BIOHYPE: What can we learn from hyperspectral solar ...€¦ · • Chl fluorescence, once emitted...

BIOHYPE: What can we learn

from hyperspectral solar-induced

chlorophyll fluorescence of urban

tree leaves?

Shari Van Wittenberghe1, Luis Alonso3, Jochem Verrelst3, Inge Hermans2,

Frank Veroustraete1, Roland Valcke2, Jose Moreno3, Roeland Samson1

BEO day, 20th of November 2014, Lier

1Laboratory of Environmental and Urban Ecology, Dep. Bioscience Engineering,

University of Antwerp 2Labboratory of Molecular and Physical Plant Physiology, University of Hasselt

3Image Processing Laboratory, Dep. Earth Physics and Thermodynamics,

University of Valencia

[email protected]

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Introduction on chlorophyll fluorescence

Methodology: leaf vs. Remote sensing

Results 1: Drivers of Chl fluorescence

Results 2: Parameter retrieval from

hyperspectral reflectance, absorbance &

fluorescence dataset

Results 3: Chlorophyll content map of urban

vegetation in Valencia

General outcome

2/20

BIOHYPE: What can we learn from hyperspectral solar-

induced chlorophyll fluorescence of urban tree leaves?

Wavelength (nm)

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

Incom

ing r

adia

nce (

W m

-2 s

r-1 n

m-1)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Incoming solar radiance

Wavelength (nm)

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

Incom

ing r

adia

nce (

W m

-2 s

r-1 n

m-1)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Incoming solar radiance

Absorbed leaf radiance

Visible

UV Near Infrared Shortwave Infrared

Energy dissipation mechanisms

(Rosema et al., 1998, RSE)

General outcome Introduction Methodology R1:Drivers of Chl F R2: Parameter retrieval R3: Chl map

3/20

General outcome Introduction Methodology R1:Drivers of Chl F R2: Parameter retrieval

Excitation De-excitation

Chl

S1

S2

1Chl*

heat loss

Fluorescence

S0

Ground

state

Red photon Blue photon

Chl

Wavelength (nm)

400 500 600 700 800 900 1000 1100

Absorb

ed r

adia

nce (

W m

-2 s

r-1 n

m-1

)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35VISIBLE NEAR-INFRAREDUV

Absorbed light

Wavelength (nm)

400 500 600 700 800 900 1000 1100

Ab

so

rbe

d r

ad

ian

ce

(W

m-2

sr-1

nm

-1)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Flu

ore

scence r

adia

nce (

W m

-2 s

r-1

nm

-1)-2

sr-1

nm

-1)

0.000

0.002

0.004

0.006

0.008


Absorbed light

Emitted fluorescence

Some vegetation physiology…

R3: Chl map

4/20

Chl F: 650-850 nm

Wavelength (nm)

400 500 600 700 800 900 1000 1100

Appare

nt

reflecta

nce (

-)

0.0

0.1

0.2

0.3

0.4

0.5

Reflectance

+

Chl fluorescence

= Apparent reflectance

• Chl fluorescence is a weak

signal compared to leaf

reflectance

Wavelength (nm)

400 500 600 700 800 900

Ra

dia

nce

(W

m-2

sr-1

nm

-1)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Apparent reflected radiance• Cut off filter at 650 nm

Wavelength (nm)

400 500 600 700 800 900

Ra

dia

nce

(W

m-2

sr-1

nm

-1)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Apparent reflected radiance

Chl fluorescence

• FluoWat leaf clip (by L.

Alonso) coupled with ASD

spectroradiometer

• Pointed towards the sun

(clear sky is needed)

• Upward and downward Chl

fluorescence emission, together

with reflectance and

transmittance

Spectroradiometer

• Why solar-induced?


5/20

• Remote vegetation monitoring through

reflectance since „70s

Source: http://ipl.uv.es/flex-parcs/

GOME satellite (Joiner et al. 2013)

• Chl fluorescence: more physiological signal

allowing direct diagnose of actual vegetation status

(instead of “greenness”)

• FLEX (FLuorescence Explorer) mission and

CarbonSat now contesting to be ESA‟s 8th Earth

Explorer, both address key climate and

environmental change issues

(Meroni et al. 2009) Fraunhofer Line Depth

Hyplant sensor – fields with different

fluorescence intensity


6/20

Fluorescence intensity

Absorbed PAR

• Chl fluorescence intensity ~ solar

intensity

• By pointing towards the sun: constant

sun angle irradiance more constant

• Not interested in Chl fluorescence

intensity, but in Chl fluorescence yield

• Fluorescence Yield (FY) =

• Upward ↑FY, Downward ↓FY

• Total FY= ↑FY + ↓FY

• FY at peak wavelength: 687 nm, 741 nm

• Peak ratios: eg ↑ FY(687)/ ↑ FY(741)

• Bidirectional F ratio: eg ↓FY(687)/↑ FY(687)

Wavelength (nm)

650 700 750 800 850

FY

(-)

0

1e-5

2e-5

3e-5

4e-5

↑ FY

↓FY


7/20

(Miller et al. 2005, ESA contract report)

Results 1: Drivers of Chl fluorescence

BIOHYPE Dataset

• 4 tree species

• 40 trees, 10 locations

• ≠ sampling heights

• ≠ traffic exposure

• > 300 leaf spectra


8/20

1. Chlorophyll content

Wavelength (nm)

400 500 600 700 800 900 1000 1100

Ab

so

rbe

d r

ad

ian

ce

(W

m-2

sr-1

nm

-1)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Flu

ore

sce

nce

ra

dia

nce

(W

m-2

sr-

1 n

m-1

)-2 s

r-1 n

m-1

)

0.000

0.002

0.004

0.006

0.008


Absorbed light

Emitted fluorescence

• Red peak overlaps Chl absorption

range: re-absorption effect

• Red/Far-red peak ratio is sensitive

to Chl: re-absorption in the red, not

in the far-red

• Re-absorption effect stronger for ↓FY

Wavelength (nm)

660 680 700 720 740 760 780

F/

F

0.0

0.2

0.4

0.6

0.8

1.0

re-absorption effect

C. australis

M. alba

P. x acerifolia

P. canariensis

Introduction Methodology R1:Drivers of Chl F R2: Parameter retrieval R3: Chl map General outcome

9/20

0,0

0,5

1,0

1,5

2,0

2,5

3,0

↑ F

Y (

x 1

0-5

)

0,0

0,5

1,0

1,5

2,0

2,5

3,0

↑ F

Y (

x 1

0-5

)

0,0

0,5

1,0

1,5

2,0

650 700 750 800 850

↓ F

Y (

x 1

0-5

)

Wavelength (nm)

0,0

0,5

1,0

1,5

2,0

650 700 750 800 850

↓ F

Y (

x 1

0-5

)

Wavelength (nm)

P. x. acerifolia P. canariensis **

*

**

** **

**

*

2. Air pollution

High traffic exposure Low traffic exposure

• Red/Far-red peak

ratio increases for

high exposure

• No significant

differences in Chl

between classes

• Chl F more

sensitive than

“static” Chl

concentration

Re-absorption ~

Chl efficiency to re-

absorb light

(Van Wittenberghe et al., 2013, Environmental Pollution)


10/20

3. PS Stoichiometry

P. canariensis

Chl a/b

1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4

FY

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

y=0.0006*x - 0.0007 r²=0.8418 (p<0.001)

TS-B

TS-M

TS-T

P. canariensis

Chl a/b

1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4

FY

(687)/

FY

(741)

0.4

0.5

0.6

0.7

0.8

0.9

1.0

(p<0.01)

TS-B

TS-M

TS-T

A

B

• Chl a/b is indicator of the size of the

LHCII…

• Chl F driver: ↓ Light ↑LHCII ↓ Chl a/b

• LHCII= light harvesting complex associated

with PSII

• LHCII collects solar radiation, and transfers

the excitation energy towards the PSII reaction

centers

• PSII contributes most of the total fluorescence

emission

• No “shadow” leaves, leaf deposit is

suggested as indirect factor

(Van Wittenberghe et al., 2014a, Science of the Total Environment)


11/20

C. australis M. alba

P. canariensis P. x acerifolia

1

2

3

4

5

5

4. Leaf structure (scattering behavior)

(1) Upper epidermis, (2) Palisade parenchyma, (3) Spongy parenchyma,

(4) Lower epidermis, (5) Mesophyll

equifacial

bifacial bifacial

bifacial


12/20

Multiple upward

scattering →reflectance

Multiple downward scattering

→ transmittance


The same happens with

fluorescence…

Wavelength (nm)

650 700 750 800

Flu

ore

scence Y

ield

(-)

0

1e-5

2e-5

3e-5

4e-5

Flu

ore

scence Y

ield

(-)

0

1e-5

2e-5

3e-5

4e-5

5e-5

Total

Upward

Downward

Wavelength (nm)

650 700 750 800

C. australis M. alba

P. x acerifoliaP. canariensis

• Each species characteristic F

shape and FY

• Same FY ≠ same F shape

• ↓FY up to 40% of FYtot


13/20


equifacial bifacial

• Influence of leaf scattering effects onto the bidirectional partitioning of Chl fluorescence

• Chl fluorescence, once emitted from Chl a, interacts as light of the same wavelength

would do

• However, still variation due to different light origin:

Transmittance ↓F (Van Wittenberghe et al., Accepted, Remote Sensing of the Environment)


14/20

There is a lot information captured at leaf level which will help to understand Chl F

behaviour at canopy level…

Chl F will not only decrease with

depth into the canopy (↓illumination),

the spectral composition will also

change

Multi absorption and scattering effects

Ground measurements will play an

important role in separating physiological

from canopy structure effects

A correct estimation of canopy

structural parameters (leaf area index,

leaf angle distribution) will be needed

4. Lessons for RS


15/20 (Van Wittenberghe et al., Accepted, Remote Sensing of the Environment)

P. x acerifolia

Wavelength (nm)

500 1000 1500 2000

Reflecta

nce (

-)

0.0

0.2

0.4

0.6P. canariensis

Wavelength (nm)

500 1000 1500 2000

Reflecta

nce (

-)

0.0

0.2

0.4

0.6

M. alba

Reflecta

nce (

-)

0.0

0.2

0.4

0.6C. australis

Reflecta

nce (

-)

0.0

0.2

0.4

0.6

• Full range (350-2500 nm)

• All data (4species) put together

for leaf parameter retrieval of

• Specific leaf area

• Leaf water content

• Chl content

• Nitrogen content

• Better use of the hyperspectral

data cube

• Gaussian Processes regression:

• Nonlineair, non-parametric

machine learning algorithm

• Confidence interval

• Band ranking

(Van Wittenberghe et al., 2014b, Journal of Photochemistry and Photobiology B :Biology)

• ARTMO toolbox

• ipl.uv.es/artmo/

• MLRA toolbox

Results 2: Parameter retrieval from hyperspectral reflectance, absorbance &



16/20

http://ipl.uv.es/artmo/



Measured SLA (cm2 g

-1)

40 60 80 100 120 140 160 180

Estim

ate

d S

LA

(cm

2 g

-1)

40

60

80

100

120

140

160

180

RMSE=8.5 cm2

g-1

NRMSE=7.2%

1:1

Wavelength (nm)

500 700 900 1100 1300 1500 1700 1900 2100 2300

Fre

qu

en

cy

0

5

10

15

20

Absorb

ance (

-)

0.0

0.4

0.8

710

470

22501490

Fre

qu

en

cy

0

5

10

15

20

Reflecta

nce (

-)

0.0

0.2

0.4

0.6510

910

Specific Leaf Area1310

1490

1730

2170

1310 1730

2190

Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 Rank 6 Rank 7 Rank 8

• Parameter Specific leaf area=fresh leaf area/ dry weight

• Range: 35.1-160.0 cm² g-1

• Spectral input: every 20 nm of full range 86 hyperspectral bands

• 70% training (179), 30% testing (76) data set

• 20 model runs Band selection

Measured SLA (cm2 g

-1)

40 60 80 100 120 140 160 180

Estim

ate

d S

LA

(cm

2 g

-1)

40

60

80

100

120

140

160

180

RMSE=7.3 cm2 g

-1

NRMSE=6.0%

1:1

• Several distinct wavelengths are chosen over the full range

• Related to leaf productional or structural biochemicals

Cellulose,

lignin

Protein

Starch

Protein Pigment

Cellulose,

lignin

Results 2: Parameter retrieval from hyperspectral reflectance, absorbance &


Model performance

(testing dataset)


17/20

• Parameter: Chl content

• Range: 15.9-189.2 µg cm-²

• Estimation of high Chl content rather difficult

Band selection Model performance

• Red edge most important band

• Absorption maxima (e.g. pigment) avoided: deeper light penetration

Starch

• Other bands related to leaf productional or structural biochemicals

Fre

qu

en

cy

0

5

10

15

20

Re

fle

cta

nce

(-)

0.0

0.2

0.4

0.6

410

510 590 710

950

Chlorophyll a+b

500 700 900 1100 1300 1500 1700 1900 2100 2300

Fre

qu

en

cy

0

5

10

15

20

Ab

so

rba

nce

(-)

0.0

0.4

0.8

1130

710950

590510

2250

1650,1670

(1430)

(1730)

Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 Rank 6 Rank 7 Rank 8

Measured Chl a+b (µg cm-2)

0 50 100 150 200

Estim

ate

d C

hl a+

b (

µg

cm

-2)

0

50

100

150

200

RMSE=14.3 µg cm-2

NRMSE=9.2%

1:1

Measured Chl a+b (µg cm-2)

0 50 100 150 200

Estim

ate

d C

hl a+

b (

µg

cm

-2)

0

50

100

150

200

RMSE=14.4 µg cm-2

NRMSE=9.1%

1:1Pigment Red edge

Cellulose

Cellulose,

lignin

Water

Protein

Starch

Results 2: Parameter retrieval from hyperspectral reflectance, absorbance & fluorescence dataset


18/20

Using several spectral features, both directly and indirectly linked to a parameter

through covariation, helps avoiding saturation effects

• NAOC (Normalized Area Over reflectance Curve)

• Less saturation compared to common VI:

Results 3: Chlorophyll content map of urban vegetation in Valencia

(Delegido et al., 2014, Ecological Indicators)

• Relation with traffic intensity/habitat quality:


• CASI, experimental configuration • 144 spectral bands using binning outside areas of interest

• FWHM: 2.4 nm at O2 absorptions and red-edge (630-802 nm) and PRI

bands

• Pixel size Along track: 1.6 m; Across track: 1.0 m

a =643nm, b=795nm

19/20

General outcome of the project

• 7 peer reviewed publications

• Largest/most complete solar-induced leaf

fluorescence dataset published

• Dataset could be further used for modelling

studies

• Results picked up and used by FLEX-team

• BIOHYPE Special Session organized at

• 2 PhDs completed with complete/partial

BIOHYPE support, 1 still running


Thank you for you attention!

20/20

BIOHYPE: What can we learn from hyperspectral solar ...€¦ · • Chl fluorescence, once emitted...

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