VEGETATION PREPARATORY PROGRAMME
STEM-VGT:
Satellite Measurements and Terrestrial Ecosystem
Modelling
using VEGETATION instrument
Prelaunch Phase
Final report
15 July 1998
Convention N° 95/CNES/0427
Principal Investigator
Gérard DEDIEU (CESBIO)
Tel : (+33) 5.61.55.85.26 Fax : (+33) 5.61.55.85.00
Gerard.Dedieu@cesbio.cnes.fr
Co-Investigators
Jean-Claude GERARD (Université de Liège-LPAP)
Dean GRAETZ (CSIRO-Earth Observation Centre)
Participants
A. CHEBOUNI (ORSTOM)
A. FISCHER, W. CRAMER (PIK)
L. FRANCOIS, C. PICCHI (LPAP)
B. BERTHELOT, P. CAYROL, P.MAISONGRANDE, S.
MOULIN (CESBIO)
L. KERGOAT (LET)
A. RUIMY, B. SAUGIER (LEV)
STEM-VGT: Satellite Measurements and Terrestrial Ecosystem Modelling using VEGETATION instrument
Prelaunch Phase Final Report
Summary
During the pre-launch phase of the project, we addressed the general topic of the use of
VEGETATION measurements to improve the modeling of biosphere functioning. At the regional
scales, the aims are the assessment of biomass production and possibly water balance. The aim of
global scale studies is related to global change and global carbon cycle issues.
We explored, and present in this report, three different directions for coupling biosphere models and
remotely sensed measurements, namely :

Test of vegetation model results : model outputs are compared to satellite observations. We
present examples where the analysis of discrepancies provided a efficient guide to improve
process modeling or model parameters.

Driving of vegetation model : the TURC diagnostic model uses NDVI as input to determine
absorption of solar radiation from which net primary productivity is estimated. Satellite NDVI put
strong constraints on the model. By now, this approach is probably the more robust to estimate
vegetation production. We show with some examples that the capabilities of this approach to
detect year to year fluctuations of NPP are quite promissing. However, long term analysis of NPP
is limited by the quality and consistency of the measurements provided by current satellite sensors.

Assimilation techniques : this approach is based on optimization techniques that compare the
distance between model outputs and satellite observations. It allows a fine tuning of model
parameters, even if these parameters cannot be observed directly from space. Results we
obtained with shortwave measurements are quite promising and open the path to regional
assessment of vegetation functioning. The benefit of thermal infrared measurements is more
difficult to assess and requires further work.
In addition, we prepared satellite datasets to serve the project, including the reprocessing of five years
of NOAA Global Vegetation Index dataset. The resulting product (LASUR) has been distributed to
international teams for evaluation purpose and for use.
The post-launch phase will mainly consist in the evaluation of the benefit gained from the use of
VEGETATION data for vegetation modeling. We will continue to explore the three directions explained
above, with a special emphasis on the development of assimilation techniques in the framework of the
SALSA and EUROSIBERIAN CARBONFLUX experiments.
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Table of contents
1.
OBJECTIVES OF THE INVESTIGATION ....................................................................................... 4
1.1
1.2
1.3
2.
BACKGROUND ............................................................................................................................. 4
APPROACH.................................................................................................................................. 5
EXPECTED RESULTS .................................................................................................................... 6
DATA SIMULATION ........................................................................................................................ 7
2.1
2.2
3.
EXISTING DATA SETS ................................................................................................................... 7
REPROCESSING OF NOAA/GVI : THE LASUR PRODUCT ............................................................... 8
BUILDING OF DATA SETS ........................................................................................................... 12
3.1
3.2
HAPEX-SAHEL ........................................................................................................................... 12
AUSTRALIA ................................................................................................................................ 12
4.
USE OF SATELLITE DATA TO DRIVE TERRESTRIAL BIOSPHERE MODELS ........................ 15
5.
USE OF SATELLITE DATA TO TEST PROCESS MODELS ....................................................... 21
5.1
5.2
6.
CESBIO MODEL........................................................................................................................ 21
LPAP MODEL (CARAIB) ........................................................................................................... 26
ASSIMILATION OF REMOTELY SENSED MEASUREMENTS ................................................... 29
6.1
INTRODUCTION .......................................................................................................................... 29
6.2
MATERIAL AND METHOD ............................................................................................................. 30
6.3
RESULTS .................................................................................................................................. 30
6.3.1
Assimilation of shortwave data ........................................................................................ 30
6.3.2
Assimilation of longwave data .......................................................................................... 33
7.
IMPACT OF DOUBLE CO2 ON CLIMATE AND VEGETATION ................................................... 36
7.1
7.2
8.
RESULTS : LAI SIMULATION........................................................................................................ 37
RESULTS : EVAPOTRANSPIRATION SIMULATION ........................................................................... 40
WORKPLAN FOR THE POST-LAUNCH PHASE ......................................................................... 42
8.1
8.2
8.3
8.4
8.5
9.
SATELLITE DATA TO IMPROVE VEGETATION PROCESS MODELS ..................................................... 42
ASSESSMENT OF CARBON BUDGET AT THE REGIONAL SCALE ....................................................... 44
ADAPTATION OF VEGETATION TO CLIMATE .................................................................................. 44
GLOBAL CARBON CYCLE AND CHANGE MONITORING .................................................................... 44
PRODUCT REQUEST ................................................................................................................... 45
CONCLUSION AND PERSPECTIVES .......................................................................................... 46
10.
10.1
10.2
REFERENCES ........................................................................................................................... 48
QUOTED PUBLICATIONS ............................................................................................................. 48
PUBLISHED WORKS THAT USE LASUR DATASET ......................................................................... 51
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1. OBJECTIVES OF THE INVESTIGATION
Our general objective is to develop process models of vegetation functioning that can be used at
regional and global scale for predicting carbon and water exchanges between land surface and the
atmosphere.
In this text, "vegetation functioning" refers to the temporal development of vegetation characterized by
carbon uptake, phenology, carbon pools, biomass and its allocation within the various plant
components (stems, roots, leaves, etc.). In a first step, we will focus on the assessment of fluxes at the
seasonal time scale. Process models (TBMs: Terrestrial Biogeochemical Models) attempt to describe
in detail most of biological processes but they require input parameters that are generally available for
only a few ecosystems. The spatial patterns of these processes is not always well described within all
ecosystems. The specific objective of this project is to develop and validate satellite-based methods for
calibrating such spatially heterogeneous vegetation process models.
We will first describe the work carried out to build the data sets that are needed to run TBMs. Then, we
will present the use of satellite data to drive (section 4) and validate (section 5) the models.
Assimilation techniques have been tested with both shortwaves and longwaves measurements and
preliminary results are described in section 6. Finally, section presents our workplan for the postlaunch phase .
1.1 Background
A number of statistical, parametric or process (mechanistic) vegetation models have been developed
in view to assess crop yield, Net Primary Productivity (NPP) or energy and mass exchanges with the
atmosphere. Each of the existing vegetation models has its advantages and drawbacks, but they all
need to be further validated. Several compartments, or sub-models, like water balance and phenology,
may be improved, as may be the time scale, space resolution and coupling with physical processes
like energy and mass exchanges. A number of issues and assumptions have to be further investigated:
i) most of the existing vegetation models are calibrated using a limited set of ground based
measurements and then spatially extrapolated to similar ecosystems (e.g. Raich et al., 1991, Ruimy et
al., 1994). Remotely sensed data could help us to validate models and to retrieve some of the
parameters over large areas.
ii) our interest is in prognostic models that could be used to predict vegetation functioning under
various scenarios of climate change, or coupled to atmospheric circulation models. This means that we
must assess the use of remotely sensed data for model validation or parameter estimation, not only for
retrieving state variables such as Leaf Area Index.
iii) Most of the models dealing with regional and global scales assume that every model grid-cell,
generally corresponding to several hundreds of square kilometers, is an homogeneous medium. This
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concept has to be checked and possibly improved, for example by investigating sub-grid processes
and upscaling methods.
These issues must be addressed in the framework of long term multidisciplinary projects, such as the
ones developed under the auspice of IGBP or supported by the Environment and Climate Programme
of the European Commission. We are contributing to several of these programmes (see the proposal
for details). In the frame of the VEGETATION Preparatory Program, our objective is more a limited
feasibility study i) to investigate the potential of VEGETATION and HRVIR sensors in solving some of
the above mentionned issues and develop the required methods, and ii) to prepare the integration of
VEGETATION data within terrestrial biosphere and carbon cycle models.
1.2 Approach
Three different types of TBMs aimed at estimating Net Primary Productivity are being developed by
research groups at CESBIO, LPAP and PIK. The first type is a diagnostic and parametric model, based
on the Monteith's model (1977), which relies on satellite data inputs (Ruimy et al. 1994, Maisongrande
et al. 1995, Ruimy et al., 1996-a). The second type of models are prognostic and process models
which may use satellite data for initialization or validation but can be run alone (Kergoat et al. 1995-a &
b, Warnant et al., 1994). These models operate with short time resolution (typically 1 day), space
resolution of 0.5°x0.5 or 1°x1°, and are integrated over periods ranging from a few days to several
years.
Within the frame of this project, our objectives are to investigate the potential of VEGETATION
measurements for contribution to these modelling activities. In order to improve soil moisture
estimates, we also plan to supplement optical measurements by thermal infrared data that we will be
used to constraint a surface flux model. We will address the following general topics :
 development, assessment, improvement of assimilation techniques of SPOT-4 HRVIR and
VEGETATION measurements (actual or simulated) in vegetation models, including SWIR
band ;
 most of vegetation processes such as phenology, growth, photosynthesis strongly depend on
water availability. We plan to couple vegetation process models with a SVAT model in order:
 to improve soil moisture estimates
 to supplement satellite measurements in the shortwaves domain (HRVIR,
VEGETATION) by thermal infrared data to add more constraints on the models
through multispectral assimilation
 investigation of the use of combined high and low space resolution to account for sub-grid
surface (vegetation, soil) heterogeneity
 assessment and improvement of "ecological rules", combined with assimilation techniques to
further constraint the system or provide parameters which cannot be derived from satellite
measurements. By "ecological rules", we mean a-priori hypothesis on the strategies that
vegetation has developed during evolution to survive a given environment.
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1.3 Expected results
The investigation should provide a detailed assessment of the feasibility of constraining vegetation
process model by simulated and then actual VEGETATION measurements in the shortwaves.
Coupling vegetation and SVAT models would allow to investigate the use of SWIR band and the
interest of thermal infrared measurements. This evaluation could be of interest for a possible evolution
of the VEGETATION mission specification or of other sensors. It should provide a simulation tool for
the specification of mission parameters such as the time and frequency of acquisition.
The results of this investigation should also contribute to the improvement of global vegetation models
used for global carbon cycle studies, by providing better initialization or validation.
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2. DATA SIMULATION
We are mainly using NOAA/AVHRR and SPOT-HRV data. The NOAA/AVHRR archive we use
consists of four different data sets.
2.1 Existing data sets

AVHRR measurements other Europe are routinely processed by the Institute of Remote Sensing
Application (IRSA) of the Joint Research Center of Ispra, in the framework of the MARS project.
IRSA kindly supplied us with daily, 1km, data acquired over France from 1991 to 1994. These data
are geometrically and radiometrically corrected. Atmospheric corrections are based on a
parameterization of the 5S code. Inputs are water vapor and ozone monthly climatologies. No
correction is done for aerosols.

We are also using AVHRR measurements acquired on a 5x5 degrees area centered near Niamey
(Niger) where the Hapex Sahel took place in 1992. These data have been processed by the
Hapex Sahel Information System (HSIS), and consist of daily, 1km, AVHRR data for 1991 and
1992 (from may to october). Both Top of the Atmosphere and atmospherically corrected data,
using the SMAC code, are available. Detailed information are available on
http://www.orstom.fr/hapex.

The following AVHRR data sets have been acquired by Dean Graetz at CSIRO over Australia
(continental coverage) :
 raw GAC (afternoon pass only) from 1981 - 1992 - thereafter HRPT until present; yet to be
processed to a precision product.
 NASA processed GAC NDVI (ex Jim Tucker), 1981-1992
 NASA Pathfinder GAC (8 km), daily datasets for 1982-1994
 NASA Pathfinder GAC (8 km), 10 day composites for 1982-1994
 NASA Pathfinder GAC (8 km), monthly composites for 1982-1994
In addition, GMS Pathfinder data have been acquired for the period July 1994 - December 1995.
Unfortunately, the use of cuurent NOAA/DAAC pathfinder data for quantitative analysis is limited.
A limited number of SPOT scenes have been acquired over the Hapex-Sahel site and geometrically
and radiometrically corrected by LPAP in collaboration with VITO (F. Veroustraete). We plan to acquire
new SPOT data in 1998 over the SALSA area.
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2.2 Reprocessing of NOAA/GVI : the LASUR product
Satellite measurements offer the unique opportunity to monitor vegetation at the global scale, but
complex processing is required for a reliable analysis of interannual variability and use within
assimilation procedures.
In figure 1, zonal variation of Net Primary Productivity (NPP) is plotted for two years, 1989 and 1991.
NPP is derived from a model (see section 4.) driven by NDVI computed from NOAA/AVHRR
measurements (Maisongrande et al., 1995). The lower NPP of the southern hemisphere in 1991 is due
to the eruption of the Pinatubo volcano which injected large amount of aerosols in the stratosphere in
june 1991. Aerosol scattering and absorption lead to a decrease of NDVI, and therefore a decrease of
NPP estimates. In other words, the variation of NPP between 1989 and 1991 as seen in figure 1 is
mainly due to atmospheric effects on satellite measurements, and not to a change in actual
productivity.
Figure 2 presents time evolution of NPP as a function of latitude from 1986 to 1991. NPPs have been
averaged weekly over zonal belt of 1°. Seasonal and geographical variations of NPP are clearly
depicted, but a more quantitative assessment is difficult. For example, the change of satellite in
november 1988 is visible, even if sensor calibration drift was taken into acount. This is due to the
orbital drift of NOAA-9 which lead to later and later time of overpass, and consequently to an evolution
of surface directional effects. When NOAA-11 was launched, time of overpass was again the nominal
one, i.e. around 13 :30, and surface directionnal effects differed from the ones observed by NOAA-9.
Another feature we already mentionned is the decrease of NPP in 1991 in the southern hemisphere.
Figure 1 : zonal variation of Net Primary Productivity (NPP) for 1989 and 1991. NPP is derived from a
model driven by NDVI computed from NOAA/AVHRR measurements (Maisongrande et al., 1995).
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Figure 2 : time evolution of Net Primary Productivity (NPP) as a function of latitude, from 1986 to 1991.
NPPs have been averaged weekly over zonal belt of 1°. (Maisongrande et al., 1995)
Figures 1 and 2 illustrate the need for accurate datasets. Therfore, we have reprocessed
NOAA/AVHRR dataset in order i) to test calibration, filtering and atmospheric corrections parameters
and methods at the global scale, and ii) to obtain improved global data sets that can be used by global
carbon cycle modelers and other applications.
The Global Vegetation Index product distributed by NOAA (Tarpley, 1984) we have acquired consists
of weekly global data at a 1/7x1/7 degrees resolution in Plate Carree projection. Seven channels are
available, namely raw digital counts of channel 1 and 2 (visible and near-infrared), calibrated thermal
infrared channels (Ch4 and 5), NDVI computed from raw counts, solar zenith angle and scan angle.
This data set suffers from some weaknesses analysed by Goward (1993)
In a first step, we reprocessed (Berthelot et al., 1997) two years of weekly data (1989 and 1990) that
correspond to the first two years in orbit of NOAA-11. Two CD-ROMs have been produced, that include
top of atmosphere and « surface » (atmospherically corrected) reflectances, temperatures and NDVI,
viewing and sun angles, plus a channel (flag channel) that indicates the data that should be discarded
and the reasons to discard (clouds, inconsistent angle, low solar elevation, ...). The product is called
LASUR (LAnd SUrface Reflectance) and is available at both 1/7x1/7 and 1x1 degrees resolution.
LASUR was designed to fit the needs of both remote sensing experts and scientific « end-users » who
do not wish to apply corrections by themselves.
A detailed description of the processing can be found in the documentation files distributed with the
CD-ROMs. Shortly, filtering procedure is intended to identified clouds, clouds shadows, large aerosol
optical depths, and abnormal data. It is based on improvements of the so-called Bise (Viovy et al.,
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1992) and INTUITIV methods (Loudjani et al., 1994). Atmospheric corrections are performed using the
SMAC code (Rahman and Dedieu, 1994). Water vapor and ozone contents are derived from
climatologies (Oort, 1983, and TOVS retrieval provided by CNRM). Aerosol optical depth is assumed to
increase from the poles to the equator. The atmospheric corrections we applied is therefore rather
close to the procedure used for actual VEGETATION measurements. The main steps of the
reprocessing and example of results are given in figures 3-a and 3-b.
The 1989-1990 LASUR dataset have been distributed to about 20 teams for evaluation purposes. The
result of this evaluation was positive. LASUR results were also compared (figure 4) to FASIR product
(Sellers et al, 1994). Following this evaluation, further years have been processed and the LASUR
product is now available for the period 1986-1990.
Information about LASUR, documentation file, and 0.5x0.5 degree resolution dataset are available on
the web at the following address : http://www-sv.cict.fr/cesbio/lasur/.
Figure 3-a : main steps of the LASUR
reprocessing of GVI, illustrated by NDVI maps
obtained for July 1990 (weekly composite).
Figure 3-b : example of surface filtered NDVI for
january, april, july and october 1990 produced by
the LASUR reprocessing of GVI ((weekly
composites).
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Figure 4 : LASUR versus FASIR maximum annual NDVI. 13110 pixels. Correlation coefficient = 0.89.
NDVI(LASUR) = 0.99 NDVI(FASIR) + 0.113. Lafont, 1998. See also Ouaidrari et al, 1997.
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3. BUILDING OF DATA SETS
The development of assimilation and upscaling techniques needs TBMs to be run at a finer resolution
(ideally 1km or even less) than usually done for global carbon cycle study (50 to 100km), and then to
integrate estimated flux at the scale of global model grid-cells. Consequently a significant amount of
work has been devoted to the collection and processing (remapping, interpolation of weather fields, ...)
of the data sets used as input to TBMs.
3.1 Hapex-Sahel
LPAP has prepared several data sets for the Hapex-Sahel square degree area. Both HSIS data base
and external information have been used, with the support of CESBIO for AVHRR data.
Input variables of the water balance model IBM (Improved Bucket Model, Hubert et al., submitted)
have been estimated at a 0.5x0.5 degrees resolution. IBM is coupled to the CARAIB model developed
by LPAP (Warnant et al., 1994). Input parameters and variables include precipitation, air temperature,
net radiation, rooting depth. Air humidity has been derived from radiosoundings performed every 6
hours. Interpolation at 0.5° relies on the use of ECMWF AGCM analysis and EPSAT-Niger data.
NOAA/AVHRR data available in the HSIS data base are daily data that need further processing for
eliminating clouds and other perturbing effects. The BISE procedure (Viovy et al., 1992) has been
using to generate « clean » data every ten days. Best results are obtained with a sliding window of 30
days, especially during the rainy season. Exemple of results are given in figures 5 and 6. This AVHRR
data set has been compared to NOAA Pathfinder data : similar time profiles are observed, but NDVI
levels are different.
3.2 Australia
The following digital datasets for landcover characterisation have been prepared by Dean Graetz at
CSIRO at 5 x 5 km spatial resolution for the entire Australian continent :
SOIL







soil type (descriptive)
density (kgs/m3)
estimated max soil depth (cms)
maximum profile water holding capacity (mms)
minimum saturated hydraulic conductivity
texture classes
nutrient status rank
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VEGETATION
 Type (descriptive; 31 classes)
 Overstorey LAI
 Overstorey projected foliage cover (pfc)
 Overstorey biomass (t/ha)
 Understorey LAI
 Understorey biomass (t/ha)
 Understorey projected foliage cover
 Biomass carbon density (t/ha)
 Estimated soil carbon density (t/ha)
 Estimated NPP (tC/ha/year)
 Estimated biomass carbon turnover time (years)
 Surface roughness (z0)
 Albedo (* month)
 Surface resistance (* month)
Figure 5 : Time profile of atmospherically
corrected NDVI derived from AVHRR daily
measurements, and of NDVI values resulting
from the BISE procedure. Data for a test site
located at 13.50°N, 2.70°, in 1992, 1 km
resolution.
Figure 6 : Time profile of filtered surface NDVI
for three different windows: 1 pixel (1km), 3x3
pixels and 5*5 pixels. Data for a test site
centered on 13.50°N, 2.70°, in 1992.
WEATHER
30 year monthly means for the following variables interpolated to 0.05, 0.01, and 0.005 ° :
 max temp
 min temp
 rainfall
 solar radiation with rainfall
 solar radiation
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






evaporation
dew point 0900 and 1500
dry bulb 0900 and 1500
wet bulb 0900 and 1500
raindays
wind run
wind speed 0900 and 1500
PIK (A. Fischer and W. Cramer) in collaboration with CSIRO (D. Graetz) has prepared 10 days
average of continental temperature (minimum and maximum) and precipitation fields from the
interpolation of daily measurements performed by 645 australian stations. Interpolation procedure uses
the digital elevation model (0.025° resolution) provided by the Australian National University. By now,
temperatures have been processed for the period 1981-1990, and precipitation for 1981-1989, with a 8
km resolution. The processing of further years is in progress.
Australian teams have identified on two large (>100 km2) and inert (rather than truly invariant) sites
that can be used to check radiometric stability and consistency of satellite data sets.
The first is Lake Argyle (128° 45'E; 16° 15'S): a dark (reflectance ~ 10%) target located in the tropical
north under high and seasonal atmospheric loadings of water vapour and aerosols from biomass
burning. The second is Strzleckie (139° 50'E; 29° 00'S); a bright (reflectance ~ 40%) white sand desert
dune field under low and variable aerosol and water vapour loadings. CSIRO has instrumented both
sites with Cimel sun photometers in conjunction with continuous or episodic surface reflectance
measurements in the VIS and NIR - including the VMI blue band.
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4. USE OF SATELLITE DATA TO DRIVE TERRESTRIAL BIOSPHERE
MODELS
The diagnostic global NPP model previously developed by Ruimy et al. (1994) has been improved by
computing NPP as the difference between carbon uptake by the vegetation (or gross primary
productivity GPP) and carbon released by autotrophic respiration Ra. Only GPP is dependent on solar
radiation, while Ra has a maintenance component that depends on biomass and temperature, and a
growth component that depends on C availability for growth. Thus, NPP is computed according to :
NPP = e' f c S – Ra
(1)
which is the basis of the TURC (Terrestrial Uptake and Release of Carbon) model (see Ruimy et al.,
1996, for a detailed description of the model). In this equation, e' is theconversion efficiency for GPP,
S is the incoming solar radiation (300-4000 nm), c is the fraction of this radiation that is available for
photosynthesis (cS is called PAR or photosynthetically active radiation), f is the fraction of incoming
PAR that is absorbed by vegetation, derived from NDVI data.
To get estimates of e' (conversion efficiency for GPP), Ruimy et al. (1995) compiled over one hundred
relationships between CO2 flux measured above plant canopies (from micrometeorological or chamber
methods) and incident solar radiation S. To compute Ra, a large number of respiration measurements
(expressed per unit biomass) have been used with a separation into various plant organs : leaves, fine
roots, wood. Thus average maintenance respiration rates have been computed at 20° and found to be
10.7 mgC gC-1 day-1 for leaves, 6.6 for fine roots and 0.5 for sapwood (same units). Sapwood is the
living part of wood, and has to be estimated. In our case, we started from a map of vegetation biomass
derived by Olson, derived leaf biomass from NDVI data, took fine root biomass equal to leaf biomass
and calculated wood biomass as the remainder. Sapwood was then derived from total wood biomass
using regression lines established on a few forest stands. The temperature dependence of
maintenance respiration was taken as a linear one, rather than as an exponential for there is ample
evidence that the Q10 values - often used in respiration - decreases markedly with increasing
temperature.
Finally, available carbon for growth was computed as the difference between GPP and total
maintenance respiration, and growth respiration was taken as a constant fraction (0.28) of this
available carbon.
Putting all this information into eq. (1) leads to a GPP of 125 Gt C/yr, an autotrophic respiration
of 74 GtC/yr (72% maintenance, 28% growth resp.) and thus a NPP of 51 GtC/yr (Fig. 7). It is
important to stress that in the TURC model there is no calibration, i.e. all parameters have been
derived from experimental data and no subsequent adjustment has been done.
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2500
90
70
2000
50
30
1500
10
-10
1000
-30
-50
-70
500
GPP
0
-90
-180
-140
-100
-60
-20
20
60
100
140
180
90
1500
70
1250
50
30
1000
10
750
-10
-30
500
-50
-70
250
Ra
-90
0
-180
-140
-100
-60
-20
20
60
100
140
90
180
1250
70
1000
50
30
750
10
-10
500
-30
-50
-70
250
NPP
-90
-180
-140
-100
-60
-20
20
60
100
140
0
180 g(C) m-2 yr-1
Figure 7 : Maps of continental Gross Primary Productivity (GPP), leaf and sapwood maintenance
respiration (autotrophic respiration, Ra), and Net Primary Productivity (NPP), as computed by TURC
for 1986 (Ruimy et al., 1996)
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Improvements to the TURC model may come from better estimates of solar radiation, and of f (using
better algorithms to derive it from satellite measurements).
Sensitivity studies concerning the input data on solar radiation and NDVI as well as the slope of the fNDVI relationship have been performed (Ruimy et al., 1996). The global and zonal values for predicted
incoming solar radiation are pretty close for the data sets based on GCM simulations, cloud cover
climatologies and remote sensing (Fig. 8-a). The greatest deviation (+14% for the global average) from
the bulk of the other data sets was found for solar irradiation predicted by the ISCCP cloud cover
project.
The quantitave effects of differences in NDVI caused by different data processing are rather important.
The CESBIO product (an earlier version of LASUR) and the FASIR-NDVI have been compared (Fig. 8b). Annual global GPP was found to be 46 % higher for simulations based on FASIR-NDVI than for
those based on the CESBIO product. These results are in accordance with a similar sensitivity study
performed for an other diagnostic model of biospheric carbon exchange fluxes, CASA (Malmström et
al., 1995).
In addition to the type of NDVI processing the slope of the f-NDVI relationship contributes substantially
to incertitudes of simulated GPP and NPP. The simulated carbon exchange fluxes show a relative
sensitivity of about 1 with respect to this parameter. For an increase of 10 percent in the slope of the fNDVI relationship GPP increased by 10.7 %, autotrophe respiration by 10.0% and NPP by 11.2 %.
Figure 8 : a) Incident PAR integrated over 10 degree latitude zones and one year. PIK: derived from
cloud observations (Leemans and Cramer, 1991), ISCCP: derived from satellite observations (Bishop
and Rossow, 1991), ISLSCP: derived from satellite observations (Meeson et al, 1995), CNRM: derived
from GCM outputs (Planton et al., 1991) b) Fraction of PAR absorbed, f, integrated over 10 degree
latitude zones and one year, as given by FASIR-NDVI product for 1987 (Sellers et al 1995) and derived
from CESBIO-NDVI for 1986 (Berthelot et al. 1994).
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The TURC model and the LASUR dataset have been used to estimate weekly global NPP from 1986
to 1990. Newly available global solar radiation dataset produced by the SRB-GEWEX project from
meteorological satellite data were used instead of AGCM analysis. These SRB products agree rather
well with AGCM estimates, excepted in convective regions, such as the tropics, where SRB dataset is
expected to be more reliable. In order to drive autotrophic respiration, we used air temperature at 2m
generated by ECMWF reanalysis activity. Compared to previous runs of the model, input datasets of
NDVI, temperature and solar radiation have been improved and correspond to each year considered.
Figure 9 is an example of the results we obtained. This figure maps over Africa the difference of NPP
between 1990 and 1989, with surimposed isolines of precipitation anomalies derived from the 2.5x2.5°
resolution dataset established by Huffman et al (1995). Differences of annual NPP in 1989 and 1990
are well correlated with precipation fluctuations over Sahel, Namibia/Angola and Botswana. The TURC
NPP model does not use precipitation, and these correlations result from the sensitivity of carefully
processed NDVI to the impact of drought situations. However, NPP over Tanzania is larger in 1990
than in 1989 despite a 30% decrease of precipitations. Further analysis is needed to explain this
phenomenon which could result from different temporal distribution of rainfall during the year. The
recent El-Niño 1997-1998 event drew attention on its impact on weather and biosphere. It is interesting
to note that even if 1989 and 1990 are not El-Niño years, fluctuations of NPP are very significant over
large areas.
Figure 9 : Differences of yearly Net Primary Productivity between 1990 and 1989, with surimposed
isolines of precipitations anomalies (e.g. +30 means that precipitations were 30% higher in 1990
compared to 1989). Resolution is 1x1°. Precipitations were interpolated from the 2.5x2.5° resolution
dataset established by Huffman et al (1995). NPPs were computed using the TURC model (Ruimy et col.,
1996) driven by the LASUR NOAA/AVHRR product and SRB-Gewex solar radiation. Precipitation data :
Huffman et al (1995).
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The assessment of global weekly NPP results for the period 1986-1990 is in progress (e.g. fig.10 and
11 for results over Africa and Australia). In a first step, NPP estimates and fluctuations are analysed
together with weather data. We also started to use FAO agricultural statistics available at country level.
Preliminary results are quite promising at country or regional level. Global long term trends of NPP are
more difficult to assess. The main limitation is due to the orbital drift of NOAA satellite that induces
complex bidirectional effects on the measurements that can hardly be corrected using NOAA/AVHRR
data alone. Modeling and normalization of bidirectional effects is being studied by several teams,
including colleagues in CESBIO involved in the POLDER programme. Normalization of bidirectional
effects will be included in the LASUR processing chain as soon as possible. This issue is crucial to
address long term trends of biosphere functioning.
Finally, we also plan to use SPOT4-VEGETATION data and to work on specific algorithm to establish
consistent long term AVHRR and VEGETATION data archives.
Figure 10 : variations of NPP over tropical Africa between 1986 and 1987 (top) and 1989 and 1989
(bottom), respectively. The TURC NPP model was driven by LASUR dataset.
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Figure 11 : NPP and its year to year variations over Australia. The TURC NPP model was driven by LASUR dataset.
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5. USE OF SATELLITE DATA TO TEST PROCESS MODELS
5.1 CESBIO model
We have develop a mechanistic and pronostic model (Kergoat, 1998) that is built on the knowledge of
basic ecophysiological processes. This model does not require satellite data, even if it can be
constrained by information retrieved from such data (e.g. phenology). It is based on the assumption
that vegetation adapts to the local climate, leading to a development of the canopy which ensures a
large light absorption but prevents severe soil water depletion. Particular attention has been paid to the
processes which strengthen or weaken the basic LAI/evapotranspiration relationship, such as : soil
moisture feedback on transpiration and evaporation, deciduousness of the canopy and interception
losses. The model predicts LAI (fig. 12) and vegetation functioning (e.g. photosynthesis). It is driven by
climate variables such as mean monthly temperature and precipitation and land characteristics such as
soil water holding capacity and biome distribution. Phenology is currently derived from satellite data
(Moulin et al., 1997).
Our efforts have focused on the test of the model with available data, and, from the learnings of these
tests, on the improvement of the model. We must stress that the model was never calibrated against
data, and only rely on parameters and knowledge found in the litterature.
Figure 12 : Global estimate of LAI, based on the availability of water (Kergoat, 1998).
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A first test was to compare modeled annual water balance to stream flow data, since it is one of the
rare large scale datasets available for TBM evaluation. Over a large river basin, the long term water
balances reduces to :
Pr - Etr = DR
where DR is the annual long term average stream flow, Pr and Etr basin-averaged precipitation and
actual evapotranspiration. Thirty years average rivers discharge were taken from Probst abd Tardy
(1987). These data mainly follows UNESCO reports (UNESCO, 1977). The annual evapotranspiration
and the basin contours (kindly provided by CGS) are shown in figure 13. For the 28 rivers we
considered, the runoff simulated by the LAI model is plotted against the measured runoff (Fig. 14). The
overall result is that the large variability of both runoff and evapotranspiration is well reproduced by the
model. Two caveats however have to be considered : a) both runoff and precipitation observations can
display significant errors and b) on a basin scale, climate alone exerts a strong constraint on the water
balance and the surface processes effects are therefore bounded. Nevertheless, the general
agreement found on Fig. 15 suggests that using an explicit canopy scheme and general physiology
leads to reasonnable results.
Figure 13 : Annual evapotranspiration estimated by the vegetation model and contours of river basins
used for comparisons presend in figs. YY and YY (Kergoat, 1998).
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Figure 14 : Modeled versus observed basin- Figure 15 : Modeled versus observed basinaveraged runoff, for 28 large rivers
averaged evapotranspiration, for 28 large rivers
A second test was to compare some of the pronostic variables of the model to satellite data. Model
estimate of LAI are used to predict APAR (photosynthetically active radiation which is absorbed by the
vegetation). We used satellite data to derive APAR values globally, with a weekly time step. Model and
satellite APAR values have been compared. We present here the results for winter and summer
months (Fig. 16).
The general agreement between the two estimates, and especially over aridity gradients, supports our
initial assumption that water constraint explains a great part of LAI variability at the global scale.
However, the comparison also reveals that this constraint is not the major or unique limiting factor of
canopy development, especially for northern latitudes biomes where light and its impact on carbon
budget also plays an important role. The comparison also highlights some model weaknesses for dry
tropical forests. The explanation, supported by ground experiments reported in the litterature, lies in an
unsufficient rooting depth in the model. A third major discrepancy can be seen over China. This is
probably due to the use of potential vegetation (deciduous forest for China) map by the model, while
satellite observes the actual land use (mostly crops and grasslands).
Following this comparison of model outputs to satellite estimates, we have implemented several
improvements in the model (Lafont, 1997).
First, we have increased from 1.5 m to 4.5m the rooting depth of forest biomes with suffer significant
water stress, namely tropical evergreen forest, dry deciduous forests, savanna woodlands, and
mediterranean vegetation. This modification is supported for example by the work of Nepstad (Nepstad
et al , 1994) who report a rooting depth larger than 8 meters for evergreen tropical forest located at the
border of the amazonian rainforest.
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Second, in order to improve model results for northern latitudes biomes, we have implemented in the
model a carbon budget constraint. This mechanism limits LAI by computing the carbon cost of new
leaves. Since this cost is assumed proportional to LAI while photosynthesis tends to saturate when LAI
increases, an optimum exists that depends on local conditions. Equilibrium LAI is reached when the
annual carbon cost of building new leaves is larger than the gain of carbon produced by the
photosynthesis of additional leaves.
Figures 17 and 18 present LAI maps obtained with the previous and the new version of the model,
respectively. The carbon budget criterion reduces LAI of northern areas to more realistic values. For
example, LAI decreases from 4 to 1 m 2.m-2 over Siberia. Change of the rooting depth of several
biomes lead to an increase of LAI over several regions, such as south-east of Amazonia and southern
Africa.
APAR MJ/DAY
Figure 16 : Comparison of satellite estimate of monthly APAR (left side) to model estimates (right
side). Top : winter, Bottom :summer.
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Figure 17 : Simulation of LAI by the previous
version of the process model (Kergoat 1998).
Figure 18 : Simulation of LAI by the modified
version of the process model (Kergoat 1998).
Change of parameters and process improvements
were based on the analysis of the comparison of
model results with satellite data (fig. 16). Lafont,
1997.
Again, we used satellite data to assess LAI predicted by the new version of the model. The comparison
is based on NDVI given by the FASIR (Sellers et al, 1994, results are not shown here) and LASUR
(Berthelot et al ,1997) datasets derived from NOAA/AVHRR measurements. Modeled LAI is used to
compute NDVI according to the following simple relationship :
NDVI = NDVIinf + (NDVIs – NDVIinf).exp(-kLAI)
Where NDVIinf is NDVI for an infinite canopy, determined for each dataset over Amazonia, NDVIs is
bare soil NDVI, determined for each dataset over Sahara and australian desert, k is the light
attenuation coefficient (k=0.5).
Figure 19 : maximum yearly NDVI derived from
model LAI.
Figure 20 : maximum yearly NDVI derived from
LASUR dataset
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Figures 19 and 20 present annual maximum of NDVI derived from model LAI and LASUR reflectances,
respectively. The agreement is improved, compared to the results obtained with the previous version
of the model (figure 16). However, one must keep in mind that the way these two NDVI maps are
obtained are rather different. The model is run with long term climate average and assumes potential
vegetation, while satellite NDVI corresponds to a specific year (1989) and to actual land cover.
Figures 21 and 22 present the scatterplots of the comparison with LASUR dataset. When using the
previous version of the vegetation model, the correlation coefficient between predicted and LASUR
NDVI is 0.62 (0.63 with FASIR). It increases to 0.76 with the new version of the model (0.79 with
FASIR).
This significant increase of the correlation coefficient shows the benefit of global satellite data to
improve vegetation models. Depending on the model, further analysis could be done, for example to
assess the way models predict vegetation phenology or respond to climate fluctuations.
Figure 21 : Pixel by pixel comparison of LASUR
NDVI and model NDVI derived from discrete LAI,
using the previous version of the process model.
Figure 22 : Pixel by pixel comparison of LASUR
NDVI and model NDVI derived from discrete LAI,
using the improved version of the process model.
r=0.62, N=13000.
r=0.76, N=13000.
5.2 LPAP Model (CARAIB)
CARAIB (CARbon Assimilation In the Biosphere, Warnant et al., 1994, Nemry et al., 1996) is a global
mechanistic model of the carbon cycle in the terrestrial biosphere which was entirely developed at
LPAP. It couples various modules associated with leaf level physiology, canopy light attenuation and
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CO2 assimilation, wood respiration and soil microbial oxidation of organic matter. It is also partially
coupled with the IBM water model. The most important outputs of CARAIB are the net primary
productivity (NPP) and the net ecosystem productivity (NEP) calculated with a 1°x1° resolution in
latitude-longitude over the whole continental area. However, to achieve a formulation as mechanistic
as possible, other fluxes or vegetation characteristics such as leaf and wood respiration, soil
heterotrophic respiration (SHR) and leaf area index (LAI) are also calculated.
The leaf carbon assimilation sub-model calculates photosynthetic rates using the models proposed by
Farquhar et al. (1980) for C3 plants and Collatz et al. (1992) for C4 species. Due to the non-linearity of
this system and the large variation of climatic conditions during the day, the leaf carbon assimilation is
calculated every 2 hours. At the canopy level, the leaf area index of each vegetation class is estimated
from criteria on the monthly average temperature and soil moisture. The distribution of light within the
foliage is calculated assuming a spherical distribution of leaves and a quasi-exponential absorption of
the light inside the canopy. Wood biomass in forest vegetation is estimated on an annual average
basis from the stand age and the NPP fraction allocated to wood growth, using a simple
parameterization proposed by Esser (1991). Daily mean wood respiration is taken as proportional to
wood biomass, the proportionality factor varying as a Q10 function of the temperature integrated over
the diurnal cycle. Soil heterotrophic respiration is estimated using a formulation depending on
temperature (Q10 relationship) and soil moisture (Nemry et al., 1996). It is proportional to soil carbon
content. At the present stage of development, SHR is assumed to be in equilibrium with NPP on an
annual basis.
One specificity of CARAIB compared to other global biosphere models is that each 1°x1° degree grid
cell is subdivided into several fractions of different vegetation types, instead of considering only the
dominant type in each grid element as done in existing global vegetation maps usually adopted by
biospheric modellers. The fractional covers are estimated on every grid cell by combining the ecotypes
distribution of Wilson and Henderson-Sellers (1985) with land use data at the country level published
by the Food and Agricultural Organization (FAO, 1993).
In a first step, CARAIB has been used to simulate vegetation phenology and leaf area index over the
Hapex-Sahel area, without taking into account the vegetation carbon budget. Inputs data have been
described in section 3.1. Since the availability of water is a major limiting factor in Sahel, most of the
efforts have been devoted to check and improve the water budget model IBM. The sensitivity of LAI to
a parameter that describes water availability relatively to the field capacity has been tested. When the
model is run with parameter value used for global scale studies, the length of vegetation cycle of
Hapex-Sahel is underestimated when compared to the phenology derived from AVHRR NDVI time
series. The threshold that drives water stress has been modified in order to account for the adaptation
of sahelian vegetation to dry conditions. In that case, phenology is well reproduced, but the maximum
LAI predicted by the model is to high (LAI=4.5) compared to ground measurements available in the
HSIS data base (and more qualitatively to NDVI). We are now investigating the reasons of this
discrepancy. A possible explanation could be the effect of an inadequate description of vegetation
cover (assumed to be 1) and distribution (proportion of grass, bare soils, bush and trees). In addition,
the relative contribution of soil and canopy to evapotranspiration has probably to be improved, by a
better coupling of carbon and water budgets. In order to achieve a more quantitative use of satellite
data to test the model, the SAIL model is being implemented to predict reflectances and NDVI from
simulated LAI.
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NOAA/AVHRR data have also been used to asses model results obtained over the australian NATT 1
transect located in the Northern Territory. The study is based on climate (PIK), satellite (CSIRO) and
land use and soil data (CSIRO) available for the period 1980 to 1989, with a 10 days sampling or
average. Modeled fPAR have been compared to satellite estimates, using average of 10 days period
over ten years. This comparison indicated a too large dynamics of vegetation canopy, which was
attributed to a too high value of the so-called water threshold. In the CARAIB model, this threshold
defines the level of sustainable water stress. Water threshold was increased by a factor of two in order
to get a more realistic simulation of vegetation growth. This change lead to a decrease of productivity
by a factor of two.
1
NATT: Northern Australia Tropical Transect
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6. ASSIMILATION OF REMOTELY SENSED MEASUREMENTS
(Note : the unpublished results presented in this section have been obtained by P. Cayrol in the
framework of her PhD thesis)
6.1 Introduction
Measurements acquired by Earth observing satellites provide global and consistent observations with
high temporal and spatial sampling. Meteorological satellites as well as sensors dedicated to ocean
topography monitoring, such as TOPEX/POSEIDON, are increasingly used to supply unique
information that feed atmospheric and ocean models through assimilation procedures. We believe that
satellite data will play in the future a similar role for biosphere studies and land monitoring. However,
several reasons such as a wide range of issues and scientific objectives have delayed the
development of a consistent combination of models and satellite observations for ecological research
and land ressource management. The lack of satellite sensor dedicated to biosphere observation and
the difficulty to make direct remote measurements of the variables of interest are two other major
reasons for this delay.
New sensors aimed at the observation of land surfaces and vegetation have been recently launched
(e.g. SPOT-4 VEGETATION and POLDER on ADEOS) or will be available in a near future (e.g.
MODIS and MISR onboard EOS, MERIS and ATSR onboard ENVISAT, SEVIRI on METEOSAT
second generation). There is therefore a need to develop new techniques that maximize the benefit of
these new sensors.
The approach developed by CESBIO team is based on the assimilation of satellite data within process
models. Assimilation is an inverse method that consists in controlling the time evolution of a vegetation
model with radiometric observations, by modifying parameters, state variables or input data. This latter
choice depends on the goal and on the variables which suffer the largest uncertainties. Two main
reasons justify this approach :

satellite measurements over land provide only spectral radiances that are not directly linked to the
variables, such as soil moisture or Leaf Area Index (LAI), that drive or characterize vegetation
functioning. The assimilation of satellite data within vegetation functioning models is probably the
most consistent way to solve this issue.

The use of satellite data alone, without coupling with process model, does not allow to build
prognostic models and lacks the needed robustness, simply because satellite measurements are
not always available, for example because of cloud cover.
The question is to evaluate if the information contained in radiometric measurements can be used to
determine non-radiometric parameters, such as photosynthates partitioning coefficients. As the
objective is the spatial extension, the model must be simple and general enough.
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6.2 Material and method
Our work relies on ground-level, airborne and satellite datasets acquired during the Hapex-Sahel
experiment held near Niamey (Niger) in 1992. We used the data available in the Hapex Sahel
Information System (HSIS) database. In a first step, the focus was on the so-called perennial fallow
vegetation type, that is grassland.
The global vegetation process model developed by Kergoat (1998) has been first adapted to simulate
the growth and senescence of grasslands in arid conditions. Then we coupled this vegetation process
model with the Soil-Vegetation-Amosphere Tranfer Scheme (SVATs) developed by Chehbouni (1992,
see also Lo Seen et al., 1997) for use in arid and semi-arid regions. The vegetation model is driven by
weather data and predicts vegetation state variables (e.g. LAI, biomass). The SVAT model uses these
variables to calculate surface resistance and latent and sensible heat fluxes. The resulting water fluxes
allow to update soil moisture and therefore the water available for plant transpiration in the next step of
vegetation model computations. Both models operate at a hourly time step in order to account for nonlinear processes, such as diurnal variation of stomatal resistance.
Conventional approaches attempt to retrieve some vegetation parameters or state variables (for
example leaf area index) by inverting the instantaneous satellite signal without using the knowledge
incorporated in a vegetation model and the information of long (several months) time series. In our
approach, the vegetation and SVAT models are coupled with radiative transfer models that allow to
predict shortwave and longwave radiances measured by ground level radiometers or satellite sensors.
We use optimization techniques (so-called data assimilation) to retrieve the set of parameters which
minimizes the difference between predicted and observed reflectances over a given time period (e.g. a
vegetation seasonal cycle). The main advantage of this technique is to allow the retrieval of model
parameters which cannot be observed from space. The aim is to map model parameters that allow the
best agreement between predicted and actual satellite observations. This best agreement is expected
to lead to better model outputs in terms of, for example, LAI, photosynthesis and productivity.
6.3 Results
6.3.1 Assimilation of shortwave data
The approach was first applied to radiances measured at ground level. The results were satisfying.
They are not presented here, the focus being on the assimilation of satellite data.
In a first step, we used only visible and near-infrared measurements acquired by NOAA-AVHRR over
three sites of the Hapex-Sahel experiment. NDVIs were computed from both satellite and model
shortwave reflectances for the whole rainy season (may-october 1992). In order to account for land
cover heterogeneity, a land cover map derived from SPOT data was used to perform the so-called
spectral unmixing of the satellite measurements (figs. 23 and 24). This method, described for example
by Puyou et al. (1994) and Faivre and Fischer (1994), allows to retrieve the temporal evolution of
reflectances of the main land use types.
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Figure 23 : atmospherically corrected NDVI derived
from daily NOAA/AVHRR data. Dotted line
corresponds to the whole set of 1 km resolution
NDVI. Stars correspond to NDVI for fallow land,
obtained after cloud screening and spectral
unmixing.
Figure 24 : atmospherically corrected NDVI derived
from daily NOAA/AVHRR data. Dotted line
corresponds to the whole set of 1 km resolution
NDVI. Stars correspond to NDVI for millet, obtained
after cloud screening and spectral unmixing.
Over each site, the assimilation was performed with « unmixed » NDVI derived from 1x1 km 2 data. Our
initial plan was to use data assimilation to adjust the carbon allocation coefficient for grassland. This
parameter drives the partitioning of photosynthesis products between roots and above-ground organs
such as leaves and stems. This parameter is difficult to measure and has a strong impact on LAI
development. However, first trials revealed that is was difficult to separate the impact on LAI (and
therefore NDVI) of six parameters that have similar impact on growth (Table 1). Therefore, we adjust
these parameters simultaneously through a weighting coefficient, . This approach may not be fully
satisfactory since it assumes that the various parameters are correlated. More independant information
and observations would be needed to adjust these parameters separately.
Parameters
First guess values
Adjusted Values
Ground
Satellite
Shoots respiration yield
0.6
0.56
0.57
Allocation to roots
dimensionless
0.56
0.63
0.60
23
27
26
Mortality coefficient
day-1
0.01
0.009
0.009
:quantum yield
mol(CO2) mol-1(PAR)
0.044
0.049
0.047
0.00369
0.0045
0.0042
SLA:Specific Leaf Area
m2 kg-1 (C)
Initial storage
kg m-2
Table 1 : List and values of parameters adjusted through the assimilation of shortwave measurements
(NDVI). These parameters are calibrated simultaneously through a weighting coefficient, .
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The weighting coefficient, , was adjusted by minimizing the distance between observed (i.e satellite)
and predicted (i.e. modeled) NDVI. The minimization was applied to all the clear sky days of the rainy
season. Root mean square error and correlation distance criteria have been tested. Table 1 presents
first guess and adjusted values of parameters. These values are realistics, and results obtained with
ground level and satellite data are very similar. Figure 25 shows model simulation of LAI obtained with
first guess and adjusted parameters, respectively. The differences between first guess and adjusted
parameters are rather weak, but lead to significant change of modeled LAI (fig.25). This illustrates the
sensitivity of LAI modeling to growth parameters. Consequently satellite shortwave measurements,
which strongly depend on LAI, provide an efficient way to retrieve growth parameters.
Figure 25 : Assimilation of 1x1km satellite NDVI to adjust growth parameters of fallow grassland over
the central west site of Hapex-Sahel experiment, Niger, 1992. Model simulation of LAI obtained with
first guess (dotted line) and adjusted (solid line) parameters, respectively, and ground measurements
of LAI (*).
Sensitivity studies we performed showed that surface fluxes are highly sensitive to LAI, at least in arid
regions. This is illustrated by figures 26 and 27 which present time profiles of latent heat flux before
and after adjustement of growth parameters. Some discrepancies still exist at the end of the rainy
season, and could be attributed to a poor description of senescence processes. Also, the accuracy of
the ground measurements used to estimate latent heat with the Bowen ratio method decreases when
soil moisture is low. However, these results show that improvement of LAI simulation lead to a better
estimate of latent heat flux, with r.m.s. errors decreasing from 75 W.m -2 to 60 W.m-2.
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Figure 26 : Model simulation of surface latent heat
flux at 14:00 UTC obtained with LAI predicted with
first guess (dotted line) and adjusted (solid line)
growth parameters, respectively.
Figure 27 : Model simulation of daily surface latent
heat flux obtained with LAI predicted with first guess
(dotted line) and adjusted (solid line) growth
parameters, respectively.
6.3.2 Assimilation of longwave data
6.3.2.1 Assimilation of ground level measurements
Assimilation of thermal infrared (TIR) data is a rather new topic, and we first explored it with ground
measurements of brightness temperature.
In a first step, we minimized the distance between the brightness temperatures predicted by the
coupled vegetation-SVAT models and the measured temperatures. We did not attempt to perform
simultaneous assimilation of short- and longwave measurements.
In arid regions, vegetation cover and LAI are strong drivers of surface temperature. Consequently, we
used the assimilation of thermal infrared data to adjust vegetation growth parameters. Results (not
shown here) were only slightly less satisfactory than the ones obtained with ground level shortwave
data. However, the main interest of longwave data should be to complement shortwave
measurements. Therefore we used them to adjust the coefficients of the soil resistance
parameterization, which plays a key role on surface energy budget but is poorly known :
rss = a(wsat-wg)-b
where rss is the surface resistance, wsat the saturated volumetric water content (m 3m-3), and wg the
volumetric content water at the surface. The initial values of a and b coefficients were 7114 and 1515,
respectively. Their adjustement with TIR assimilation lead to new values, respectively 11550 and 2543.
Figures 28 and 29 present modeled and observed surface brightness temperatures. In order to be as
close as possible to the characteristics of satellite observations, we only considered one measurement
per day, at 14 :00 UTC. Assimilation of TIR data has a small but significant impact on temperatures
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predicted after adjustement of surface resistance. This small impact is probably due to the fact that we
used growth parameters determined with shortwave assimilation, i.e the model was already tuned
against observations. From this experiment, we learned that the coupled vegetation-SVAT models
allow to predict both LAI and brightness temperature time course, a result which was not certain. We
also identified a possible difficulty for further development of the approach which results from the high
spatial variability of surface temperatures. Temperatures were measured over four different but close
targets. Depending on the target, temperatures can differ by 5°C to 10 °C (figure 30). In the
experiment we described above this difficulty was successfully solved by using the average of these
four temperatures.
Figure 28: Comparison to ground measurements of
surface brightness temperatures predicted by the
coupled models. Period : may and october 1992,
14:00 UTC.
Figure 29 : Ground measurements (green solid
line) and model simulation of surface brightness
temperatures obtained with first guess (black dotted
line) and adjusted (red, solid line) surface
resistance, respectively.
Figure 30 : time profile of brightness temperatures
measured over four different but close targets
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6.3.2.2 Assimilation of satellite measurements
We used 1km resolution NOAA/AVHRR thermal infrared data available in the Hapex-Sahel database.
These data have been corrected for atmospheric emissivity effects according to the split-window
approach of Kerr et al. (1992). Comparisons of modeled and NOAA/AVHRR surface brightness
temperature are shown in figures 31 and 32 for two test sites. The model used to predict temperature
was run with the growth parameters adjusted with shortwave data (section 6.3.1), and no further
adjustement was done. Model and satellite temperatures exhibit rather similar seasonal variations, but
differ in their absolute value by several degrees. No definite explanation of this discrepancy has been
found yet. We believe that land surface heterogeneity and inaccurate modeling of radiative transfer in
the thermal infrared domain are the best candidates to explain this discrepancy.
We tried to perform the assimilation of TIR satellite data within the model in order to adjust surface
resistance parameters. When the distance used is based on r.m.s.e criterion, the results are not
realistic. The correlation distance criteria lead to more realistic adjustement but does not improve
significantly model outputs.
Figure 31 : time evolution of model and
NOAA/AVHRR surface brightness temperatures.
Surface air temperature is given for reference.
Hapex-Sahel Western site, 1992.
Figure 32 : time evolution of model and
NOAA/AVHRR surface brightness temperatures.
Surface air temperature is given for reference.
Hapex-Sahel Eastern site, 1992.
As a preliminary conclusion, we have shown that the assimilation of shortwave remotely sensed
measurements allows to improve the outputs, such as LAI and evapotranspiration, of a coupled
vegetation-SVAT model. In addition, this coupled model predicts realistic surface brightness
temperatures at the local scale. However, but the assimilation of TIR satellite data deserves further
work, especially to account for surface heterogeneity.
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7. IMPACT OF DOUBLE CO2 ON CLIMATE AND VEGETATION
Forewords : This section addresses the simulation of vegetation functioning in the future. Stricly
speacking, this issue is not a part of our VEGETATION investigation. However, we believe it opens this
report to scientific questions that partly motivated the work described in the previous sections.
Since carbon dioxide is crucial to vegetation functioning, increased CO2 concentration is expected to
deeply modify water and carbon exchanges at the Earth surface. A number of CO 2 enrichment
experiments have shown a significant decrease of leave stomatal conductance, often of the order of
40% (Field et al. 1995, Drake et al. 1997). Simulteanously, leaf photosynthesis and biomass production
increase, a phenomenon called the fertilizing effect of CO 2. It is difficult to extrapolate short term plant
experiments to the response of whole ecosystems on the long term. However, it is important to test
various assumptions in order to better understand the possible impact of a modified vegetation
functioning on water and carbon cycles and energy fluxes.
Most of the few published works dealing with this topic (e.g. Sellers et al.,1996) assume that vegetation
under a double CO2 climate will have the same structure (e.g. LAI) than at present. However, one may
expect that changes in plant physiology will combine with climate change and lead to change in
vegetation structure (Betts et al., 1997).
We have studied the response of vegetation to a doubling of atmospheric CO 2 concentration. The
process model we developed (Kergoat, 1998) was driven by 2xCO 2 climate simulation produced by the
Meteo France atmospheric model (ARPEGE, Douville et al., 1998). Leaf area index and water and
carbon exchanges at the global scale were predicted.
The vegetation process model used to predict LAI and vegetation functioning is the model we improved
using satellite data. For every grid-cell, water and carbon balances are computed for a range of LAI.
The iteration stops when one of the following conditions limits growth.

C1 : Water stress for mean climatic conditions becomes critical (e.g. Woodward 1987). An
optimum can be usually found since evapotranspiration increases with LAI .

C2 : The annual carbon cost of building new leaves is larger than the gain of carbon produced by
the photosynthesis of additional leaves. Since the carbon cost of new leaves is assumed
proportional to LAI while photosynthesis tends to saturate when LAI increases, an optimum exists
that depends on local conditions.

C3 : Lower leaf layers receive an unsufficient amount of solar radiation to get a positive carbon
balance. This could be a mechanism explaining limitation of LAI in tropical rainforests.
The C2 criterion was added following the comparison of model results to satellite data (section 5.1)
The result is the maximum LAI permitted by local ressources.
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The climate model and double CO2 simulations are described in Douville et al. (1997). Climate model
outputs drive vegetation model in off-line mode. We performed four simulations of LAI, namely :

« CLIM » is the reference simulation for current climate conditions. Inputs are monthly
climatologies, averaged over 30 years (IGBP-GAIM intercomparison dataset).

« RAD » is the LAI simulation driven by the double CO2 climate, described in terms of climate
anomalies. Only the impact of the physical climate on LAI is considered, that is the impact of
variables such as temperature, precipitation, net radiation, and water vapor pressure deficit.

« STOM » is the LAI simulation that assumes that leaf stomatal conductance decreases by 34%
under double CO2 concentration (Field et al., 1995). Climate forcing is the same as for the « RAD »
experiment.

« FERT » considers an increase of 25 % of photosynthesis (Drake et al. 1997), in addition to the
previous decrease of leaf stomatal conductance. The increase is applied to photosynthesis rate
both under saturating light (maximum assimilation) and at low irradiance (quantum yield). Climate
forcing is the same as for the « RAD » experiment. For deciduous biomes, the vegetative period is
allowed to change, according to temporal shifts or changes in air temperature or precipitations.
7.1 Results : LAI simulation
Figure 33 maps the regions where the various limiting factors operate. The carbon budget criterion
(C2) limits LAI for high latitude biomes such as tundra and boreal forest. Leaf shadowing (C3) plays the
major role to limit LAI of tropical rainforests and of some temperate forests. As expected, the water
stress (C1) is dominant for all arid climates, including hot or cold continental climates.
Figure 33 : map of dominant limiting factors. Gray : water stress (C1), black : carbon budget
(C2), white : leaf shadowing (C3).
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Figure 34 present differences between LAI of the CLIM reference simulation and LAI obtained with
RAD, STOM and FERT simulations, repectively.
When only the impact of physical climate (RAD simulation) is taken into account (fig.34, top), LAI
increases at high latitudes. This increase of LAI under 2xCO 2 climate results of air temperature
increase which favors photosynthesis and therefore improves the annual carbon budget and the
amount of carbon available for leaf production. LAI slightly decreases over the southern part of
Eurasia. This decrease results of the enhancement of potential evapotranspiration or of precipitation
anomalies, leading to enhanced water stress. In tropical regions, LAI changes do not exhibit a unique
trend.
In the STOM experiment (fig. 34, middle), the main effect of reduced stomatal conductance is to
compensate in most regions the impact of a warmer climate on water budget. As a consequence, most
of LAI decreases obtained in the RAD experiment disappear in the STOM simulation.
Combining CO2 fertilizing effect with reduced stomatal conductance (FERT experiment, fig. 34, bottom)
leads to a general increase of LAI. This applies to biomes limited by the carbon budget criterion, mainly
located in boreal and temperate latitudes, but also to tropical rainforest where the leaf shadowing effect
is prevailing. In addition a positive feedback of LAI on photosynthesis exists. Over Alaska for example,
the 25% increase of leaf photosynthesis efficiency, combined with reduced stomatal conductance,
leads to an 50% increase of LAI and a final 80% increase of canopy photosynthesis.
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Figure 34 : differences between LAI of the CLIM reference simulation and LAI obtained with RAD (top),
STOM (middle) and FERT (bottom) simulations, repectively.
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7.2 Results : Evapotranspiration simulation
When only the physical variables are taken into account, a warmer climate (RAD simulation) leads to
increased evapotranspiration, ETR (fig. 35). The stomatal closure we assumed in the STOM simulation
compensates this increase, and the latitudinal profile of ETR is rather similar to the ETR profile of
current climate conditions (CLIM).
In the FERT simulation, the increase of LAI due to the fertilization effect of CO 2 corresponds to an
increase of evaporative surfaces that counteracts stomatal closure. As a result, ETR of boreal and
temperate biomes slightly increases. However, increased LAI does not compensate stomatal closure
effect in tropical rainforests.
Figure 35 : Impact of climate and physiological processes on land evapotranspiration relative to
current climate (CLIM simulation). Impact of physical climate change (RAD minus CLIM), stomatal
closure (STOM minus CLIM), and of fertilization plus stomatal closure (FERT minus CLIM).
Even if the magnitude of the effects are slightly different, results of the FERT simulation corroborates
the ones obtained by Betts et al. (1997) who also considered changes of vegetation structure under
2xCO2 climate. On the contrary, our results differ from the ones presented by Sellers et al. (1996) who
did not take into account LAI response to a modified climate.
The simulation of LAI under a 2xCO2 climate will be improved to account for possible nitrogen
limitations. However no clear effect of 2xCO2 climate on nitrogen cycle has been established yet. The
results we obtained are probably in the upper range of vegetation response to climate change when
physiological effects are accounted for. On the long term, biome dynamics and human impacts should
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be taken into account. However, this experiment clearly shows that vegetation functioning has to be
better understood to predict the impact of increased CO 2 concentration, since even the sign of water
and carbon flux changes depends on vegetation processes that are considered.
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8. WORKPLAN FOR THE POST-LAUNCH PHASE
During the post-launch phase, we plan to use VEGETATION data to address the four topics described
below. Some of them will not be completed within the rather short duration of the post-launch phase.
Our short-term objective will be to obtain significant results demonstrating the capabilities and benefits
of the VEGETATION system.
8.1 Satellite data to improve vegetation process models
Our aim is to improve the results of vegetation process models by tuning model parameters against
satellite data.
Assimilation techniques developed over the Hapex-Sahel area will be tested and further developed
over the SALSA experiment area, located at the border between Mexico and the USA (fig.38). Ground,
airborne and satellite measurements started in 1997 and will continue at least until the end of 1999.
This site will also serve the validation of EOS algorithms. The experimental plan defined by ORSTOM
and CESBIO for the mexican part of the site was specifically dedicated to the study of upscaling issues
and the development of multispectral assimilation within coupled vegetation and SVAT models.
Among the various measurements performed over the SALSA area, the ones of the Large Aperture
Scintillometer (LAS) are of great interest for our research. LAS provides estimate of areally averaged
sensible heat flux. Figure 36 and 37 compare LAS heat flux to that obtained using an eddy correlation
(EC) method over 300 and 900 m pathlengths, respectively (Chehbouni et al., 1998). Over the 300m
pathlength LAS and the EC are integrating the same surface, which is no more the case with the 900
m pathlength. Therefore, LAS measurements will be usefull to test results of satellite data assimilation
at 1x1km resolution within coupled models.
Figure 36 : comparison between sensible heat
flux measured by eddy correlation method (Solent)
and that measured by the scintillometer (LAS) at a
pathlength of 300 m (Chehbouni et al., 1998). r2=
0.90.
Figure 37 : comparison between sensible heat
flux measured by eddy correlation method (Solent)
and that measured by the scintillometer (LAS) at a
pathlength of 900 m (Chehbouni et al., 1998). r2=
0.75.
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We plan to use both VEGETATION and NOAA/AVHRR data, in order i) to compare the respective
benefit of the two sensors ii) to evaluate the interest of thermal infrared channels. The expected output
of the research is a robust method allowing estimation of vegetation productivity, water balance and
surface fluxes.
We also plan to test assimilation techniques over the Hapex-Sahel area in order to check the method
in different conditions. The Hapex-Sahel is more heterogeneous than the SALSA site, climate and
vegetation gradient are different, and atmospheric turbidity is higher. This study will be performed in
close collaboration with the participants of the investigation leaded by A. Bégué (CIRAD) and entitled
« Application of VEGETATION data to ressource management in arid and semi-arid rangelands ».
We also wish to acquire VEGETATION data over France and Northern Spain. These data will serve
several ongoing activities and projects linked to the current investigation, such as :

Crop growth monitoring and water ressources assessment for crop irrigation in the Ebre watershed
(Saragosse area, Spain)

monitoring of the phenology of french deciduous forests and modeling of forest functioning. This is
a continuation of the work partly presented in the previous report.
We also contemplate to submit proposals to the fifth framework programme of the EC. These
proposals will address water ressources and vegetation productivity. The VEGETATION datasets we
request, especially over France and Spain, will be used for preparatory work and will also serve as
historical reference.
Figure 38 : approximate location of the northern hemisphere study sites of the post-launch phase
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8.2 Assessment of carbon budget at the regional scale
In the framework of the EUROSIBERIAN CARBONFLUX project, theTURC diagnostic model we have
developed will be used to estimate net primary productivity over two sites located in Russia (fig. 38).
EUROSIBERIAN CARBONFLUX is coordinated by M. Heimann and supported by the Environnement
and Climate Programme of the European Commission. “EUROSIBERIAN CARBONFLUX” is a
feasibility study for the development of an observing system for the quantification of regional (1-2000
km) and continental scale CO2 and other long-lived biogeochemical trace gas fluxes over several
years. The target region of EUROSIBERIAN-CARBONFLUX is eastern Europe and north-western
Siberia. Amongst the various tasks of the project, remote sensing measurements and NPP model
driven by VEGETATION data will be used to provide boundary conditions of a hierarchy of nested
three-dimensional atmospheric models of varying horizontal and vertical resolution. These models will
be used to relate atmospheric observations to surface fluxes on different spatial and temporal scales
using forward and inverse modeling techniques. Using these tools, an optimal, cost-effective sampling
strategy is to be determined which allows the quantification of regional scale surface fluxes on time
scales of days to several years.
8.3 Adaptation of vegetation to climate
The process model developed by Kergoat (1998) is based on several assumptions on the way plants
adapt to climate. There is a need to test this model in a more detailed manner and to incorporate more
processes. For example, we assume that LAI develops in order to maximize the use of ressources
such as light and water. However, some studies have shown that a sub-optimal adaptation might be
more efficient on the long-term to get over extreme weather fluctuations.
We plan to perform this study over the whole Australian continent. Satellite data archive and a unique
dataset on vegetation, soil and weather have been collected by australian colleagues. In addition,
Australia has a large variety of biomes which periodically suffer from drought induced by El-Niño
events. The study corresponds to a step in difficulty compared to our previous work on the model. We
expect that VEGETATION data will provide more accurate data than existing sensors.
VEGETATION dataset over Australia will also drive the TURC diagnostic model. The environmental
conditions and availability of surface datasets we already mentionned will allow a detailled analysis of
the results.
8.4 Global carbon cycle and change monitoring
We wish to acquire global VEGETATION dataset in order to perform global Net Primary Productivity
estimates, as a continuation of the work already performed with NOAA/AVHRR measurements in the
framework of the ESCOBA-Biosphere project on global carbon cycle. NPP will be estimated with the
TURC diagnostic model.
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This global dataset will also be used to monitor possible change of vegetation functioning on the long
term, i.e. back to the mid 80’s.This requires to make NOAA and VEGETATION archives consistent in
terms of geometry, radiometry and spectral signatures.
8.5 Product request
Apart from the topics described above, we also plan to work on radiometric correction algorithms, for
example atmospheric corrections using the B0 band. Therefore, the products needed for topics 8.1
and 8.2 are daily P product. Over Australia (topic 8.3), we wish to receive S.1 products, plus daily P
products over limited areas.
For global scale study, our preliminary choice is S10.8 product.
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9. CONCLUSION AND PERSPECTIVES
During the pre-launch phase of the project, we addressed the general topic of the use of
VEGETATION measurements to improve the modeling of biosphere functioning. At the regional
scales, the aims are the assessment of biomass production and possibly water balance. The aim of
global scale studies is related to global change and global carbon cycle issues.
We explored, and presented in this report, three different directions for coupling biosphere models and
remotely sensed measurements, namely :

Test of vegetation model results : model outputs are compared to satellite observations. We
presented examples where the analysis of discrepancies provided a efficient guide to improve
process modeling or model parameters.

Driving of vegetation model : the TURC diagnostic model uses NDVI as input to determine
absorption of solar radiation from which net primary productivity is estimated. Satellite NDVI put
strong constraints on the model. By now, this approach is probably the more robust to estimate
vegetation production. As we shown with some examples, the capabilities of this approach to
detect year to year fluctuations of NPP are quite promissing. However, long term analysis of NPP
is limited by the quality and consistency of the measurements provided by current satellite sensors.

Assimilation techniques : this approach is based on optimization techniques that compare the
distance between model outputs and satellite observations. It allows a fine tuning of model
parameters, even if these parameters cannot be observed directly from space. Results we
obtained with shortwave measurements are quite promising and open the path to regional
assessment of vegetation functioning. The benefit of thermal infrared measurements is more
difficult to assess and requires further work.
As explained in section 8, the post-launch phase will mainly consist in the evaluation of the benefit
gained from the use of VEGETATION data for vegetation modeling. We will continue to explore the
three directions explained above, with a special emphasis on the development of assimilation
techniques in the framework of the SALSA and EUROSIBERIAN CARBONFLUX experiments.
We anticipate that VEGETATION data will benefit to the development of terrestrial biosphere model in
two different ways :

the first benefit we expect relates to the service that will be provided by the VEGETATION system.
It is currently difficult to get consistent datasets at 1km resolution over the studied areas (Sahel,
France, Australia, ...). One may note that we have had to gather AVHRR datasets coming from
various sources, depending of the site and time period. Geometrical and radiometric
preprocessings, including compositing and filtering, of the data are not homogeneous, that leads to
additional work or difficulties to calibrate models.
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
The second benefit we expect concerns the quality of VEGETATION data which is needed for
assimilation procedures and to study long term phenomenon such as the carbon cycle and
ecosystem dynamics. Currently, the development of most studies is difficult because of several
problems related to AVHRR, mainly orbital drift and poor geometry, expecially for global products.
Large AVHRR pixel size at large scan angles is a severe limitation to study small or heterogeneous
areas. These issues are difficult to assess quantitatively. However, we can say that, for example,
the analysis of global NPP for several years has been limited by the amount of work that would be
needed to reprocess AVHRR data at the needed level of quality, assuming this quality can be
reached.

The third benefit is the availability, on the same platform, of high spatial resolution and high
temporal repetitivity sensors. As explained in section 6, both type of data are needed for the
assimilation of satellite measurements within vegetation process models.
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10. REFERENCES
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Field CB Jackson RB and Mooney HA, 1995, Stomatal response to increased CO2 : implication from the leaf to
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Moulin, S., A. Fischer, and G. Dedieu, Assimilation of short wavelength remote sensing observations within a
crop model: methodological development and interannual field scale study, in ISPRS Photosynthesis and
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Biogeochemical Cycles, 10, 269-285 .
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July 1998
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STEM-VGT: Satellite Measurements and Terrestrial Ecosystem Modelling using VEGETATION instrument
Prelaunch Phase Final Report
10.2 Published works that use LASUR dataset
Gobron, N., B. Pinty, and M. M. Verstraete (1997) `A New Approach for the Characterization of Land Surfaces',
American Geophysical Union Fall Meeting}, {\bf AGU}, San Francisco, California, USA, December 812.
Gobron, N., B. Pinty, Verstraete, M. M., and Y. Govaerts (1997) `Presentation and Application of an Advanced
Model for the Scattering of Light by Vegetation in the Solar Domain', in 7th International Symposium
Physical Measurements and Signature in Remote Sensing. Courchevel, France, 7-11 avril 1997, edited
by G. Guyot, 1997., in 7th International Symposium Physical Measurements and Signature in Remote
Sensing, 7-11 avril 1997, edited by G. Guyot, and T. Phulpin, pp. 267-273, A.A. Balkema, Rotterdam,
Netherlands, Courchevel, France.
Gobron, N. : Caractérisation des surfaces terrestres par télédétection spatiale à partir de méthodes physiques
avancées. Thèse de l’Université Blaise Pascal, UFR de Recherche Scientifique et Technique, Numéro
d’Ordre : D.U. 906, N°140, S.P.I. 97.101, 29 mai 1997.
Knorr, W., N. Gobron, Ph. Martin, B. Pinty, M. M. Verstraete, and G. Dedieu (1995) `Constraining a Climate
Driven Vegetation Model with Satellite Data', in Proceedings of the International Colloquium and Remote
Sensing, Edited by G. Guyot, Montpellier, France, 28-30 August, 269-279.
Ouaidrari, H., E. Vermote, N. El Saleous, and D. Roy, 1997, AVHRR Pathfinder II data set: evaluation and
improvements, in 7th International Symposium Physical Measurements and Signature in Remote Sensing.
Courchevel, France, 7-11 avril 1997, edited by G. Guyot, 1997., in 7th International Symposium Physical
Measurements and Signature in Remote Sensing, 7-11 avril 1997, edited by G. Guyot, and T. Phulpin,
pp. 131-137, A.A. Balkema, Rotterdam, Netherlands, Courchevel, France.
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