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Mesures physiques et signatures en télédétection

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LERTS, CNES-CNRS, 18 Av E.Belin, bpi 2801, F-31055 Toulouse cedex, France
The current concern about the global carbon cycle and, more generally, interest in ecosystem functioning
encourage the development of prognostic process-based vegetation models. Satellite measurements provide a
unique source of observations when regional to global spatial scales are considered. These data can be used in
different ways. We focus, in this paper, on a control strategy: The model trajectory is constrained by temporal
series of remote sensing data. We present a generic vegetation model designed for this assimilation scheme. A
synthetic experiment for a deciduous-type forest simulation shows that phenological behavior and allocation of
carbon to the leaves can be controlled by visible and near infrared reflectances, and that carbon fluxes
estimation can thus be improved. Finally, we point out the interests and requirements of this combined use of
prognostic model and remote sensing measurements, and discuss the application to natural vegetation at the
global scale.
KEYWORDS : vegetation model - global - assimilation - phenology - carbon cycle
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Assessing the contribution of the terrestrial biosphere to the global carbon cycle has become a major concern,
in a global change context (direct effect of [C02] increase and possible related climatic changes). At the
global scale, validation of the carbon fluxes estimations is non trivial: One possibility is to use atmospheric
CO2 measurements, available from current stations network, to compare modelled net surface fluxes and these
measurements, through an atmospheric transport model. This requires an accurate spatial and temporal
description of the surface CO2 sources and sinks, with emphasis on seasonal and interannual variations.
Several studies have demonstrated that AVHRR times series allow the monitoring of the vegetation, at regional
to global scales. Fung et al. 1987 used empirical relationships between NDVI time profiles and photosynthesis,
to distribute, in time, annual NPP estimates. This results in satisfying simulations of atmospheric [CO2]
seasonal oscillations. Various authors also applied Monteith's model (Monteith 1972) and derived Absorbed
Photosynthetically Active Radiation ( APAR ), from the global weekly AVHRR1NDVI archive and incident solar
radiation. APAR is transformed into dry matter through biological efficiency coefficients and results in global
NPP estimations (Heiman and Keeling 1988, Ruimy et al. 1994). A major advantage of these diagnostic
studies is that the response of the vegetation to environmental facing is included, to some extent, in the
satellite signal. However, we also need to develop predictive understanding of the vegetation functioning. This
means that prognostic process vegetation models are promising tools to address the response of the global
system to anthropogenic perturbations (Agren et al. 1991, Mellilo et al. 1993). However, as these models are
still in their infancy and suffer from the scarcity of relevant large scale data sets, it seems interesting to use
satellite measurements which are a unique source of observations.
In this paper, we propose a methodology to control a vegetation model with remote sensing data. A
medium-term objective is to evaluate the impact of satellite measurements assimilation on the estimations of
vegetation CO2 uptake and release during the 80's and to investigate whether biophysical processes modelling
can benefit from remotely sensed information (see Fischer et al. this issue, fa a more general discussion). In
section 2 we briefly describe the model and mention the specific features required for the control by AVHRR
measurements. The temporal development of the canopy is of particular importance, fa the carbon budget as
well as for reflectance modelling. In section 3, we present a synthetic experiment which establishes the
feasibility of this approach. Finally, we discuss (section 4) the possible contributions of satellite dam
assimilation to global carbon cycle studies and spatialization of biosphere models.