Can nutrient status of four woody plant species be predicted using field spectrometry?

J.G. Ferwerda, A.K. Skidmore

Research output: Contribution to journalArticleAcademicpeer-review

50 Citations (Scopus)


This paper demonstrates the potential of hyperspectral remote sensing to predict the chemical composition (i.e., nitrogen, phosphorous, calcium, potassium, sodium, and magnesium) of three tree species (i.e., willow, mopane and olive) and one shrub species (i.e., heather). Reflectance spectra, derivative spectra and continuum-removed spectra were compared in terms of predictive power. Results showed that the best predictions for nitrogen, phosphorous, and magnesium occur when using derivative spectra, and the best predictions for sodium, potassium, and calcium occur when using continuum-removed data. To test whether a general model for multiple species is also valid for individual species, a bootstrapping routine was applied. Prediction accuracies for the individual species were lower then prediction accuracies obtained for the combined dataset for all except one element/species combination, indicating that indices with high prediction accuracies at the landscape scale are less appropriate to detect the chemical content of individual species.
Original languageEnglish
Pages (from-to)406-414
JournalISPRS Journal of Photogrammetry and Remote Sensing
Issue number6
Publication statusPublished - 2007


  • reflectance spectroscopy
  • absorption features
  • vegetation indexes
  • hyperspectral data
  • leaf
  • nitrogen
  • variability
  • regression
  • quality
  • corn


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