Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation

Yoshio Inoue, Martine Guérif, Frédéric Baret, Andrew Skidmore, Anatoly Gitelson, Martin Schlerf, Roshanak Darvishzadeh, Albert Olioso

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI; R815, R704) with the reflectance at 815 and 704 nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy-chlorophyll-metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive-ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.

Original languageEnglish (US)
Pages (from-to)2609-2623
Number of pages15
JournalPlant Cell and Environment
Volume39
Issue number12
DOIs
StatePublished - Dec 1 2016

Fingerprint

hyperspectral imagery
Chlorophyll
vegetation types
remote sensing
canopy
chlorophyll
reflectance
methodology
Beta vulgaris
Poaceae
Soybeans
Italy
Triticum
Zea mays
France
Ecosystem
Japan
sugar beet
wavelengths
soybeans

Keywords

  • photosynthesis
  • reflectance
  • spectral index

ASJC Scopus subject areas

  • Physiology
  • Plant Science

Cite this

Simple and robust methods for remote sensing of canopy chlorophyll content : a comparative analysis of hyperspectral data for different types of vegetation. / Inoue, Yoshio; Guérif, Martine; Baret, Frédéric; Skidmore, Andrew; Gitelson, Anatoly; Schlerf, Martin; Darvishzadeh, Roshanak; Olioso, Albert.

In: Plant Cell and Environment, Vol. 39, No. 12, 01.12.2016, p. 2609-2623.

Research output: Contribution to journalArticle

Inoue, Y, Guérif, M, Baret, F, Skidmore, A, Gitelson, A, Schlerf, M, Darvishzadeh, R & Olioso, A 2016, 'Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation', Plant Cell and Environment, vol. 39, no. 12, pp. 2609-2623. https://doi.org/10.1111/pce.12815
Inoue, Yoshio ; Guérif, Martine ; Baret, Frédéric ; Skidmore, Andrew ; Gitelson, Anatoly ; Schlerf, Martin ; Darvishzadeh, Roshanak ; Olioso, Albert. / Simple and robust methods for remote sensing of canopy chlorophyll content : a comparative analysis of hyperspectral data for different types of vegetation. In: Plant Cell and Environment. 2016 ; Vol. 39, No. 12. pp. 2609-2623.
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