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


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Feret, JB, Francois, C, Gitelson, A, Barry, KM, Panigada, C, Richardson, AD and Jacquemoud, S 2011 , 'Optimising spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling' , Remote Sensing of Environment, vol. 115 , pp. 2742-2750 , doi: 10.1016/j.rse.2011.06.016.

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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.

Item Type: Article
Authors/Creators:Feret, JB and Francois, C and Gitelson, A and Barry, KM and Panigada, C and Richardson, AD and Jacquemoud, S
Journal or Publication Title: Remote Sensing of Environment
ISSN: 0034-4257
DOI / ID Number: 10.1016/j.rse.2011.06.016
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