Clevers, Jan 
  
Table 3: Deviations in final yield (t ha!) for the 5 winter wheat fields in 1997 (Observed - Simulated). The average 
presents the average absolute deviation. 
  
  
  
  
  
  
  
Field-id | Simulation Simulation + Simulation, 
reset reset + 
interpolation 
101 -0.858 -0.370 -0.647 
120 -0.139 0.226 -0.017 
208 1.158 1.625 1.302 
210 1.868 2.244 2.073 
300 -0.134 0.381 0.027 
average | 0.83 0.97 0.81 
  
  
  
  
  
  
5 CONCLUSIONS 
Results show that the semi-empirical CLAIR model can well be used for estimating LAI of wheat fields in Southern 
France. The parameters derived for cereals in The Netherlands can be applied in Southern France. Moreover, the 
relationships and methods derived from field-based and airborne observations can be applied to spaceborne 
measurements. This indicates that this approach may have a broad applicability and that parameter estimates are not 
site-specific. 
Often the lack of specific field data hampers the good calibration of mechanistic simulation models like Rotask y 1.5. 
Remote sensing has proven to offer a valuable tool for the run-time calibration, leading to better estimates of LAI and 
grain yield in situations where only average values for model input parameters are available. If the cropping calendar is 
more or less known, the timing and frequency of calibration by remote sensing images can be optimized. The error in 
LAI estimation by SPOT remote sensing images is directly imposed on the simulation model, and can lead to 
unsatisfactory results. 
ACKNOWLEDGEMENTS 
The Alpilles-ReSeDA project is funded by the EEC-DG XII (contract ENV4-CT96-0326 - PL952071), the French 
Programme National de Télédétection Spatiale and Programme de Recherches en Hydrologie. The IRSA-MARS project 
provides access to ground data and SPOT images (Arles segment). 
REFERENCES 
Ahlrichs, J.S. and Bauer, M.E., 1983. Relation of agronomic and multispectral reflectance characteristics of spring 
wheat canopies. Agronomy Journal, 75, pp. 987-993. 
Baret, F. et al., 1999. ReSeDA: Assimilation of multisensor & multitemporal remote sensing data to monitor vegetation 
and soil functioning. Second Annual Report ReSeDA project, INRA, July 1999, 46 pp. 
Bouman, B.A.M., 1991. Linking X-band radar backscattering and optical reflectance with crop growth models. Ph.D. 
Thesis, Wageningen Agricultural University, Wageningen, The Netherlands. 
Bouman, B.A.M., Van Kasteren, H.W.J., and Uenk, D., 1992. Standard relations to estimate ground cover and LAI of 
agricultural crops from reflectance measurements. Eur. J. Agron., 4, pp. 249-262. 
Clevers, J.G.P-W., 1988. The derivation of a simplified reflectance model for the estimation of Leaf Area Index. Remote 
Sens. Environ., 25, pp. 53-69. 
  
278 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 
BE SEE s