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

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Presentation on theme: "Biophysical variables estimates from Venµs, FORMOSAT2 & SENTINEL2 F. Baret, M. Weiss, R. Lopez, B. de Solan."— Presentation transcript:

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

10 LAI: Scurlock et al LAIeff: VALERI ALA: VALERI hot: Lopez et al 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 x 2/3 cases The best of 5 initial guesses selected

15 Theoretical performances RMSEMode% LAI FAPAR FVC albedo LAI.Cab LAI.Cw Bs Covers very different situations

16 Input & Output out of range Input 2 Input 1 Min(Inuput1) Max (Inuput1) Min( Inuput 2) Max( Inuput 2) P tol min P tol max LAI FAPAR FVC albedo LAI.Cab0450 LAI.Cw00.20 Bs 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 Indice foliaire estimé Indice foliaire mesuré Temps Indice foliaire Bonne description de la dynamique

23 Several methods developed to estimate LAI/fAPAR at ground level Transmittance/ gap fraction – Hemispherical photos: CAN_EYE – 57°: CAN_EYE – suivi en continu en réseau communiquant (cultures) 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 57°  to the rows RMSE=0.28 Calibrated over 4D wheat models

26 continuous monitoring of FAPAR & PAI in web sensors 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


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