A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy

By Sylvain Jay, Ryad Bendoula, X. Hadoux, Jean-Baptiste F'eret, Nathalie Gorretta. 2016

150 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/efa048c88969441f822c36590f50fc9c3ec42459

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In Ecology

In Global and Planetary Change

In Plant Science

Citations in the corpus

PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle (2017, background): “The development of PROSPECT-D partly answers these limitations, but further efforts will be needed to include specular reflection (Bousquet et al., 2005; Comar et al., 2014; Jay et al., 2016).”
Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform (2018, background): “suitable in close-range imaging spectroscopy because the physically-based leaf or canopy RTMs are not adapted to the specific illumination problems (Jay et al., 2016).”
Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping (2017, background): “…Gamon, 2002) and MERIS Terrestrial Chlorophyll Index ( 𝑀𝑇𝐶𝐼 ) (Dash and Curran, 2004) decrease the two above influences, as such effects can, respectively, be approximated by multiplicative and additive perturbations under the bi-directional hypothesis in the visible domain (Jay et al., 2016).”
PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents (2020, unknown): “Close-range remote sensing applications, based for instance on the PROCOSINE model (Jay et al., 2016; Morel et al., 2018), could be considered as an intermediate scale between leaf and canopies, but a certain number of challenges would need to be addressed first.”
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods (2018, unknown): “…data include: PROSPECT inversion to retrieve leaf chloro-623 phyll content (Zhang and Wang, 2015), retrieval of leaf biochemistry against an improved624 version of PROSPECT (COSINE) (Jay et al, 2016), and PROSAIL leaf and canopy vari-625 ables (Bayat et al, 2016; van der Tol et al, 2016).”