A Multiscale LAI Product from Sentinel Data

Richard Fernandes, Khalid Omari, Francis Canisius CCRSFred Baret INRA

Elisabeth Pattey, Jianqui Liu Agriculture Canada

LAI Requirements

• GCOS/CEOS

– <=w000m spatial resolution

– <=monthly temporal resolution

– <=20% or 1 unit at biome scale

• Regional

– <=100m

– <=bi-weekly

– <=20% or 1 unit on a local (100km2) basis

Temporal ResolutionAugust 95% Clear Sky Waiting Time

Sentinel 2A Only

Sentinel 2A 2B Sentinel 3A 3B

Sentinel 3A Only

Spatial ResolutionLAI Difference between 30m and 1km

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Re

lati

ove

Ab

solu

e L

AI E

rro

r %

Landsat Reference Scene

Total Scaling Error

Land Cover Scaling

Reflectance Scaling

Fernandes et al., 2003.

AccuracyLAI Bias in comparison to refernce

Canisius et al., 2010

Red Edge reduces LU Bias in LAI

Gitelson et al., 2008 Herrmann et al., 2011

But …

• What controls the red-edge vs LAI relationship?

• Why do multispectral approximations work?

• Can we consistently scale beween ~30m and ~300m resolution?

Knyzhakin’s Spectral Invariant Model

• Assuming black soil homogenous canopy

• pi = probability a photon recollides during ith scattering order

• qi = probability a photon escapes in direction i given it has not recollied duringith scattering order

• L = element single scattering order• = uncollided tranmissivity

effL

Leff

L

LL

LLLLbs

p

qp

p

qpqp

qpppqppqpR

1

11

1

111

...1111,,

21

1

inf

2

inf2111

3321

3

221211121

P

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0.9

1

0 1 2 3 4 5 6

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1

0 1 2 3 4 5 6

Knyzhakin et al., 2010Lewis Disney 2007

Is Knyzhakin’s model valid?

y = 0.3913x + 0.1426

y = 0.5309x + 0.1858

y = 0.6352x + 0.1874

y = 0.7008x + 0.1795

y = 0.7427x + 0.1708

y = 0.77x + 0.1635

y = 0.7881x + 0.1579

y = 0.8003x + 0.1539

0.00

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0.00 0.20 0.40 0.60 0.80

DH

R/w

L

DHR

Corn Canopy710-780 nm

211 11 qpRpR

effbseff

L

bs

Monte Carlo Pro Sail

Lewis Disney 2007

Solving for LAI in red-edge

• Normalized Difference in Red Edge

• REIP

BS

BS

L

L

L r

rp 1

1I

L

I

L

I

L

p2

1

Using:

21212121 ,,,,, sBS rrr

Ottawa Mixed Land Use Site

Multi-spectral Equivalents

NDRE soil effect

Variability in w

• Uncertainty in p proportional to (1/w) for both NDRE and REIP

• Range in REIP relative to measurement noise >> range in NDRE relative to measurement noise

• Use NDRE

LOPEX fresh leavesPROSPECT5 0,002PROSPECT5 0,016Water 0,0131Car = 0,1*ChlBrown = 0,001

LAI(NDRE) from CHRISLAI June 5 Nadir

LAI June 5 Nadir,

June 6 -33d, June 6+55d

0 1 2 3 4

LAI(REIP) from CHRIS

0 1 2 3 4

LAI June 5 Nadir

LAI June 5 Nadir,

June 6 -33d, June 6+55d

Multi-scale Strategy(following Knyzhakin 1997; Tian 2002)

• a few (e.g. 2) different cover types c in a pixel

– pmin(c), pmax(c), and pdf of w(c)

– dp(c)/dt specified on a relative basis

• From fine resolution image estimate p(x)

– Estimate ensemble pdf of valid p|c(x),c(x),w(c)

• From coarse resolution pixel X estimate w(X) relying on linearity of p with scale

• Downscale p(X)

Ottawa Mixed Land Use Site

Pure Pixel Assessment

CASI 30m MERIS 300m

Constellation Wish List

• Estimate w: high resolution red-edge in darkspot

• Calibrate p-LAI : height from lidar, crown diameter/row spacing from high res

• OR – high resolution airborne red-edge lidar

• OR – in-situ constellation of red-edge cameras