Precision Agriculture

, Volume 20, Issue 2, pp 237–259 | Cite as

A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat

  • Nathalie Al MakdessiEmail author
  • Martin Ecarnot
  • Pierre Roumet
  • Gilles Rabatel


In-field hyperspectral imagery is a promising tool for crop phenotyping or monitoring. In association with partial least square regression (PLS-R), it allows building high spatial resolution maps of the chemical content of plant leaves. However, several optical phenomena must be taken into account, due to their influence on collected spectral data. The most challenging is multiple scattering, produced when a leaf is partly illuminated by light reflection or transmission from neighboring leaves. It can induce bias in prediction results. This paper presents a method for multi-scattering correction. Its development has been based on simulation tools: a 3D canopy model of winter wheat was combined with light propagation modeling, in order to simulate the apparent reflectance of every visible leaf in the canopy for a given actual reflectance. Leaf nitrogen content (LNC) prediction has been considered. A data set of reflectance spectra associated with LNC values has been issued from real leaf measurements. A theoretical disturbance subspace representing the spectrum dispersion in the spectral space due to multi-scattering has then been built by considering polynomial combinations of the initial spectra, and a projection along this subspace has been applied to every simulated spectra. Using this strategy, a PLS-R model built on initial spectra was still satisfactory when applied to simulated spectra with multiple scattering. The method has then been applied to real plants in greenhouse and field conditions, and its prediction results compared with those of a standard PLS-R, confirming its efficiency in the presence of various lighting environments.


Hyperspectral imagery Multiple scattering Phenotyping Canopy modeling 



This study has been supported by Agropolis Foundation under the reference ID 1202-008 through the ‘Investissements d’avenir’ program (Labex Agro:ANR-10-LABX-0001-01), France Agrimer and l’Agence Nationale de la Recherche (ANR) through the Phenoble and Phenome programmes.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Nathalie Al Makdessi
    • 1
    Email author
  • Martin Ecarnot
    • 2
  • Pierre Roumet
    • 2
  • Gilles Rabatel
    • 1
  1. 1.IRSTEA, UMR ITAPMontpellierFrance
  2. 2.INRA, UMR AGAPMontpellier Cedex 02France

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