retrieval of vegetation biophysical parameters by

Retrieval of vegetation biophysical Retrieval of vegetation biophysical parameters by inverting parameters by inverting

hyperspectralhyperspectral, , multiangularmultiangularCHRIS/PROBA Data from SPARC CHRIS/PROBA Data from SPARC

20032003

D'UrsoD'Urso G., Dini L., Vuolo F., Alonso L.G., Dini L., Vuolo F., Alonso L.

ITAP, Albacete

Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, Madrid

Universitá degli Studi di Napoli �Federico II�, Italy

University of Thessaly, Greece

INRA-CSE, Avignon

Laboratoire du Télédétection et SIRS, Tunisia

Meteo-France

European Spatial Agency (ESA)

University of Valencia - Remote Sensing Unit

University of Valencia - Global Change Unit

University of Valencia - Solar Radiation Unit

University of Valencia � GPDS

University of Castilla-La Mancha

Institute of Regional Development (IDR), Albacete

SPARC SPARC campaigncampaign((BarraxBarrax, 12, 12--14 14 JulyJuly 2003)2003)

AimAim ::

Assessment of retrieval accuracy by using :- RT models vs. empirical approaches (i.e. veget. Indexes)- multi-angular and/or super spectral info

Retrieval of canopy parameters (in particular LAI) from E.O. data for :- calculation of crop transpiration and soil evaporation (P-M approach)- soil water balance simulations (input forcing)

FIELD DATA

LAI measurements

113 Elementary Sampling Units

(24 data samples each ESU)

b

bb

b bb

bb b

b bbb

bbb b

bbbbbbbb

bb bbb

b

bb

bb bb

bbb

bb

bb

bbbbbb

bbbbb

b

bbbbbbbbbbb

bbbbbb

bb

b

bbb

bbb

bbbbbb

bbbbb

bbbbbb

b

bb b bb

b

bbb

bbb

LAI measurements

7 types of crop:

alfalfacornsugarbeetonionsgarlicpotatopapaver

0%

2%

4%

6%

8%

10%

12%

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7 3

3.3

3.6

3.9

4.2

4.5

4.8

5.1

5.4

5.7 6

6.3

LAI

avg = 3.07; std = 1.45

LAI = 1.32

LAI = 2.49

LAI = 3.72

Alfalfa, LAI = 3.72

Sugarbeet, LAI = 3.78

Corn, LAI = 3.84

Chlorophyll MeasurementsGood correlation between laboratory and field measurements for different crops

i.e. 4000+ valid chlorophyll measurements

0.010

0.020

0.030

0.040

0.050

0.060

0.070

1 10 100 1000

Chlorophyll Units

2 )

Clor.A1 (mg/cm2)Clor.C1 (mg/cm2)Clor.B1 (mg/cm2)Clor.W1 (mg/cm2)Clor.G1 (mg/cm2)Clor.ON1 (mg/cm2)Clor.P1 (mg/cm2)

Chl

orop

hyll

(mg/

cm2 )

S6S6

S5S5

S3S3

S0S0

WW

AA

Sampling Points for Radiometric Calibration

Sampling Points for Radiometric Calibration

targets: soil, vegetation

radiometers inter-comparison at Las Tiesas14 july

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

300 400 500 600 700 800 900 1000 1100

wavelength (nm)

refle

ctan

ce

LAI,LIDF,HOT,Esky

ModelsModels parametersparametersPROSPECT requires:PROSPECT requires:!! Leaf Leaf mesophyllmesophyll structure, Nstructure, N!! Chlorophyll Chlorophyll a+ba+b content, Cab content, Cab (mg cm(mg cm--22))!! Equivalent water thickness, Equivalent water thickness, CwCw (gcm(gcm--2)2)!! Dry matter content, Cm Dry matter content, Cm (gcm(gcm--2).2).

SailHSailH requires:requires:!! Leaf Area Index: LAILeaf Area Index: LAI!! Leaf inclination distribution function: Leaf inclination distribution function:

LIDF LIDF !! Leaf Leaf relectancerelectance and and trasmittancetrasmittance

(PROSPECT)(PROSPECT)!! Soil spectral reflectance, which is Soil spectral reflectance, which is

assumed to be Lambertianassumed to be Lambertian!! Solar zenith (Solar zenith (qqss) and azimuth angle () and azimuth angle (YYss) ) !! View zenith (View zenith (qqvv) and azimuth angle () and azimuth angle (YYvv))!! Fraction of incident diffuse skylight Fraction of incident diffuse skylight

expressed in terms of visibility, expressed in terms of visibility, EskyEsky!! KuuskKuusk hot spot size parameters, shot spot size parameters, s

FORWARD SIMULATION

Alfalfa measured groundreflectance (ASD ASD FieldSpecFieldSpec) and PROSPECT/SAILH simulated reflectance byusing different background measured spectra.

Data from SPARC 2003

Soil reflectance: ground measurement

FIELD MEASUREMENTSFIELD MEASUREMENTS

0

0,1

0,2

0,3

0,4

0,5

0,6

300 400 500 600 700 800 900 1000 1100

wavelength (nm)

refle

ctan

ce

soil (5-1.001)soil (5-2.001)soil (5-3.001)soil (5-4.001)soil (5-5.001)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

300 400 500 600 700 800 900 1000 1100

wavelength (nm)

refle

ctan

cesoil (6.001)

soil (6-2.001)

soil (6-4.001)

soil (6-5.001)

Best fit soil

RRMSE=0.0207

Soil Analsysis for CHRIS/PROBA reflectance simulation on Barrax site.

CHRIS max soilreflectance

CHRIS min soilreflectance

Ground measurement

CHRIS mean soilreflectance

1 Chris/proba bands

Ref

lect

ance

0.7

0.6

0.5

0.4

0.2

0.3

0.1

0101 20 30 5040 60

+55: VZA= 55.99°; VAA= 26.11°+36: VZA= 38.78°; VAA= 37.98º0: VZA= 19.40°; VAA= 102.40º-36: VZA= 39.15°; VAA= 165.44º-55: VZA= 56.24°; VAA= 177.06º

SUN ZENITH ANGLE= 22.4°SUN AZIMUTH ANGLE= 134.7°

[0º]

+55: VZA= 57.29°; +36: VZA= 42.44°; VAA= 339.44º0: VZA= 27.4°; VAA= 285.27º-36: VZA= 42.53°; VAA= 231.22º-55: VZA= 57.4°; VAA= 216.91º

SUN ZENITH ANGLE= = 19.8°SUN AZIMUTH ANGLE= 148.3°

12th of July Acquisition

14th of July Acquisition

+36° 0°

12/07/2003

+36° 0°

14/07/2003

Alfalfa: Forward

+55° 0°

Potatoes: Forward

+55° 0°

12/07/2003

14/07/2003

MODELINVERSION

Inversion algorithm - 1PEST-ASP using Gauss-Marquardt-Levenberg estimation techniquePEST runs the PROSAILH model, compares the model results with the target values (observed reflectance values), adjustsselected parameters using optimisation algorithm and runs the model as many times as is necessary in order to determine the optimal set of adjustable parameters

Inversion algorithm - 2

Parametersestimate

( LAI )

• LUT (look-up table) using RRMSE (relative mean square error)

+55+36 0 -36 -55

+55+36 0 -36 -55

[ ]

[ ]∑∑

∑∑

= =

= =

−= 5

1

62

1

2

5

1

62

1

2

),(

j imeas

j iestmeas

ij

(j,i)ρ (j,i)ρRRMSE

ρ

(Privette, 1994)

PEST-ASP theory

( )( ) ( )( )0000 bbJccQbbJcc t −−−−−−=Φ• Objective function :

Where:

• b0 : parameters vector to be upgraded

• b : parameter vector upgraded

• c0 = PROSAILH ( b0 ) : model calculated observations vector

• c : experimental observation vector

• J : Jacobian matrix of PROSAILH

• Q : Observation weigths matrix

( ) ( )01

0 ccQJIQJJbb tt −+=− −α• Algorithm by which the system parameter vector is estimated :

Marquardt-LevenbergWhere:

I : Identity matrix

α: Marquardt parameter

Parameters rangePROSPECT N=[1 � 3 ]Cab=[10 � 110]Cw=0.022Cm=[0.001 � 0.02]SAILHLAI=[0.3 � 8]HOT=[0.0001 � 1]LIDFS=[1 2 3 4 5 6 7 8 9 10 11 12 13]Esky=0.13

Initial parameters vector estimatePROSPECTN=1Cab=10Cw=0.022Cm=0.001SAILHLAI=0.4HOT=0.05LIDFS=1Esky=0.13

PEST-ASP settings

Contours of equalΦΦΦΦ

Initial parameterestimation

Parameter # 1

Parameter # 2

PEST-ASP results ILAI PEST ASP

0,00

1,00

2,00

3,00

4,00

5,00

6,00

7,00

8,00

9,00

0,00 1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00 9,00

Measured LAI

Estim

ated

LA

I

LAITheor.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00

Measured LAI

Estim

ated

LA

I AlfalfaPotatoesCornSugarbeetOnionTheor.

Potatoes

Alfalfa

RRMSE = 0.40

PEST-ASP results II

Alfalfa1 < N < 2

LIDFS Plagiophile

Meas. Estim.

LAI:3.24 3.30

Cab: 63 27(25-35)

+55 +36 0-36 -55

A priori A priori knowledgeknowledge

PEST-ASP results II

Potatoes1 < N < 1.6

HOT < 0.0027

LIDFS Uniform

Meas. Estim.

LAI: 5.20 5.10

Cab: 35 14(10-14)

+55 +36 0-36 -55

A priori A priori knowledgeknowledge

PEST-ASP results IIA priori A priori knowledgeknowledge

SugarbeetN = 1.5

HOT < 0.6Meas. Estim.

LAI: 4.08 4.05

+55 +36 0-36 -55

PEST-ASP results IIA priori A priori knowledgeknowledge

0

1

2

3

4

5

6

7

8

0,00 1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00

Measured LAI

Estim

ated

LA

I

- 15%

+15%

RRMSE = 0.11

Potatoes

Sug. Beet

Alfalfa

585000

Simulatedspectral profiles

Points in the parameter space are uniformely taken:

PROSPECT N=[1.5 1.7 … 2.5]Cab=[10 15 … 70]Cw=0.011Cm=[0.002 0.004 … 0.02]SAILHLAI=[1 1.2 … 6.8]HOT=[0.05 0.15 … 0.5]LIDFS=[Planophile; Plagiophile; Extremophile; Erectophile; Spherical]Esky=0.13

Geometry of illumination and observation was fixedby time of acquisition and Chris/PROBA orbit

CHRIS/PROBA 12/07/2003 Barrax

Image extracted spectral profile

Estimated parameters (LAI)

RRMSE minimum

Look-up table theory and settings

0

1

2

3

4

5

6

7

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

Measured LAI

Estim

ated

LA

I

LAITheor.

Look-up table results

0

1

2

3

4

5

6

7

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

Measured LAI

Estim

ated

LA

I AlfalfaPotatoesOnionCornSugar beetTheor

Potatoes

Alfalfa

RRMSE = 0.55

+ 55 + 36 0- 36 - 55

AlfalfaLAI Estim. 1.8

LAI Meas. 1.9

Look-up table Alfalfa

Look-up table Potatoes

+55+36 0 -36 -55

PotatoesLAI Estim. 2.6

LAI Meas. 5.6

Conclusion

• LAI WAS ESTIMATED WITH AN ACCURACY OF AROUND 15% FOR CROPS CLOSE TO THE TURBID MEDIUM HYPOTHESIS

• PEST IS A GOOD TOOL TO ESTIMATE LAI OF SOME CROPS (ALFALFA AND POTATOES) WITH LITTLE A PRIORI KNOWLEDGE. PROBABILY FOR DIFFERENT CROPS (CORN, ONION) IT IS NEEDED TO ADD MORE A PRIORI KNOWLEDGE TO AVOID “ILL-POSED INVERSION PROBLEM”

• LUT PROBABILY NEED FINEST AND BETTER PARAMETERS SPACE SAMPLING TO BETTER ESTIMATE BIOPHYSICAL PARAMETERS

Future steps

• WE HAVE TO BETTER DEFINE A PRIORI KNOWLEDGE FOR CORN, ONION, WHEAT

• WE NEED TO BETTER UNDERSTAND THE INFLUENCE OF THE SOIL IN THE RADIOMETRIC SIGNAL FOR MULTI-ANGULAR AND HYPERSPECTRAL SATELLITE DATA ON VEGETATION WITH DIFFERENT LAI.

• WE HAVE TO TEST DIFFERENT INVERSION ALGORITHMS (NEURAL NETWORKS, GENETHIC ALG.)