Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation

Crop models can be useful tools for estimating crop growth status and yield on large spatial domains if their parameters and initial conditions values can be known for each point. By coupling a radiative transfer model with the crop model (through a canopy structure variable Like LAI), it is possible to assimilate, for each point of the spatial domain, remote sensing variables (like reflectance in the visible and near-infrared, or their combination into a vegetation index), and to re-estimate for this point some of the parameters or initial conditions of the model. The spatial adjustment of the crop model provides, therefore, a better estimation of yield. This process has been tested on the re-estimation of crop stand establishment parameters and initial conditions for sugar beet (Beta vulgaris L.) crops, using the crop model SUCROS coupled to the radiative transfer model SAIL. The quality of recalibration depends in particular on the precision on the SAIL parameters other than LAI: soil reflectance, optical properties of leaves, and leaf angles. In applications on large domains, where these parameters may vary a lot, their estimation is an important factor in recalibration error. It was shown, using stochastic simulation, that beforehand knowledge of the variability of soil and crop characteristics considerably improved the results of the assimilation of reflectance measurements. The best results were obtained when the spectral reflectances were combined into the TSAVI vegetation index, which minimised the disruptive contribution of soil to the canopy reflectance. In particular, the use of TSAVI provided more consistent results for the estimates of the sowing date and emergence parameters, which remained poorer than yield estimates. Over a wide range of unknown crop situations corresponding to extreme sowing dates and emergence conditions, the proposed method allowed to estimate the sugar beet yield with relative errors varying from 0.6 to 2.6%. The results appeared to depend on the timing of remote sensing data acquisition; the best situation was when the data covered the whole period of LAI growth (including the highest values). (C) 2000 Published by Elsevier Science B.V.