Full text: Mesures physiques et signatures en télédétection

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.
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.