Transcript S2-Colombo

Estimation of the Net Ecosystem Exchange in
Agro-Forestry Ecosystems by Assimilation of
Satellite-Derived Information in a Process Model
R. Colombo(1), M. Meroni(1) , M. Migliavacca(1), L. Busetto(1-2),
C. Panigada(1), G. Seufert(3)
(1) Lab.
Telerilevamento Dinamiche Ambientali DISAT-UNIMIB, Milano, Italy
[email protected]
www.disat.unimib.it/telerilevamento
(2) CNR
(3)Joint
- IIA, Roma, Italia
Research Centre, IES - Ispra (VA), Italia
Workshop on Climatic Analysis and Mapping for Agriculture
14-17 June 2005, Bologna, Italy
Main objectives
Mapping carbon exchange in fast growing forests (poplar plantation)
•
ground estimation of RS parameters related to carbon;
•
prediction of canopy parameters at different RS scales;
•
recalibration strategy for modelling carbon exchange at regional level.
Workshop on Climatic Analysis and Mapping for Agriculture
14-17 June 2005, Bologna, Italy
Index of presentation
1.
Experimental site overview;
2.
NEE vs biophysical parameters;
3.
Ground measurements techniques;
4.
Canopy characteristics from remote sensing data;
5.
Carbon mapping by coupling RT and BIOME-BGC models
Workshop on Climatic Analysis and Mapping for Agriculture
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Experimental site
• Type: I-214 clone;
• Age: 14 years;
• Tree density: 278 trees/ha;
• Height: 27 m;
• Area: 10.5 m2 ha-1
M ila n o
Natural
forest
r
ive
or
cin
Ti
After logging
regenerating forest
S tu d y S ite
T ic in o R iv e r P a rk
Poplar plantation
Flux tower
Rice-paddies
• Exchange rates of sensible heat, water vapour and carbon
dioxide are measured by the eddy covariance technique,
since march 2002.
• The site belongs to the CARBOEUROPE-IP network
(managed by JRC-IES)
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LTDA
Canopy characteristics and NEE at the experimental site 1/2
Temporal trend of
Fipar and NEE
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Canopy characteristics and NEE at the experimental site 2/2
2,50
1,50
2
2
LAI (m /m )
2,00
Temporal trend of
1,00
LAI
0,50
0,00
30
60
90
120
150
180
Day of Year
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and NEE
Data collection
Field data
Remote Sensing
Lab. data

Pigment, water, dry matter
content and N (PROSPECT)
Hyperspectral airborne data
LAI, fIPAR, SPAD, Fc,
fluorescence
Field radiometric
measurements
0.6
0.5
R
0.4
0.3
0.2
0.1
25 0 0
24 00
23 00
2 20 0
2 10 0
2 00 0
1 90 0
18 0 0
17 00
1 6 00
1 5 00
1 40 0
1 30 0
1 20 0
90 0
11 0 0
800
10 00
700
6 00
5 00
40 0
0.0
w l (nm )
Spectroscopy
and
Leaf samples
chemical
extraction
MODIS data
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LAI measurements
Field: canopy structural characterisation, LAI, fIPAR
LAI - 2000
eSAIo
ePAIo/u+o
Digital Camera
eLAIg
Belowu+o - Belowo
LAIu
A:Belowo with B:Belowo+u
Colombo et al., 2003
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eLAIo
fIPAR measurements
Field: canopy structural characterisation, LAI, fIPAR,
fAPAR vs fIPAR
IA [W/m2] IA
RA
IB
RB
(IA - RA) - (IB - RB) / IA = Fapar
IA- IB / IA = Fipar
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Relating canopy properties to RS observations
Physically based models
Reflected
(6-12% of PAR)
Field data VS hyperspectral data
direct use
0 .9
0 .8
0 .7
0 .6
NDVI
Canopy level (e.g. SAILH)
PAR
incoming
0 .5
- LAI, Fapar, Fc, rcl, tcl -
Transmitted
(0-40% of PAR)
inversion
Semi-empirical models
Absorbed
(48-94%)
0 .4
0 .3
2
r = 0 ,8 3
0 .2
0 .1
Heat
(75-97%)
0
0 .0 0
0 .5 0
1 .0 0
1 .5 0
2 .0 0
2 .5 0
Fluorescence
(3-5%)
Photochemical
(0-20%)
3 .0 0
eLAIo
Leaf level (e.g. PROSPECT)
Regression model between NDVI and LAI
Colombo et al., 2004
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- Cab, Cw, Cm, N; rfl, tfl -
CR Model: direct use
SAILH
R canopy
Canopy
model
PROSPECT
Leaf
R and T
Leaf
model
Canopy structure
Acquisition geometry
Soil reflectance
Workshop on Climatic Analysis and Mapping for Agriculture
14-17 June 2005, Bologna, Italy
Chlorophyll
Water
Dry matter
N
35 g cm-2
0.02 cm
0.01 g cm-2
1.3
Laef
biochemistry
CR Model: direct use
SAILH
R canopy
Canopy
model
PROSPECT
Leaf
R and T
Leaf
model
Canopy structure
Acquisition geometry
Soil reflectance
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Chlorophyll
Water
Dry matter
N
35 g cm-2
0.02 cm
0.01 g cm-2
1.3
Laef
biochemistry
CR Model: direct use
SAILH
R canopy
Canopy
model
PROSPECT
Leaf
R and T
Leaf
model
Canopy structure
Acquisition geometry
Soil reflectance
LAI
MTA
hot spot
….
3 m2m-2
50 deg
0.05
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Chlorophyll
Water
Dry matter
N
35 g cm-2
0.02 cm
0.01 g cm-2
1.3
Laef
biochemistry
CR Model: direct use
SAILH
R canopy
Canopy
model
PROSPECT
Leaf
R and T
Leaf
model
Canopy structure
Acquisition geometry
Soil reflectance
LAI
MTA
hot spot
….
3 m2m-2
50 deg
0.05
Workshop on Climatic Analysis and Mapping for Agriculture
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Chlorophyll
Water
Dry matter
N
35 g cm-2
0.02 cm
0.01 g cm-2
1.3
Laef
biochemistry
CR Model: inverse mode
SAILH
R canopy
Canopy
model
PROSPECT
Leaf
R and T
Leaf
model
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Laef
biochemistry
CR Model: inverse mode
SAILH
R canopy
Canopy
model
PROSPECT
Leaf
R and T
Leaf
model
Canopy structure
LAI
MTA
hot spot
….
3 m2m-2
50 deg
0.05
Workshop on Climatic Analysis and Mapping for Agriculture
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Chlorophyll
Water
Dry matter
N
35 g cm-2
0.02 cm
0.01 g cm-2
1.3
Laef
biochemistry
CR Model inversion: method
The models parameters are iteratively “adjusted” until the modeled
reflectance closely “resembles” the measured one
modeled
observed
A sum of squares cost function may be the measure of this
“resemblance” to be minimized:
n dir
C 
obs
n spec
meas
  R
k 1
k , i , obs
 R k , i,mod

2
i 1
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Model output: biophysical maps
Multitemporal LAI maps from MODIS reflectance
LAI map from hyperspectral MIVIS data
Meroni et al., 2004
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Carbon modeling: BIOME-BGC overview
BIOME-BGC estimates fluxes and storage of energy, water, carbon, and
nitrogen for vegetation and soil on a daily basis
Major Features:
• Daily time step (day/night
partitioning based on daily information)
• Single, uniform soil layer hydrology
(bucket model)
•1 canopy layer (sunlit/shaded leaf
partitioning)
• Dynamic phenology and C/N
allocation (e.g. LAI, biomass, soil and
litter)
• Variable litter and soil C
decomposition rates (3 litter and 4 soil
C pools)
Heinsch et al., 2001
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Biome-BGC: modified version
COUPLING Biome-BGC WITH THE PROSAIL RADIATIVE TRANSFER MODEL
NDVI 
•
At daily time-step LAI values simulated by
the BIOME-BGC model are used as input for
the PROSAIL model
•
PROSAIL calculate daily shortwave, PAR
transmitted and absorbed by the canopy
and site shortwave albedo
•
APAR and shortwave absorbed are forced
into Biome-BGC (radtrans routine) and then
are divide into sunlit and shaded canopy
portion.
LAI
Site characteristics and
Inizialization data
SW and PAR
Albedo, Abs, Trans.
BIOME - BGC
Meteorological Data
MODELED GPP
input
output/input
output
model
Migliavacca et al., 2004
R a d ia t iv e T r a n f e r D e s c r ip t io n
B io m e - B G C v 4 . 1 1
B io m e - B G C c o u p le d w it h P R O S A I L
  L a m b e r t B e e r d e s c r ip t io n
  A s s u m p t io n o f c o n s t a n t s it e a lb e d o
  P la t e m o d e l P R O S P E C T f o r t h e
d e s c r ip t io n o f le a f o p t ic a l p r o p e r t ie s
  1 D t u r b id m e d iu m S A I L 4 f lu x e s K - M
th e o ry fo r N D V I
  S a m e m o d e l, 2 f lu x e s a p p r o x im a t io n f o r
a lb e d o , t r a n s m is s io n a n d a b s o r p t io n
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r NIR  r R
NDVI MODIS
PROSAIL
Biome Characteristics
•Ecophysiological parameters
•Phenology (onday, offday)
r NIR  r R
Recalibration strategy
RECALIBRATION OF THE MODIFIED BIOME-BGC MODEL
An inverse modeling approach is used to recalibrate the input parameters of the PROSAIL-BGC
model. The optimization technique is based on a Quasi-Newtonian algorithm that minimizes the
sum of squared differences between observed and modeled data.
•
The recalibration strategy was based on a two steps approach:
Inversion Step
Scale
Objective
Observed Data
Target Variables


1
Local Scale
To determine the uncertain of some ecophysiological parameters
Eddy Covariance
Measurement s
(GPP 2002)



2
Regional
Scale
To retrieve phenological parameters and site characteristics
application at regional scale
– Model

NDVI MODIS 250 m
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
C:NLeaf
Fraction of leaf N in
RUBISCO (PLNR)
Fine root to leaf carbon
allocation ratio (FR:LC)
Maximum stomatal
conductance (gsmax)
yearday to start new
growth (onday)
yearday to end litterfall
(offday )
first -year maximum leaf
carbon (LC )
Recalibration steps
First – Step Recalibration
OPTIMIZATION ALGORITHM - (Leaf C:N; PLNR, gsmax, FR:LC)
An inversion against GPP measurements (year
2002) was applied in order to obtain a better
ecophysiological parameterization
PROSAIL
Biome Characteristics
•Ecophysiological parameters
•Phenology (onday, offday)
LAI
Online framework for gap-filling and fluxpartitioning of eddy covariance flux data
(Reichstein 2003)
SW and PAR
Albedo, Abs, Trans.
Site characteristics and
Inizialization data
BIOME - BGC
Meteorological Data
Eddy Covariance
Partitioning
n  obs
f 
 ( GPP
i  MOD
 GPP i  OBS )
2
i0
The new ecophysiological dataset will be use to
estimate carbon budget of poplar plantations at
regional scale
COST FUNCTION
EVALUATION
MODELED GPP
Second – Step Recalibration
OPRIMIZATION ALGORITHM (On date ; Off date)
PROSAIL
Biome Characteristics
•Ecophysiological parameters
•Phenology (onday, offday)
LAI
SW and PAR
Albedo, Abs, Trans.
n  obs
f   ( NDVI
i0
MODIS
 NDVI
PROSAIL
)
2
MODIS FILTERED
NDVI TIME SERIES
COST FUNCTION
EVALUATION
Method to estimate phenological parameters by
comparing the NDVI values retrieved from MODIS
data and the NDVI values estimated with the
PROSAIL-BGC model
Site characteristics and
Inizialization data
BIOME - BGC
MODIS - TERRA
Meteorological Data
MODELED GPP
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NDVIPROSAIL-BGC estimated for the same day and
for the same sun-surface-geometry of MODIS
observations.
Assimilation of RS data
Regional scale: deconvolution
and filtering
MODIS 250m 16-days composite
NDVI data (Product MOD13Q1)
Time period 2000-2003
Original vs Filtered MODIS NDVI
10000
9000
8000
NDVI (x10000)
7000
6000
5000
4000
3000
2000
Original MODIS NDVI
1000
FILTERED MODIS NDVI
0
1/
1/
00
1/
7
/0
0
31
/
12
/0
1/
7
0
/0
1
31
/
12
/0
1
1/
7/
02
31
/
12
/0
2
1/
7
/0
3
31
/
12
/0
3
DOY
Recursive application of a Savitzky-Golay filter
MODIS 2003 NDVI
[250x250m; 2 bands; 0.4-0.9m; daily; regional application]
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Preliminary results: 1st step local scale
The new set of the eco-physiological parameters
was then used to simulate annual GPP for the
2003 growing season, achieving a good
improvement in GPP estimation.
•
Year
Year
GPP
measured
GPP
measured
-2year -1
gC m
gC
m
year
2003
200
3
1473
1473
GPP
before
rec.
GPP
before
_rec
_
GPP
after_rec
_rec.
GPPafter
-2 year -1
gC
gCmm
year
-2 year -1
gC
gCmm
year
1304
126
5
RMSE:
EF:
1569
1569
Loague et al., 1991
Before rec.: 1.81 gC m-2 day-1 Before rec.: 0.77
After rec.: 1.41 gC m-2 day-1
After rec.: 0.86
Cumulative GPP 2002/2003 (error %):
Prosail - Biome-BGC (before rec.) 12%
Prosail - Biome-BGC (after rec.) 2%
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0.76
0.0006
Preliminary results: 2nd step for regional scale
The recalibration against MODIS data was used in order to obtain an estimate of the
start and the end of the growing season.
•
Simulation:
Year
EPC DATA: Recalibrated ecophysiological parameters
START - END FIRST GUESS: Random
Is the optimisation technique able to determine the
corrects start and end of growing season?
2002
2003
*
Workshop on Climatic Analysis and Mapping for Agriculture
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S ta rt
End
O b s .*
91
267
E s t.
88
279
O b s .*
78
315
E s t.
70
310
Start and End of growing season were determined as the
dates of NEE inversion.
Ongoing: spectral measurements at the experimental site
 Long
term
continuous
spectral
measurements (2 spectra h-1): passive
fluorescence with FLDP,
r400-1100, VIs (e.g.
CO2H2O,T
sonic anemometer & IR gas analyzer
inlet
PRI, REP) at the eddy covariance site;
Spectral system optics
micromet sensors
 Discontinuous field campaign for spectral
Eddy and Spectral system housing
heterogeneity sampling.
Gitelson et al., 2004
soil met sensors
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Conclusions
A)
NEE and Canopy characteristics
•
Temporal and spatial behavior of canopy characteristics (e.g. LAI and fIPAR) exerts a control
in annual NEE; efforts in their measurements;
B)
RS and biophysical retrieval
•
Inversion of leaf & canopy reflectance models provide accurate estimation of LAI (MVA, LAI
RMSE=.40 m2m-2);
C)
Carbon modeling
•
The assimilation methods can provide important informations for the application of the
BIOME-BGC process models at regional scale;
•
Inverse modelling approach against eddy covariance measurements allow to determine the
ecophysiological dataset that improve GPP estimation and to determine start and end of growing
season
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thank you
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Biome-BGC Modification – 2/4
Main features of PROSAIL Model
(Jacquemoud)
SAILH
R canopy
Canopy
PROSPECT
Leaf R and T
Leaf
Biochemical
Leaf Model
model
Canopy Structure
Sun-Surface-Sensor geometry
Soil Reflectance
LAI
MTA
….
...
50 deg

•
Schematic description of fluxes within the canopy
•
Two diffuse fluxes (E+ upward – E- downard )
•
E0 - Direct solar flux
•
Es - Flux in the direction of observation
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
Clorophyll
Water Thick.
SLA
N
Biome-BGC modification
COUPLING Biome-BGC WITH THE PROSAIL RADIATIVE TRANSFER MODEL
Goals of modification
•
•
To Improve Biome-BGC simulations at experimental site
using a different description of canopy radiation regime
•
Radiative transfer regime describe using K-M approssimation
•
Shortwave and PAR albedo linked to phenological cycle
To simulate NDVI MODIS for a given sun-surface-sensor
geometry
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Biome-BGC Modification – 4/4
Comparison between Biome-BGC and PROSAIL-BGC
Biome -BGC
PROSAIL-BiomeBGC
Observed GPP
Year
G P P m e a su re d
gC m
-2
year
-1
G P P B io m e - B G C
gC m
-2
year
-1
G P P P R O S A IL-B G C
gC m
-2
year
2002
1567
1330
1362
2003
1473
1265
1304
-1
Slightly improves the accuracy of GPP estimations (for both year).
Cumulative GPP 2002/2003 (error %):
Biome-BGC 15%
Prosail - Biome-BGC 12%
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G PP (
0 .0 0 6
0 .0 0 4
Preliminary Results – 1/4
0 .0 0 2
First-Step Recalibration Results (against GPP)
0 .0 0 0
50
100
150
200
250
300
350
DOY
0 .0 1 6
0 .0 1 6
G PP 2 0 0 2 - O b serv ed
GPP
A fte r R e c
= 1546 gCm
-2
-2
year
year
-1
-1
G P P (K g C m
0 .0 0 8
0 .0 0 6
0 .0 0 4
0 .0 0 2
0 .0 0 0
50
100
150
200
250
A fte r re ca lib ra tion
-1
year
= 1327 gCm
day
= 1578 gCm
B e fo r e R e c
-2
0 .0 1 0
obs
0 .0 1 4
300
350
0 .0 1 2
-2
)
-1
GPP
-1
M o d e le d G P P ( K g C m
GPP
-2
)
G P P 2 0 0 2 - M o d - B e fo r e R e c .
0 .0 1 2
day
B e fore re ca lib ra tion
G P P 2 0 0 2 - M o d - A fte r R e c .
0 .0 1 4
0 .0 1 0
0 .0 0 8
0 .0 0 6
0 .0 0 4
B efore-R ec.
slope: 0.68
Intercept: 0.001
r ²: 0.88
0 .0 0 2
0 .0 0 0
0 .0 0 0
0 .0 0 2
0 .0 0 4
DOY
0 .0 0 6
0 .0 0 8
0 .0 1 0
O b se rv e d G P P (K g C m
-2
A fter-R ec.
slope: 0.83
Intercept: 0.0009
r ²: 0.93
0 .0 1 2
0 .0 1 4
-1
day )
0 .0 1 6
B e fo re
A fte r
R e ca lib ra tio n R e ca lib ra tio n
0 .0 1 4
A ft e r re c a lib ra t io n
Recalibrated ecophysiological parameters
0 .0 1 2
M o d e le d G P P (K g C m
-2
day
-1
)
B e fo re re c a lib ra t io n
0 .0 1 0
F R C :L C
-
0 .3 3 3
Le a f C :N
(K g C /K g N )
1 5 .5 9
2 1 .6 6
PLN R
g sM A X
-
0 .0 8 8
0 .0 0 6
0 .1 0 5 4
0 .0 0 4 1
0 .0 0 8
0 .0 0 6
0 .0 0 4
0 .0 0 2
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14-17 June 2005, Bologna, Italy
B e fo re -R e c .
slo p e : 0 .6 8
In te rc e p t: 0 .0 0 1
slo p e : 0 .8 3
In te rc e p t: 0 .0 0 0 9
m /s
2 .0 0 1
0 .0 1 6
Assimilation of RS data
•
MODIS 250m 16-days composite NDVI data (Product MOD13Q1)
•
Time period 2000-2003
•
RETRIEVE
PROFILES
HIGH
QUALITY
MODIS
NDVI
•
Selection high quality observation (according
to MODIS Quality Assurance flags)
•
Recursive application of a Savitzky-Golay filter
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Assimilation of RS data
Regional scale: deconvolution
Low Res./High Freq PIXEL
250 m
rice (fc = 70 %)
MODIS 2003: unmixed NDVI
deciduous (fc = 15%)
poplar (fc = 15%)
[250x250m; 2 bands; 0.4-0.9m; daily; regional application]
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Final comparison
Comparison between measured and modeled annual GPP (2003)
after recalibrations
0.01 6
0 .01 8
B e fo re R e c a lib ra t io n
GPP 2 00 3 - Ob s
GPP 2 00 3 - A fte r Re c. (2 Step )
0 .01 6
0.01 4
A ft er R e c a lib ra t io n
GPP 2 00 3 - B efo re R ec.
)
-1
-1
0 .01 2
-2 y
da
day
-2
0 .01 0
0.01 2
)
0 .01 4
0.01 0
0.00 8
0 .00 8
0.00 6
0 .00 6
0.00 4
0 .00 2
Recalibrated ecophysiological parameters
0 .00 0
0
50
10 0
1 50
2 00
DOY
250
30 0
35 0
Mo de le d G PP (Kg C m
GPP (Kg C m
0 .00 4
Bef ore-R e c.
slope : 0. 76
Inte rce pt: 0.00 06
r ²: 0. 78
0.00 2
0.00 0
0 .000
0 .002
0 .00 4
0.00 6
0.00 8
0.0 10
O b se rve d G PP (Kg C m
-2
0.0 12
0.0 14
0. 016
-1
da y )
RMSE: 1.59 gC m-2 day-1
EF: 0.81
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Af ter-R ec .
slope : 0. 77
Inte rcep t: 0.00 21
r ²: 0. 78
METHODS – Application of Biome-BGC at Regional Scale
Ecophysiological Parameters
(Clone I-214)
Solar Radiation, PAR
Temperature, Precipitation,
VPD
Standing Biomass (Leaf
Carbon etc..)
Site Characteristics (Texture,
Soil Depth, etc.)
Credits: Bigfoot Project
Workshop on Climatic Analysis and Mapping for Agriculture
14-17 June 2005, Bologna, Italy
Data Assimilation Approaches
•
Determination of Model Inizialization Parameters
•
Forcing
•
Estimation And Optimization Through Model Inversion
Workshop on Climatic Analysis and Mapping for Agriculture
14-17 June 2005, Bologna, Italy
NEE characterisation
6
15
4
10
2002
5
2
2003
NE E (µ m o lCO 2 m
-2
-1
NEE (gC m-2 day-1)
s )
0
-5
-10
-15
-20
-25
0
-2
-4
-6
-8
-30
29 Giug no 2 0 02
2 9 G iug no 20 0 3
-10
-35
0. 00
2.00
4.00
6.00
8.00
10.00
12. 00
14.00
H o ur o f Day
16. 00
18.00
20. 00
March - April
22.00
August - September
-12
0
50
100
150
200
250
Day of Year
Daily variability
Inter-annual variability
Workshop on Climatic Analysis and Mapping for Agriculture
14-17 June 2005, Bologna, Italy
300
350
400
Canopy characteristics and NEE at the experimental site 3/3
Footprint
Poplar
Spatial trend
of LAI
4
2
N E E (µ m o lC O 2 m
-2
-1
s )
0
-2
-4
-6
-8
-10
NEE = 0 + 1LAI + 2ln(PAR)
-12
-14
-16
0
200
400
600
800
-2
1000
1200
1400
-1
P A R (µ m ol m s )
Workshop on Climatic Analysis and Mapping for Agriculture
14-17 June 2005, Bologna, Italy
Conclusions
•
The proposed methodology can provide important informations for the
application of process models at regional scale
•
Modified version of Biome-BGC sligthly improve the accuracy of GPP estimation and permit
to simulate NDVI MODIS.
•
Inverse Modelling approach against Eddy Covariance Measurements permit to determine
an ecophysiological dataset that improve GPP estimation
•
Inverse Modelling approach against MODIS NDVI permit to determine start ad end of
growing season
•To
improve this methodology in order to recalibrate other phenological parameters and
site characteristics (e.g. transfert growth and litterfall period; maximum leaf carbon, soil
effective depth).
•Optimization
algorithm (for second step rec.) is not still stable.
Workshop on Climatic Analysis and Mapping for Agriculture
14-17 June 2005, Bologna, Italy
LTDA
Thank you
Workshop on Climatic Analysis and Mapping for Agriculture
14-17 June 2005, Bologna, Italy
LTDA