models per vegetation type – Calibration of the 3D model. Probably not too sensitive – Account for row orientation – Mechanisms to speed up simulations (spectral dependency) – True multitemporal inversion (need dynamics) Spatial resolution probably too high for some patches – Need tests to decide whether RT assumptions are OK (variance) Validation: – importance of harmonization for meta analysis