Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling

Jean Baptiste Féret, Christophe François, Anatoly Gitelson, Gregory P. Asner, Karen M. Barry, Cinzia Panigada, Andrew D. Richardson, Stéphane Jacquemoud

Research output: Contribution to journalArticlepeer-review

250 Scopus citations


We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf.

Original languageEnglish (US)
Pages (from-to)2742-2750
Number of pages9
JournalRemote Sensing of Environment
Issue number10
StatePublished - Oct 17 2011
Externally publishedYes


  • Hyperspectral data
  • Leaf mass per area
  • Leaf optical properties
  • Partial least squares regression
  • Pigment content
  • Spectral indices
  • Water content

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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