Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling
Jean-Baptiste Féret,Jean-Baptiste Féret,Jean-Baptiste Féret,C. François,Anatoly A. Gitelson,Gregory P. Asner,Karen M. Barry,Karen M. Barry,Cinzia Panigada,Andrew D. Richardson,Stéphane Jacquemoud +10 more
TLDR
In this article, the authors used synthetic reflectance spectra generated by a radiative transfer model to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment.About:
This article is published in Remote Sensing of Environment.The article was published on 2011-10-17 and is currently open access. It has received 259 citations till now. The article focuses on the topics: Partial least squares regression & Synthetic data.read more
Citations
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PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle
Jean-Baptiste Féret,Anatoly A. Gitelson,Anatoly A. Gitelson,Scott D. Noble,Stéphane Jacquemoud +4 more
TL;DR: A new version of the widely-used PROSPECT model is presented, hereafter namedPROSPECT-D for dynamic, which adds anthocyanins to chlorophylls and carotenoids, the two plant pigments in the current version, and outperforms all the previous versions.
Journal Article
Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass
G. Le Maire,C. François,Kamel Soudani,Daniel Berveiller,Jean-Yves Pontailler,Nathalie Bréda,Hélène Genet,Hendrik Davi,Eric Dufrêne +8 more
TL;DR: In this article, the best vegetation indices (index form and wavelengths) were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way.
Journal ArticleDOI
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping
TL;DR: A case study is presented with sample data sets obtained from a ground-based proximal remote sensing buggy mounted with the following sensors: LiDAR, RGB camera, thermal infra-red camera and imaging spectroradiometer.
Journal ArticleDOI
Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels
Michael R. Schlemmer,Anatoly A. Gitelson,James S. Schepers,Richard B. Ferguson,Yi Peng,John F. Shanahan,Donald C. Rundquist +6 more
TL;DR: Remote sensing techniques were applied to estimate N and Chl contents of irrigated maize fertilized at five N rates and showed that at the canopy level, Chl and N contents can be accurately retrieved using green and red-edge Chl indices using near infrared (780-800 nm) and either green or red- edge spectral bands.
References
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Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages
Daniel A. Sims,John A. Gamon +1 more
TL;DR: Developing spectral indices for prediction of leaf pigment content that are relatively insensitive to species and leaf structure variation and thus could be applied in larger scale remote-sensing studies without extensive calibration are developed.
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PROSPECT: A model of leaf optical properties spectra
TL;DR: In this paper, a radiative transfer model based on Allen's generalized plate model is proposed to represent the optical properties of plant leaves from 400 nm to 2500 nm, where spectral refractive index (n) and a parameter characterizing the leaf mesophyll structure (N) are used.
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Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves
TL;DR: Spectral reflectance of maple, chestnut, wild vine and beech leaves in a wide range of pigment content and composition was investigated and it was shown that reciprocal reflectance (R lambda)-1 in the spectral range lambda related closely to the total chlorophyll content in leaves of all species.
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Detection of changes in leaf water content using Near- and Middle-Infrared reflectances
E. Raymond Hunt,Barrett N. Rock +1 more
TL;DR: In this paper, the ability of the Leaf Water Content Index (LWCI) to determine leaf relative water content (RWC) is tested on species with different leaf morphologies.
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Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages
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