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:– 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)– FCOVER green cover fraction– FAPARfraction of photosynthetically active radiation absorbed– LAI Green Area Index– LAI.Cab Canopy integrated chlorophyll content– LAI.Cw Canopy integrated water content (SWIR)– Bs Soil brightness– Albedo Albedo

• 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

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

Generic products– Use simple (few input variables) RT model– Use neural networks

• Very computationally efficient• Good performances when well trained• Easy to update

SENTINEL2

RT model used

• 1D (SAIL)• 2.5 D (GEOSAIL)• … 3D (CLAMP)

Product SAIL (2)

GEOSAIL (2)

LAI 1.25 1.40 FAPAR 0.20 0.24 FVC 0.22 0.08 Albedo 0.032 0.021 LAI.Cab (µg.cm-2) 66.7 73.1 LAI.Cw (g.cm-2) 0.032 0.033 Bs 0.47 0.68

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

The training data base

Canopy ReflectanceModelSAIL

Leaf OpticalProperties Model

PROSPECT

Reference SoilOptical Properties

N, Cab, Cw, Cm, Cbp LAI, ALA, HOT

ObservationGeometry(qs;qv;f)

ReflectanceRmod (l;qs;qv;f)

Absorptance (l)

FAPAR

FVC

albedo

Bs

Rb(l)

rl(l)tl(l)

41472 cases simulated

R*(l)=R(l)(1+(MD(l)+MI)/100)+AD(l)+AI

Reflectances contaminated by uncertainties

2% 2% 0.01 0.01

Distribution of input variables (1/3)

“realsitic” distributions of variablesTentative to get co-distributions with LAI

Cab: Feret et al. 2008 Cms: Feret et al. 2008Cms: Literature

Rs: Liu et al., 2002 Bs: Liu et al., 2002

Distribution of input variables (2/3)

LAI: Scurlock et al. 2002LAIeff: VALERI 2000-2009

ALA: VALERI 2000-2009

10-6

10-4

10-2

100

102

104

10-3

10-2

10-1

100

101

102

Wh

MzSg

Vy

Sf

SyTo

Ol

Pe

Lt Bt

WhMz

SgVySf SyTo

Ol

Pe

LtBt

10-6

10-4

10-2

100

102

104

10-2

10-1

100

101

102

103

104

Wh

MzSg

Vy

Sf SyToOl

Pe

Lt Bt

WhMzSg

Vy

Sf SyToOlPe

LtBt

D

L ro

ws

D

R le

af

hot: Lopez et al. 2010

Distribution of input variables (3/3)

Distribution of output reflectances

Distribution of target variables

Realism of simulations for LAI/FAPAR

Good consistency between LAI/fAPAR

Typical architecture of the network

Cos(f)

Cos(qs)

Cos(qo)

R560

R665

R705

R740

R775

R842

R865

R1610

R2190

Norm

Norm

Norm

Norm

Norm

Norm

Norm

Norm

Norm

Norm

Norm

Norm

S

S

S

S

S

VariableNormL

71 coefficients to adjust over 41472 x 2/3 cases

The best of 5 initial guesses selected

Theoretical performancesRMSE Mode %

LAI 0.65 2.0 32.0FAPAR 0.063 0.9 7.0

FVC 0.047 0.8 5.9albedo 0.014 0.20 7.0

LAI.Cab 37.5 100 37.5LAI.Cw 0.015 0.05 30.0

Bs 0.47 1.0 47.0

Covers verydifferent situations

Input & Output out of range

0 0 1 0 0 0 0 0 0 00 0 1 0 0 0 0 0 0 00 1 1 0 0 0 0 0 0 00 1 1 1 0 0 0 0 1 10 1 1 1 1 0 0 1 1 10 1 1 1 1 1 1 1 1 10 1 1 1 1 1 1 1 0 00 1 1 1 1 1 1 0 0 00 1 1 1 1 1 1 0 0 01 1 1 0 0 0 0 0 0 0

Input 2

Input 1Min(Inuput 1) Max (Inuput 1)Min(

Inuput

2)

Max(

Inuput

2)

Ptolmin Ptol

max

LAI -0.2 8.0FAPAR -0.1 1.0FVC -0.1 1.0albedo 0.03 0.30LAI.Cab 0 450LAI.Cw 0 0.20Bs 0.5 3.5

Definition domain of Inputs (nD) Range of Outputs

Uncertainties model

• Adjust a NNT model with same inputs to describe theoretical uncertainties

Performances of the uncertainties model

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

Specific products

• Need to know crop/vegetation types

• 2 approaches– 3D RT modeling– Empirical approach

• Correction to “generic products”• Calibration of specific transfer functions

Use of 3D RT models

• Need specific 3D (4D) models– Wheat

– vineyard

0.2 0.4 0.6 0.80.2

0.3

0.4

0.5

0.6

0.7

0.8

NIR SAIL

NIR

Par

cino

py

erectplano

0.2 0.4 0.6 0.80.2

0.3

0.4

0.5

0.6

0.7

0.8

NIR SAIL

NIR

Par

cino

py

erectplano

Differences with 1D models: wheat case

0.04 0.06 0.08 0.1 0.120.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0.11

Red SAIL

Red

Par

cino

py

erectplano

0.04 0.06 0.08 0.1 0.12

0.04

0.06

0.08

0.1

0.12

Red SAIL

Red

Par

cino

py

erectplano

Empirical approachProjet ADAM Roumanie - 2001

0 1 2 3 4 5 60

1

2

3

4

5

6

11

11

1111

1112

12

1212

1213

13

13

1313

1314

14

14

14

15

15

15

15

1516

16

16

16

16

17

1717

17

17

17

17

18

18

18

18

18

1819

19

1919

19

19

1920

20

2020

2020

2021

21

21

21

21

2122

22

22

22

22

2223

23

23

2323

2324

24

24

2424 24

2425

25

25

25

25

25

2526

26

26

2626

26

26

27

27

27

2727

27

2728

28

28

28

28

2829

29

29

29

29

30

30

30

31

31

31

31

31

3232

3232

32

32

32

3333

3333

33

3334

3434

34

34

34

343535

35

35

3535

353636

36

36

36

36

363737

37

37

37 37

3738

38

38

38

3839

39

39

3940 40

40

40

40

40

Measured LAI

estim

ated

LA

I

R2=0.85622

RMSE=0.55737

n=178

Validation units

Indi

ce fo

liaire

esti

mé

1 2 3 4 5 6Indice foliaire mesuré

6

5

4 3

2

1

0 500 1000 1500 2000 25000

1

2

3

4

5

6

7Class 1

Sum of Temperature

LAI

Temps

Indi

ce fo

liaire

3 2

1

0

Bonne description de la dynamique

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)

24/21

Start Setup Imageselection

Preprocessing

Classification EndProcessing & reporting

CAN_EYE: Digital Hemispherical imagesLA

I CAN

_EYE

LAI planimetre

Photos @ 57° to the rows

q=57.5°

FOV

RMSE=0.28

Calibrated over 4D wheat models

Incident

Transmis

Réfléchi

Grappes

Concentrateur

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

@PAR: PAI autonomous monitoring network (no communication)

incidenttransmittted

Blue LED57° orientation3 m wire6 sensors/system

Temporal filtering for spatial consistency…or time integration

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

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)

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

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