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Biophysical variables estimates from Venµs , FORMOSAT2 & SENTINEL2

Biophysical variables estimates from Venµs , FORMOSAT2 & SENTINEL2. F. Baret, M. Weiss, R. Lopez , B. de Solan. Plan. Introduction: biophysical variables Generic algorithms Adaptation to specific canopies Validation Conclusion. Introduction. Biophysical variables are needed to:

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Biophysical variables estimates from Venµs , FORMOSAT2 & SENTINEL2

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  1. Biophysical variables estimatesfromVenµs, 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 usedwithinprocessmodels • Variables accessible in the Vis-NIR (SWIR) • FCOVER green cover fraction • FAPAR fraction of photosynthetically active radiation absorbed • LAI Green Area Index • LAI.Cab Canopyintegratedchlorophyll content • LAI.Cw Canopyintegrated water content (SWIR) • Bs Soilbrightness • Albedo Albedo • Generic/Specificproducts • Genericproducts (no ancillary information) • Dedicatedproducts (whenprior information isavailable ) • Needestimates of associateduncertainties • Startingfrom L2 Top of Canopyreflectance

  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 Reflectances contaminated by uncertainties R*()=R()(1+(MD()+MI)/100)+AD()+AI 2% 2% 0.01 0.01

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

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

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

  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 Covers very different situations

  16. Input 2 2) 0 0 1 0 0 0 0 0 0 0 Inuput 0 0 1 0 0 0 0 0 0 0 Max( 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 2) 1 1 1 0 0 0 0 0 0 0 Inuput Min( Inuput 1) Max ( Inuput 1) Input 1 Min( Input & Output out of range 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 6 5 4 3 2 1 1 2 3 4 5 6 Indice foliaire mesuré Indice foliaire 3 2 1 0 Bonne description de la dynamique Temps

  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 réseaucommuniquant (cultures) • @PAR: suivi en continu en réseauautonome (forets) • TRANSEPT: estimation instantanée à 57° (cultures)

  24. CAN_EYE: Digital Hemispherical images Start Setup Image selection Preprocessing Classification Processing & reporting End

  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)

  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. Conclusion • 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

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