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

REMOTE SENSING OF ENVIRONMENT

卷:115

Article

Feret, Jean-Baptiste^1,2,3  Francois, Christophe³  Gitelson, Anatoly⁴  Asner, Gregory P.¹  Barry, Karen M.^5,6  Panigada, Cinzia⁷  Richardson, Andrew D.⁸  Jacquemoud, Stephane² 

[1] Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA

[2] Univ Paris Diderot, UMR CNRS 7154, Inst Phys Globe Paris Sorbonne Paris Cite, F-75013 Paris, France

[3] Univ Paris 11, Lab Ecol Systemat & Evolut UMR 8079, Orsay, France

[4] Univ Nebraska, Sch Nat Resources, Ctr Adv Land Management Informat Technol, Lincoln, NE USA

[5] Univ Tasmania, Sch Agr Sci, Hobart, Tas 7001, Australia

[6] CRC Forestry, Hobart, Tas 7001, Australia

[7] Univ Milano Bicocca, Dip Sci Ambiente & Terr, Remote Sensing Environm Dynam Lab, I-20126 Milan, Italy

[8] Harvard Univ, Dept Organism & Evolutionary Biol, HUH, Cambridge, MA 02138 USA

关键词: Leaf optical properties;    PROSPECT;    Hyperspectral data;    Pigment content;    Water content;    Leaf mass per area;    Spectral indices;    Partial least squares regression;   

DOI  :  10.1016/j.rse.2011.06.016

来源: Elsevier

【 摘 要 】

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. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

Free