730
resulted in a little improvement on soil noise reduction, while the sensitivity to vegetation changed little. The
combination of the blue band and the modeled L function in ASVI not only increased the vegetation sensitivities, but
simultaneously reduced the soil and atmospheric effects. All indices tested were similar in responses to view anal?
variations, having a tendency to increase at large off-nadir view angles.
In conclusion, no single vegetation index is ideal. The choice of vegetation indices in remote sensing
applications depends both on the quality of the data and on the targets studied. For arid or semiarid regions such as
north Africa where vegetation is sparse, the soil and atmosphere effects are pronounced. In this case, one may choose
ASVI or SARVI. In regions such as tropical areas where substantial vegetation presents while the atmosphere varies
frequently, then ASVI, ARVI, and GEMI are somewhat equivalent. For ground or aerial remote sensing measurements,
where the atmosphere plays little role in the data quality, the MSAVI and SAVI may be used if vegetation density is
low, and NDVI and MSAVI can be used at high vegetation covers.
It should be pointed out that the results presented here were based on simulated data sets and limited number
of measurements. Therefore, the statistical significance of these differences found among vegetation indices remain to
be further validated with satellite- and ground-based measurements. It should also be pointed out that some vegetation
indices use three bands while others use only two bands, which should be also taken into account in choosing vegetation
indices.
Acknowledgements: The authors are grateful to the USD A ARS Water Conservation Laboratory in Phoenix for financial
support and Southwest Watershed Research Center in Tucson for providing a very convenient working environment.
This work is also part of the NASA Interdisciplinary Research Program in Earth Sciences (NASA Reference Number
IDP-88-086) at the University of Arizona (USA) and LERTS (Toulouse, France).
REFERENCES
Baret F., Guyot G., and Major D., 1989, TSAVI: A vegetation index which minimizes soil brightness effects
on LAI or APAR estimation, in 12th Canadian symposium on Remote Sensing and IGARSS’90, Vancouver,
Canada, July 10-14. 1989.
Baret F. and Guyot G., 1991, Potentials and limi ts of vegetation indices for LAI and APAR assessment. Remote
Sens. Environ. 35:161-173.
Condit, H. R. (1970),The spectral reflectances of American soils, Photogramm. Eng. 36: 955-966.
Deering D. W., 1989, Field Measurements of Bidirectional Reflectance in Theory and Applications of Optical
Remote Sensing by Asrar G. (eds.), pp 14-65.
Holben B. N.. 1986, Characteristics of maximum-value composite images from temporal AVHRR data. Int.
J. Remote Sens. 7:1417-1434.
Huete A. R., 1988, A soil-adjusted vegetation index (SAVI), Remote Sens. Environ. 25:295-309.
Huete A. R„ 1989, Soil influences in remotely sensed vegetation-canopy spectra, in Theory and Applications
of Optical Remote Sensing by Asrar G. (eds.), pp 107-141.
Kaufman Y. J„ 1989, The atmospheric effect n remote sensing and its correction, in Theory and Applications
of Optical Remote Sensing by Asrar G. (eds.), pp 336-428.
Kaufman Y. J. and Tanre D., 1992, Atmospherically resistant vegetation index (ARVI) for EOS-MODIS.
IEEE Trans. Geosci. Remote Sensing, vol. 30 no. 2, pp 261 - 270.
Major D. J., Baret F., and Guyot G., 1990, A ratio vegetation index adjusted for soil brightness, Int. J. Remote
Sens. Vol. 11, No. 5, pp 727-740.
Pinty B. and M. M. Verstraete,1992, GEMI: a non-linear index to monitor global vegetation from satellites.
Vegtatio, vol. 101, pp. 15-20.
Qi J, 1993, Compositing multitemporal remote sensing data sets. A Ph.D dissertation. University of Arizona,
Tucson, Arizona, pp 106-133.
Qi J., Chehbouni A., Huete A. R., and Kerr Y., A, 1994, Modified soil adjusted vegetation index (MSAVI).
Remote Sens. Environ., in press.
Tanre, D., C. Deroo. P. Duhaut. M. Herman, JJ. Morcrette, J. Perbos, and P. Y. Deschamps, 1990, Description
of a computer code to simulate the satellite signal in the solar spectrum: 5S code, Int. J. Remote Sensing, vol.
11, pp. 659-668.
Verhoef W., 1984, Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL
model. Remote Sens. Environ., 16:125-141.