Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode.

By Stephane Jacquemoud, Cédric Bacour, H. Poilvé, J. Frangi. 2000

462 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/267cca0b72054c329378ef7c7469b023f94f6a1c

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Citations in the corpus

Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements (2004, result): “Correlation coefficients of each, respectively 0.65, 0.95, and 0.67, were obtained (Hosgood et al., 1995, cited in Jacquemoud et al., 2000).”
Hyperspectral remote sensing of plant pigments. (2006, methodology): “The inversion of coupled leaf and canopy RT models has been demonstrated for quantifying Chl from canopy hyperspectral data (Jacquemoud et al., 1995, 2000).”
Estimation of dry matter content in leaves using normalized indexes and PROSPECT model inversion (2012, background): “But Jacquemoud et al. (2000) reported a better correlation (r2 = 0.65) with 63 species.”
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, background): “, chlorophyll and water contents) in the forward mode, and for inversion of these variables from spectral measurements in the inverse mode [181].”
Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies (2009, background): “Using this approach, Jacquemoud et al. (2000), and many others, have shown how water is expressed throughout the NIR and shortwave-infrared (SWIR) spectral range (~800 nm to 2500 nm), and how radiative transfer models can be used to estimate leaf equivalent water thickness via model inversion.”
Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data (2011, background): “…the solar spectrum from 400 to 2500 nm. Reflectance and transmittance is simulated based on four input parameters: the chl-a and chl-b concentration (chl, µg cm−2), dry matter content (Cm, g m−2), leaf structure parameter (N) and the equivalent water thickness (Cw, g m−2) (Jacquemoud et al. 2000).”
Design and analysis of numerical experiments to compare four canopy reflectance models (2002, methodology): “The version of PROSPECT used here is the last one that requires the leaf structure parameter (N), the chlorophyll-a and -b content (Cab, mg cm 2), the equivalent water thickness (Cw, g cm 2 or cm), and the dry matter content (Cm, g cm 2) to simulate leaf reflectance and transmittance spectra in the ”
Sources of variability in canopy reflectance and the convergent properties of plants. (2011, background): “The consistency of direct water absorption features has enabled them to be included in leaf optical models (e.g. Jacquemoud et al., 2000) and has led to the development of simple band ratio indices for characterizing plant water via remote sensing (e.g. the normalized difference water index (NDWI;…”
Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography (2018, background): “…approaches based on all these sources have a restricted power due to the65 required empirical calibration, radiative transfer models are not widely in use either, most probably66 due to their complexity and computation power requirements (Jacquemoud et al., 2000; Verrelst et al.,67 2015).”
PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments (2008, methodology): “Unlike pigments, Jacquemoud et al. (2000) observed that the in vivo specific absorption coefficient of water was very similar to that of pure liquid water, thus we decided to use data from the literature alibration process.”
Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data (2008, methodology): “canopy as well as on the underlying soil properties, illumination conditions, and viewing geometry [20], [22], [42], [59].”