973
NOAA/AVHRR NDVI over 128 reserves in Africa to validate the spatial variations of LAI values simulated by
an ecological concept of equilibrium between precipitation and canopy structure (Woodward and McKee, 1991).
2.3 Modeling the functioning for each kind of ecosystem.
The gener alization of FOREST-BGC to all kinds of biomes was made possible by the establishment of a specific
par ame ter set for each functional type (BIOME-BGC, Running and Hunt, 1993). There is no phonological model
in the present versimi, which means that, for non-evergreen canopies, the time profile of the LAI, as well as its
value during the active period must be prescribed. The simulations made by that model have shown that the
various outputs (fluxes, NPP...) were extremely sensitive to LAI, which is a major characteristic of the canopy.
One solution could be to use temporal series of satellite data in die short wavelengths which appear to be an
efficient tool to monitor vegetatimi phenology, as we are gcnng to discuss.
231. Characterisation of vegetation cycle for African savannahs. GVI time series were used to map
informatimi about the seasonal activity of savannahs, i.e. the phonological cycle characterized by length and
be ginning of active period. At a given time, a Markovian model of radiometric behavior gives the probability for
the canopy to be in the following states: dormancy, growth, and senescence depending cm the kind of ecosystem
(Viovy and S aint, 1993) This results in an a priori knowledge of what the GVI temporal profile could be. in a
stochastic sense, allowing uncertainties in the description of the vegetarian behavior. The temporal GVI profile
can also carry some informatimi not directly related to the biological activity of the canopy: remaining noise, soil
influence... The combination of the Markovian model with the satellite signal has provided better confidence in
the phenological cycles obtained by the indicatimi of “what the most probable temporal profile” should be. In a
second step, the same GVI time series over savannahs were coupled to the “qualitative” functional model of
Fomès and Blasco (1988). This model simulates the temporal behavior of the canopy activity for several classes
of sav annah as a function of the distributi cm of the precipitatimi during the previous monthes. This model was
applied to the FAO vegetatimi map, and the comparison of the simulated profiles with the GVI data has allowed a
control of the vegetation map, with correction of the classification in some places to improve the consistency
between simulation and observations (Viovy and Saint, 1991).
These results show that it is very interesting to use an a priori knowledge about the temporal
radiometric response of the surface. After these qualitative descriptions, we want to discuss the modeling of the
seasonal behavior in a quantitative way. Clearly, what we expect from a functional model, is: a) the modeling of
the exchanges depending on the state of the canopy, b) the modeling of the changes in the state of the vegetation
itself and their links with the previous exchanges and the internal development. Among the various processes
which must be taken into account to correctly describe the functioning, the phenology and the allocatimi of
assimilates to the different organs are poorly known mechanisms. Depending cm the ecosystems, snowmelt,
temperature or soil water availability drives the phenological calendar, which controls in part the partitimi of
assimilates between above-ground biomass, roots or storage pool. If we want a correct carbon budget, for
example .at the adequate time step, an improvement of the modeling of these processes is requiered
232 A regional Sahelian grassland model. Lo Seen et al. (1994) describe an example of a regional ecosystem
process model able to simulate fluxes, growth and senescence at a daily time step far annual Sahelian grasslands.
The ecological model has been validated with 15 years of biomass measurements in two regions and appears to
reproduce the interannnal variations in the production as well as the spatial ones. Some specific daily outputs of
this model are the LAI and the vegetatimi cover fraction. These two variables allow the coupling with the
radiative transfer model SAIL (Verhoef, 1984) to simulate the temporal profile of the visible and near infrared
reflectances (and then the NDVI) over the canopy. These simulated radiometric temporal profiles are in good
accordance with vegetatimi indices derived from NOAA/AVHRR data after adequate calibration and atmospheric
corrections.
The ability of an ecological model, coupled to a radiative transfer model, to reproduce the
radiometric observations, suggests that these observations could be used to control some pertinent parameters of
the ecological model if die info rmati on required to drive the temporal feature of the canopy development are
missing. Agronoms have already tested the use of temporal radiometric measurements over crop fields in
complementarity with crop production models in order to improve the outputs of interest, especially the yield
estimation. They are available for major crops, thanks to the great amount of knowledge for these species. We
present same examples of the use of satellite data with crop models in the next section.
