Full text: Mesures physiques et signatures en télédétection

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crops which is the actual situation. Moreover, values retrieved for the sugar beet correspond to those cited in 
the literature for this particular crop (Baret and Jacquemoud, 1994). In the light of these results, little a priori 
guidance can be given as to the quality of the solution found by the nonlinear optimization alrorithms. 
CONCLUSION 
This study analysed different methods for the inversion of a canopy reflectance model, the PROSPECT+SAIL 
model, which simulates both the spectral and directional variation of vegetation reflectance. Comparisons were 
performed both on "noisy" synthetic data and airborne CAESAR data in terms of accuracy and computation 
time. It appeared that the experimental conditions had a great influence on the performances of the different 
methods and that the choice of the method depended on the priority given to the solution (accuracy or 
computation time). However, results obtained with synthetic data showed the pertinence of such an approach. 
This first attempt to retrieve canopy biophysical characteristics by inversion of a radiative transfer 
model on real airborne remote sensing data was indeed very conclusive in the sense that the problem was very 
complex and that the reflectance measurements on which inversions have been performed did not represent an 
optimal sampling for this application. In this study, we allowed all five parameters to vary freely: due to the 
lack of measurements in the hot spot region and the low canopy reflectance sensitivity to the leaf mesophyll 
arrangement, it was not possible to provide a good estimate of the N and Si parameters. It would be interesting 
in the future to fix these two parameters at their roughly measured values and to perform again new inversions. 
Be that as it may, since no guarantees can be given that a particular inversion method always work, it is 
necessary to check the computed solution even if the routine reports success. 
Finally, this work is worthy to be continued with other field data sets and other experimental 
instruments. The development of airborne sensors such as ASAS (Advanced Solid-State Array) or POLDER 
(Polarization and Directionality of the Earth's Reflectance) which prefigure spacebome instruments 
(respectively MISR and POLDER) capable of acquiring radiance measurements of the Earth surface both in 
several wavelengths and under several viewing angles, offers the possibilities to test these relatively new 
methods to extract surface properties from remote sensing data. 
ACKNOWLEDGEMENTS 
Many thanks to Dr Jan Clevers who provides some useful information about the CAESAR sensor and the field 
experiment. We are also indebted to Dr Andres Kuusk for the improvement of the Fortran code of the SAIL 
model in order to take into account the hot spot effect The data for the Revoland test site were acquired in the 
framework of the MAC Europe 1991 campaign with financial support of the Netherlands Remote Sensing 
Board (project n° 3.2/AO-04) and the Joint Research Centre (contract n° 4530-91-11 ED ISP NL). 
REFERENCES 
Baret F., Jacquemoud S„ 1994. Model inversion to retrieve canopy characteristics from high spectral resolution 
data. In Proc. 6 th Int. Symp. Physical Measurements and Signatures in Remote Sensing, Val dlstire 
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Bilker C., Clevers J.G.P.W., van Leeuwen H.J.C., Bouman B.A.M., Uenk D., 1992a. Optical Component Mac 
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Btlker C„ Clevers J.G.P.W., van Leeuwen H.J.C., 1992b. Optical Component Mac Europe, Optical Data 
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Deering D.W., Eck T.F., Grier T„ 1992. Shinnery oak bidirectional reflectance properties and canopy model 
inversion. IEEE Trans. Geosci. Remote Sens., 30(2):339-348. 
Diner J.D., Bruegge C.J., Martonchik J.V., Bothwell G.W., Danielson E.D., Royd E.L., Ford V.G., Hovland 
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sensing from the Earth Observing System, Int. J. Imaging Syst. Technol., 3:92-107. 
Rasse S.P., 1993. Extracting quantitative information from satellite data: empirical and physical approaches. 
Commission of the European Communities, Joint Research Centre, Institute for Remote Sensing 
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108.
	        
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