Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements

By Guerric le Maire, Christophe François, Eric Dufrene. 2004

649 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/fbe3480793702e16cd74694ec7bc7278423f6aaa

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Citations in the corpus

Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection (2013, background): “…is difficult, if not impossible, to establish a generic statistical framework in hyperspectral remote sensing of plant biochemistry for diverse vegetation communities across the globe (Atzberger et al., 2010; Bolster et al., 1996; Grossman et al., 1996; le Maire et al., 2004; Schlerf et al., 2010).”
Spectral signatures of sugar beet leaves for the detection and differentiation of diseases (2010, background): “Most developed indices are highly correlated with pigment content, biomass or leaf area ( Le Maire et al. 2004; Thenkabail et al. 2000).”
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 (2008, methodology): “At leaf scale, we continue the study done in le Maire et al. (2004) using an improved and newly calibrated version of the PROSPECT model (Feret et al., 2008), and a larger experimental database.”
Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data (2011, methodology): “Optical models operating in forward-mode have been used successfully in a number of studies for developing remote sensing techniques (e.g. Ceccato et al. 2001, Le Maire et al. 2004, Coops and Stone 2005, Koetz et al. 2007).”
Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments (2018, methodology): “74 Although le Maire et al. (2004) pointed out that the previous versions of PROSPECT were not 75 accurate enough to evaluate broad leaf chlorophyll content, this version has not been fully 76 evaluated.”
Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation (2008, methodology): “Le Maire et al. (2004) developed, tested, and compared several leaf chlorophyll indices using a larger simulated database (11,583 spectra) of a leaf-radiative transfer model (PROSPECT, Jacquemoud and Baret, 1990) and ground truth data to determine ‘‘universal’’ chlorophyll content indices…”
Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison (2015, methodology): “Common types of published indices as identified by le Maire et al. (2004) and other authors (see ‘Source’).”
HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging (2015, neutral): “, NDVI, MCARI) [47,48].”
An investigation into robust spectral indices for leaf chlorophyll estimation (2011, methodology): “Our results also have similarities to those reported in le Maire et al. (2004), where they used data from various deciduous trees species to test the performance of 60 published chlorophyll indices.”