Retrieval of vegetation biophysical Retrieval of vegetation biophysical parameters by inverting parameters by inverting hyperspectral hyperspectral , , multiangular multiangular CHRIS/PROBA Data from SPARC CHRIS/PROBA Data from SPARC 2003 2003 D’Urso D’Urso G., Dini L., Vuolo F., Alonso L. G., Dini L., Vuolo F., Alonso L.
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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
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
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b
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bbbbbb
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bb b bb
b
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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
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.
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 :
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.)