title: Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation.
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.
authors.affiliation: National Institute for Agro-Environmental Sciences, Tsukuba, Japan.
authors.forename: Yoshio
authors.initials: Y
authors.lastname: Inoue
authors.affiliation: INRA, UMR1114, EMMAH, F-84914, Avignon, France.
authors.forename: Martine
authors.initials: M
authors.lastname: Guérif
authors.affiliation: INRA, UMR1114, EMMAH, F-84914, Avignon, France.
authors.forename: Frédéric
authors.initials: F
authors.lastname: Baret
authors.affiliation: University of Twente, Enschede, The Netherlands.
authors.forename: Andrew
authors.initials: A
authors.lastname: Skidmore
authors.affiliation: University of Nebraska, Lincoln, NE, USA.
authors.forename: Anatoly
authors.initials: A
authors.lastname: Gitelson
authors.affiliation: Luxembourg Institute of Science and Technology, Belval, Luxembourg.
authors.forename: Martin
authors.initials: M
authors.lastname: Schlerf
authors.affiliation: University of Twente, Enschede, The Netherlands.
authors.forename: Roshanak
authors.initials: R
authors.lastname: Darvishzadeh
authors.affiliation: INRA, UMR1114, EMMAH, F-84914, Avignon, France.
authors.forename: Albert
authors.initials: A
authors.lastname: Olioso
mesh_terms: D000465:Algorithms; D027461:Beta vulgaris / Q000737:chemistry; D002734:Chlorophyll / Q000032:analysis*; D012275:Oryza / Q000737:chemistry; D010788:Photosynthesis; D018515:Plant Leaves / Q000737:chemistry; D010944:Plants / Q000737:chemistry*; D058998:Remote Sensing Technology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013025:Glycine max / Q000737:chemistry; D013057:Spectrum Analysis / Q000379:methods; D014908:Triticum / Q000737:chemistry; D003313:Zea mays / Q000737:chemistry
chemical_list: D002734:Chlorophyll
keywords: photosynthesis; reflectance; spectral index