Development and assessment of leaf area index algorithmsfor the Sentinel-2 multispectral imager

Richard Fernandes1, Marie Weiss2, Fernando Camacho3, Beatrice Berthelot4, Fred Baret2, Riccardo Duca5

1: CCRS, Government of Canada; 2: INRA, France; 3: EOLAB, Spain; 4: Magelllium, France; 5: ESTEC, European Space Agency

Objectives

Description of Validation Sentinel 2 (VALSE2) experiment - focus on LAI algorithm validation.

Description of two LAI algorithms applicable to S2 MSI INRA Neural Network inversion of PROSAILH CCRS Red-Edge analytical solution

Sentinel 2 Mission Requirements

VALSE2 – Review of Algorithms

L=low , P=Partial, F=Full satisfaction or Mission requirements.

Fernandes et al., VALSE2 Algorithm Survey, CCRS, 2014.

INRA NNET Algorithm

Baret et al., VALSE2 CFI Algorithm Theoretical Basis Document, INRA, 2014.

CCRS LAI Algorithm

,

,

,,

, , ,

Continuous radiative transfer equation:

The probability a photon recollides in the canopyat the infinite scattering order.

, ,, ,

Eigenfunction decomposition:Interaction

Scattering

Why do we care about p?

is invariant to angular or spectral variation of , is analytically related to LAI (Stenberg, 2006)

0 2 4 6 8 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

LAI

p

0.600.650.700.750.800.850.900.951.01.05

0 1 2 3 4 5 6 7 8 90

1

2

3

4

5

6

7

8

9

[1-exp(-clumping*LAI)]*[a0+a1/(1-p)]

LAI

ErectophilePlanophileUniform

Clumping

Fernandes and Gitelson, submitted to RSE.

Relating p to S2 MSI reflectance

is a function of(1) black soil reflectance and(2) leaf albedo

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

0.01

0.02

0.03

0.04

0.05

0.06

0.07

NDVI S2 Band 5, Band 6

N(7

25nm

)

+30º0º

-30º

VZA

0.5 0.55 0.6 0.65 0.7 0.750.02

0.04

0.06

0.08

0.1

N(6

95nm

)

NDVI S2 Band 4, Band 5

Red-edge NDVI for S2 closely related to = .

Fernandes et al., VALSE2 CCRS Red-Edge ATBD, CCRS, 2014.

CCRS LAI Algorithm

For each , and soil reflectance

Estimate p and LAI

Is p valid?

Add LAI(p, ) to solution

Estimate soil reflectance from

regions with lowest 5% NDVI

Estimate CHL from red-edge

CHL index

Model leaf albedo, from CHL using

PROSPECT5PUTS

B4,B5,B6urfaceectance

+unds on CHL, OSPECT meters, ,soil refl.

Inputs

YES

NO

, .

LAI Estimation

Radiative Transfer Verification

Fernandes R and Gitelson A

1:1 line

1020304050607080

CHL g/cm2

CCRS Red-EdgeINRA NNET

roducer Validation

0 1 2 3 4 5 60

1

2

3

4

5

6

L Estimated

L A

ctua

l

Fernandes R and Gitelson A

CCRS Red-Edge(maize, soybean)

INRA NNET(maize)

N=300, MAE=0.45=34, RMSE=1.13

idation of Sentinel 2: VALSE2

VALSE2 Imagery

AG

RIS

AR

AG

RIS

AR

SPA

RC

2003

SPA

RC

2003

SPA

RC

2004

CEF

LES

CEF

LES

EAG

LE

EAG

LE

Sen

2FLE

X

Sen

2FLE

X

SEN

3EXP

SEN

3EXP

SEN

3EXP

CAIS

AH

S

HYM

AP

RO

SIS

AH

S

AH

S

HYP

ER

AH

S

CASI

AH

S

CASI

AH

S

CASI

SASI

ometry No L2 Yes No No No Yes yes L2 Not available

Yes No Yes Yes Yes

saics No No No no no yes yes yes

mporal Yes No No No No Yes Yes Yes

diometry Cloudy

yes * * * Yes Yes Yes

ectral **)

3 3 2 2 1 1 1

ESU yes yes Yes yes Yes Yes Yes (Barrax) No San Rossore

ority in the ocessing

No No No barrax

San Rossore

VALSE2 Ground Reference Data

XPEX

SAR

LAI WC CHL

VALSE2 LAI Validation V1A NNET CCRS Red-Edge

CHL significantlyoverestmated (>>60ug/cm2)

Saturation in retrieval due to saturationof input bands

VALSE2 LAI Validation V2

et al., VALSE2 Validation Report, EOLAB, 2014.

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8

LAI R

ed-E

dge

v2

Ground Measurement

SEN3EXP CASI

N=45 R2=-0.53 RMSE=1.76 B=-0.58 S=1.61

A NNET CCRS Red-Edge

Conclusions

Better co-ordination and careful processing of reference datasets so radiometry and in-situ measurements meet product specifications

Need to perform forest validation (BOREAS, Harz)

Sentinel Level 2P implementing NNET and CCRS algorithms but users must have patience: MODIS LAI had ~1 version/2 years.

A t f LAI l ith i l f fitt t

600 650 700 750 800 850 9000

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Wavelength (nm)

Rel

ativ

e sp

ectra

l res

pons

e

2 MSI and S3 OLCI Red-Edge

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

ME

RIS

8

ME

RIS

9

ME

RIS

10

MSI

4

MSI

5

MSI

6

MSI

8

Bi-d

irect

iona

l Ref

lect

ance

How did we estimate leaf CHL?

Why does CCRS Red-Edgeometimes underestimate LAI?

30

35

40

45

50

55

60

65

70

0

1

2

3

4

5

ttawa cal/val site Equivalent CHL ug/cm2 LAI