Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands

Oz Kira, Raphael Linker, Anatoly Gitelson

Research output: Contribution to journalArticlepeer-review


Leaf pigment content provides valuable insight into the productivity, physiological and phenologicalstatus of vegetation. Measurement of spectral reflectance offers a fast, nondestructive method for pigmentestimation. A number of methods were used previously for estimation of leaf pigment content, however,spectral bands employed varied widely among the models and data used. Our objective was to findinformative spectral bands in three types of models, vegetation indices (VI), neural network (NN) andpartial least squares (PLS) regression, for estimating leaf chlorophyll (Chl) and carotenoids (Car) contentsof three unrelated tree species and to assess the accuracy of the models using a minimal number of bands.The bands selected by PLS, NN and VIs were in close agreement and did not depend on the data used.The results of the uninformative variable elimination PLS approach, where the reliability parameter wasused as an indicator of the information contained in the spectral bands, confirmed the bands selected bythe VIs, NN, and PLS models. All three types of models were able to accurately estimate Chl content withcoefficient of variation below 12% for all three species with VI showing the best performance. NN and PLSusing reflectance in four spectral bands were able to estimate accurately Car content with coefficient ofvariation below 14%. The quantitative framework presented here offers a new way of estimating foliarpigment content not requiring model re-parameterization for different species. The approach was testedusing the spectral bands of the future Sentinel-2 satellite and the results of these simulations showedthat accurate pigment estimation from satellite would be possible
Original languageEnglish GB
Pages (from-to)251-260
JournalInternational Journal of Applied Earth Observation and Geoinformation
StatePublished - Jun 2015
Externally publishedYes


  • Carotenoids
  • Chlorophyll
  • Neural network
  • Non-destructive technique
  • Reflectance


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