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