Single bands leaf reflectance prediction based on fuel moisture content for forestry applications

Tito André Arevalo-Ramirez, Andrés Hernán Fuentes Castillo, Pedro Sebastián Reszka Cabello, Fernando A. Auat Cheein*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Vegetation indices can be used to perform quantitative and qualitative assessment of vegetation cover. These indices exploit the reflectance features of leaves to predict their biophysical properties. In general, there are different vegetation indices capable of describing the same biophysical parameter. For instance, vegetation water content can be inferred from at least sixteen vegetation indices, where each one uses the reflectance of leaves in different spectral bands. Therefore, if the leaf moisture content, a vegetation index and the reflectance at the wavelengths to compute the vegetation index are known, then the reflectance in other spectral bands can be computed with a bounded error. The current work proposes a method to predict, by a machine learning regressor, the leaf reflectance (spectral signature) at specific spectral bands using the information of leaf moisture content and a single vegetation index of two tree species (Pinus radiata, and Eucalyptus globulus), which constitute 97.5% of the Valparaíso forests in Chile. Results suggest that the most suitable vegetation index to predict the spectral signature is the Leaf Water Index, which using a Kernel Ridge Regressor achieved the best prediction results, with a RMSE lower than 0.022, and a average R2 greater than 0.95 for Pinus radiata and 0.81 for Eucalyptus globulus, respectively.

Original languageEnglish
Pages (from-to)79-95
Number of pages17
JournalBiosystems Engineering
Early online date28 Dec 2020
Publication statusPublished - Feb 2021


  • Leaf water index
  • Machine learning
  • Remote sensing
  • Wildfire
  • Wildland fuels

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Food Science
  • Animal Science and Zoology
  • Agronomy and Crop Science
  • Soil Science


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