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Title
Mesures physiques et signatures en télédétection

1109
superior to both SAVI and MSAVI in the case of dark soils, it is always the worst of all the indices in the case
of medium and bright soils.
5-CONCLUSIONS
A number of spectral vegetation indices have been proposed to analyse satellite remote sensing data.
The objective of this work was to quantitatively evaluate the performance of a representative subset of these
indices with respect to their capability to estimate the fractional vegetation cover.
NDVI was selected because of its wide use in the literature. WDVI, SAVI and MSAVI were selected because
they were designed specifically to be less sensitive than NDVI to soil color and brightness variations. GEMI
was evaluated because it has been constructed to be less sensitive than NDVI to both atmospheric and soil
effects. Objective criteria to evaluate the performance of these indices were set up and applied to these indices.
Albedo values were computed with a coupled soil-plant canopy model, both at the surface and at the
top of the atmosphere. Ten soil types and three continental atmospheres were considered, to represent a wide
range of situations that could be expected in global applications. The results were computed over two ranges of
fractional vegetation cover (0-50% and 50-100%), and reported in the form of signal to noise (S/N) ratios. The
signal has consistently been taken as the variation in the index value over the specified range of fractional
cover, while the noise has been computed from the uncertainty in index value resulting from the lack of a
priori knowledge on the type of soil or composition of the atmosphere. Since an average atmospheric
correction could be applied to account for the presence of a standard atmosphere (mostly Rayleigh scattering),
the S/N at the top of a turbid atmosphere was computed on the basis of comparisons with albedos that would
have been observed in the abscence of aerosols.
It has been shown that, within the framework of the model assumptions made in this study, the widely
used NDVI and the WDVI are generally the poorer predictors of the fractional vegetation cover than either of
the newer indices : SAVI,MSAVI and GEMI. SAVI and MSAVI are best suited for laboratory and field studies
because of their significantly reduced sensitivity to soil type, while the GEMI is a good predictor of fractional
vegetation cover, especially in the lower range of values and when the estimation of this parameter must be
made on the basis of measuurements taken in space.
It would be interesting to try to validate these results on the basis of field measurements, although this
would be a very complex endeavor. This study has shown that it is possible to evaluate objectively the
performance of vegetation indices with respect to the evaluation of a specific surface parameter, under a variety
of soil and atmospheric conditions. To the extent that a single vegetation index is being applied globally and
repetitively to address climatic and environmental issues, it is clear that the evaluation and use of advanced
indices designed to be relatively insensitive to perturbations is most profitable.
Acknowledgments:
The authors are grateful to Dr. Y. Kaufman of NASA/GSFC for being the first to suggestthat the concept of
signal to noise may be useful to evaluate vegetation indices. In their analysis, the authors have benefited from
formal and informal discussions during a meeting organized at NASA GSFC in June 1992. M. Verstiaete is
also acknowledging the continuing support of the Global Change research program of the Institute for Remote
Sensing Applications, and in particular of its Exploratory Research Program.
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