Validation of different methods of chlorophyll content ...22)_AGR... · Validation of different...

Validation of different methods of

chlorophyll content estimation by remote

sensing data from AGRISAR campaign

S. Gandía, J. Delegido, J. Moreno, G. Fernández

Department of Earth Physics and Thermodynamics Laboratory for Earth Observation

Faculty of Physics University of Valencia

AGRISAR & EAGLE Final Workshop ESA/ESTEC October, 15-16 2007

Definition and validation of methodologies to derive Level 2

products from Sentinel-2 data: leaf chlorophyll content and leaf

area index as focus products

Sensitivity analysis of the defined SENTINEL-2 bands to

different chlorophyll retrieval methods, validated against a

defined reference (full spectrum of CASI)

• Regions of interest (ROIs) of 3 pixels area (attending crop

homogeneity) have been defined around GPS coordinates of each

elementary sampling unit (ESU) with chlorophyll data

• Defined ROIs have been used with CASI images and SENTINEL-2

simulated images and mean spectra with standard deviation error have

been obtained

• Some chlorophyll indices have been calculated for both CASI and

SENTINEL-2 sensors and capability of SENTINEL-2 indices to

reproduce CASI indices has been tested

• Correlations between CASI and SENTINEL-2 chlorophyll indices and

ground truth chlorophyll and LAI data have been analyzed

++AHSAHS--INTAINTA

CASICASI--15001500

Simulation of Sentinel-2 products to evaluate their spectral bands

capabilities

SentinelSentinel--2 Simulation Image2 Simulation Image

airborne imaging spectrometers

SENTINELSENTINEL--2 2 Bands Bands

ConfigurationConfiguration

available spectral available spectral information frominformation from Exploring the possibilities

offered on retrieving vegetation properties

ChlorophyllChlorophyll

We have focused our analysis on

288 spectral bands288 spectral bands450 450 -- 1000 nm1000 nm

CASICASIAHSAHS--INTAINTA

43 spectral bands 43 spectral bands 1000 1000 -- 2500 nm2500 nm

++

The available spectral information from

SentinelSentinel--2 simulation2 simulation

1313 spectral spectral bandsbands443 443 -- 2190 2190 nmnm

Band Unit B1 B2 B3 B4 B5 B6 B7 B8 B8a B9 B10 B11 B12

1610 2190

18090

1375

20

940

20

λλ centrecentre nm 443 490 560 665 705 740 775 842 865

Width band nm 20 65 35 30 15 15 20 115 20

Atmospheric Correction BandsAtmospheric Correction Bands

230

140

460102

450

222

UV ESUs July-06

ZALF ESUs July-06

230

222

140

460 102

450

102SB

460SB

140

Rape

230

222Corn

Wheat

Barley450

23 ROI to obtain CASI and S2S

spectra

23 Chlorophyll ESUs at July 06

Spectra from CASI + S2S July 5 2006 CASI CASI

S2S S2S

Each region of interest (ROI) has an extension of three pixels centred on the GPS coordinates of the chlorophyll ESU

From the image taken on July, 5 2006 (CASI image date), several

chlorophyll indices (taken from the literature) have been calculated

with CASI and S2S bands for the chlorophyll ESUs

Correlations between CASI and S2S indices have been analyzed in

order to check if the defined SENTINEL-2 bands provide us the same

information as CASI in terms of chlorophyll retrievals

The CASI and S2S chlorophyll indices have been correlated with

chlorophyll content and with the product (LAI*Chl) to analyze the

capability of both sensors to estimate crop chlorophyll

Chlorophyll indices from other campaigns has been compared with

AGRISAR campaigns

From the numerous chlorophyll indices that appear in the literature, the

following have been chosen :

λ

λλ λ∫

2

1I = R( ) d

⇒ Integral Index:

Area intersected between vegetation

spectrum and soil spectrum is an

indicator of chlorophyll content of the

vegetation

Also, area under vegetation reflectance

curve is related to the amount of

chlorophyll in the leaves (if soil

spectrum can be considered constant)

⇒ Band Quotient Index (Gitelson & Merzlyak, 1997)

⇒ Modified Chlorophyll Absorption in Reflectance Index (MCARI)(Daughtry et al, 2000)

750

700

RGMI =R

B6 GMIS =2SB5

749.1

699.8

CAR

GMIS =R

I

( ) ( ) ⎛ ⎞⎡ ⎤ ⎜ ⎟⎣ ⎦

⎝ ⎠700

700 670 700 550670

RMCARI = R -R -0.2* R -R *R

( ) ( ) ⎛ ⎞⎡ ⎤ ⎜ ⎟⎣ ⎦ ⎝ ⎠

B5MCARI = B5-B4 -0.2* B5-B3 *B4

S2S

( ) ( )699.8 669.2 699.8 549.4669.2

⎛ ⎞⎡ ⎤ ⎜ ⎟⎣ ⎦

⎝ ⎠700RMCC ARI = R -R -0.2* R -R *

RASI

⇒ Transformed CARI (TCARI) (Haboudane et al, 2002)

( ) ( )⎡ ⎤⎛ ⎞⎢ ⎥⎜ ⎟

⎝ ⎠⎣ ⎦

700700 670 700 550

670

RTCARI =3* R -R -0.2* R -R *R

( ) ( )⎡ ⎤⎛ ⎞⎜ ⎟⎢ ⎥⎝ ⎠⎣ ⎦

B5TCARI =3* B5-B4 -0.2* B5-B3 *B4

S2S

( ) ( )⎡ ⎤⎛ ⎞⎢ ⎥⎜ ⎟⎢ ⎥⎝ ⎠⎣ ⎦

699.8699.8 669.2 699.8 549.4

669.2

RTCARI =3* R -R -0.2* R -R *

RCASI

⇒ MERIS Terrestrial Chlorophyll Index (Dash & Curran, 2005)

( )( )

753.75 708.75

708.75 681.25

R - RMTCI =

R -R( )( )

55

B6 - BS2S MTCI =

B

-B4( )( )

753.8 709.2

709.2 681

R - RMTCI =

R C

-RASI

⇒ Optimized Soil - Adjusted Vegetation Index (OSAVI) (Rondeaux et al., 1996):

⇒ Ratio TCARI/OSAVI (Haboudane et al, 2002)

( )( )

800 670

800 670

1.16 * R -ROSAVI =

R + R + 0.16

( )( )1.16 * B8-B4

OSAVI =B8 + B4 +

S2S 0.16

( )( )

800.8 669.2

800.8 669.2

1.16 * R -ROSAVI =

RCASI

+R + 0.16

SENTINEL-2 (600-705) nm Integral Calculation

Interpolation to obtain a 600 nm band between B3(560 nm) and B4(665 nm)

12 Chl ESU from ZALF team 11 Chl ESU from UV team

0

300

600

900

1200

0 300 600 900 1200

CASI

Inte

gral I

ndex

S2S Integral Index

y = m1 + m2 *xErrorValue

162m1 0.020.88m2

0.99r

0

2

4

6

8

10

0 2 4 6 8 10

CASI

GM

I

S2S GMI

y = m1 + m2 * xErrorValue0.4-1.2m1 0.142.05m2 0.95r

Integral Integral IndexIndex

GMIGMI


0.0

0.1

0.2

0.3

0.0 0.1 0.2 0.3

CASI

MCA

RI

S2S MCARI

y = m1 + m2 * xErrorValue0.0011-0.0003m1 0.0080.316m2

0.99r

0.0

0.1

0.2

0.3

0.0 0.1 0.2 0.3

CASI

TCA

RI

S2S TCARI

y = m1 + m2 * xErrorValue0.003-0.017m1 0.020.69m2 0.99rMCARIMCARI

TCARITCARI


0

2

4

6

8

10

0 2 4 6 8 10

CASI

MTC

I

S2S MTCI

y = m1 + m2 * xErrorValue0.09-1.06m1 0.031.7m2

0.998r

0.5

0.6

0.7

0.8

0.5 0.6 0.7 0.8

CASI

OSAVI

S2S OSAVI

ErrorValue0.012-0.032m1 0.0181.050m2

0.997r

y = m1 + m2*x

OSAVIOSAVI

MTCIMTCI


0

2

4

6

8

10

0

2

4

6

8

10

0 20 40 60 80 100

S2SCASI

S2S G

MI

CASI GM

I

Chlorophyll (μg cm-2)

y = m1 + m2 * xErrorValue0.52.7m1

0.0160.003m2 0.04r

y = m1 + m2 * xErrorValue

1.24.9m1 0.03-0.007m2

0.04r

0

500

1000

1500

0

500

1000

1500

0 20 40 60 80 100

S2SCASI

S2S

Inte

gral I

ndex

CASI Integral Index

Chlorophyll (μg cm-2)

y = m1 + m2 * xErrorValue200900m1

7-6m2 0.16r

y = m1 + m2 * xErrorValue200800m1

6-5m2 0.17r

Integral Integral IndexIndex

GMIGMI


0

0.1

0.2

0.3

0

0.1

0.2

0.3

0 20 40 60 80 100

S2SCASI

S2S

MCA

RI

CASI M

CARI

Chlorophyll (μg cm-2)y = m1 + m2 * x

ErrorValue0.070.30m1

0.002-0.005m2 0.47r


0.0007-0.0016m2 0.45r

0

0.1

0.2

0.3

0

0.1

0.2

0.3

0 20 40 60 80 100

S2SCASI

S2S

TCARI

CASI TCA

RI



0.0017-0.0030m2 0.35r


0.0012-0.0024m2 0.40r

TCARITCARI

MCARIMCARI


0

2

4

6

8

10

0

2

4

6

8

10

0 20 40 60 80 100

S2SCASI

S2S

MTCI

CASI M

TCI


ErrorValue1.22.9m1

0.030.02m2 0.14r


1.93.5m1 0.060.04m2

0.18r

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

S2SCASI

S2S

OSA

VI

CASI O

SAVI



0.0017-0.0023m2 0.29r


0.0017-0.0025m2 0.30r

OSAVIOSAVI

MTCIMTCI

0

20

40

60

0 1000 2000 3000 4000

SPARC

AGRISAR

Curve fit

Chor

ophy

ll (μ

g cm

-2)

Integral Index


1.051.3m1 0.0007-0.0146m2

0.78r

0

10

20

30

40

50

60

0 0.1 0.2 0.3 0.4 0.5

Chl - ZALFChl-Haboudane

Chloro

phyll (μg

cm

-2)

CASI (TCARI/OSAVI)

y = m1+m2*ln(x)ErrorValue3 e-7-18.363m1 2 e-7-30.194m2

1r

0

50

100

150

200

250

0

50

100

150

200

250

0 400 800 1200

Chlor*LAI (1)Chlor*LAI (2)

Chl*LA

I (1

) Chl*LAI (2)

CASI Integral


60140m1 0.09-0.07m2

0.24r


60140m1 0.09-0.08m2 0.25r

0

50

100

150

200

250

0

50

100

150

200

250

0 400 800 1200


Chl*LA

I (1

) Chl*LAI (2)

S2S Integral


60150m1 0.08-0.08m2

0.30r

y = m1 + m2 * M0ErrorValue

60150m1 0.08-0.08m2 0.32r

LAI and Chl data: 12 ESUs (ZALF)LAI(1): early in the morning LAI(2): afternoon

0

50

100

150

200

250

0

50

100

150

200

250

0 2 4 6 8 10


Chl*LA

I (1

) Chl*LAI (2)

S2S GMI

y = m1 + m2 *xErrorValue11060m1 4010m2 0.09r

y = m1 + m2 * xErrorValue11030m1 4020m2 0.16r

0

50

100

150

200

250

0

50

100

150

200

250

0 2 4 6 8 10


Chl*LA

I (1

) Chl*LAI (2)

CASI GMI


80150m1 17-11m2 0.21r


90120m1 18-7m2 0.12r


0

50

100

150

200

250

0

50

100

150

200

250

0.0 0.1 0.2 0.3


Chl*LA

I (1

) Chl*LAI (2)

S2S MCARI


30130m1 160-300m2 0.51r


30130m1 160-300m2 0.49r

0

50

100

150

200

250

0

50

100

150

200

250

0.0 0.1 0.2 0.3


Chl*LA

I (1

) Chl*LAI (2)

CASI MCARI


30130m1 500-900m2

0.46r


30130m1 500-900m2 0.45r



0

50

100

150

200

250

0

50

100

150

200

250

0.0 0.1 0.2 0.3


Chl*LA

I (1

) Chl*LAI (2)

S2S TCARIy = m1 + m2 * x

ErrorValue40160m1

200-400m2 0.53r


40160m1 200-400m2 0.52r

0

50

100

150

200

250

0

50

100

150

200

250

0.0 0.1 0.2 0.3


Chl*LA

I (1

) Chl*LAI (2)

CASI TCARIy = m1 + m2 * x

ErrorValue30140m1

300-600m2 0.49r


30140m1 300-600m2

0.49r

0

50

100

150

200

250

0

50

100

150

200

250

0 2 4 6 8 10

Chl*LAI (1)Chl*LAI (2)

Chl*LA

I (1

) Chl*LAI (2)

CASI MTCI


4010m1 817m2 0.55 r


402m1 819m2 0.59r

0

50

100

150

200

250

0

50

100

150

200

250

0 2 4 6 8 10


Chl*LA

I (1

) Chl*LAI (2)

S2S MTCI


50-10m1 1430m2 0.56r


50-20m1 1433m2 0.60r



0

50

100

150

200

250

0

50

100

150

200

250

0.5 0.6 0.7 0.8 0.9 1.0


Chl*LA

I (1

) Chl*LAI (2)

S2S OSAVIy = m1 + m2 * x

ErrorValue160560m1 200-700m2 0.67r

y = m1 + m2 * xErrorValue190500m1 300-500m2 0.56r

0

50

100

150

200

250

0

50

100

150

200

250

0.5 0.6 0.7 0.8 0.9 1.0


Chl*LA

I (1

) Chl*LAI (2)

CASI OSAVIy = m1 + m2 * x

ErrorValue150530m1 200-600m2 0.67r

y = m1 + m2 * xErrorValue170470m1 300-600m2 0.57r

• Concerning to SENTINEL-2 simulations, the use of CASI images (VNIR)

together with AHS images gives a more realistic simulation than the use of

AHS data only (particularly for channel 1-9)

• Concerning SENTINEL-2 and CASI chlorophyll indices, all them shows very

good correlations between both sensors (r > 0.95)

• When correlations between chlorophyll indices and ground truth chlorophyll

data are made, for SENTINEL-2 and CASI images, bad correlation

coefficients are found (r < 0.6)

When AGRISAR data are considered with other ESA campaign data (like

SPARC) it can be seen that AGRISAR data inserts well in the set but with a

minor range of variation due to the minor number of different crops

considered in the AGRISAR campaign and a better correlation coefficient can

be found with a large dynamical range of values

• Concerning the correlation, for SENTINEL-2 and CASI images, between

chlorophyll indices and LAI*Chl, better correlation coefficients than in the

case of the correlations with chlorophyll data only are found (r < 0.7) what

indicates the necessity of obtaining LAI and chlorophyll measurements

together

The small number of crops and their small range of variability seems to be

responsible for the low correlation coefficients found when considering

chlorophyll indices and ground truth chlorophyll data

Validation of different methods of chlorophyll content ...22)_AGR... · Validation of different...

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