Remote sensing and detection of nitrogen status in crops. Application to precise nitrogen fertilization

Remote sensing techniques offer a unique solution for mapping the growth and nitrogen status of crops and monitoring its time course. Therefore these maps are powerful tools for recommending spatially precise cultural practices, and particularly nitrogen dressing. In this paper, we review the main issues to be addressed for estimating crop variables from remote sensing and for using them for managing variable rate fertilizer application. The derivation of canopy state variables such as the leaf area index (LAI) and chlorophyll content (Cab) is first addressed. It is demonstrated that the inversion of radiative transfer models leads to useful estimates of these variables. However, because of the ill-posed nature of the inverse problem, better accuracy is achieved when using prior information on the distribution of the variables and when multiplying LAI by Cab to get canopy level chlorophyll content. This variable, LAI.Cab is well suited for quantifying canopy level nitrogen content. The use of such variables for recommending optimal nitrogen rates is addressed further. A first way consists in deriving different indices (index of nutrition, index of deficit of absorption) that can either be substituted into classical empirical methods or directly used to calculate doses. The combination of remote sensing observations with crop models provides a more powerful solution that makes it possible to maximize the farmer gross margin while limiting risks of nitrate leaching