Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem

By B. Combal, Frédéric Baret, Marie Weiss, A. Trubuil, D. Macé, A. Pragnère, Ranga B. Myneni, Yuri Knyazikhin, L. Wang. 2003

690 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/b153b7eadbab652473579aee91ff8dbcaddad83f

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In Ecology

In Global and Planetary Change

Citations in the corpus

Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method (2016, background): “Although radiative transfer models are more generalized, they often require prior information when used in an inversion to reduce the number of possible variable retrieval options (combinations of canopy parameters) to avoid ill-posed problems and improve the inversion accuracy (Combal et al. 2002; ”
Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model (2015, methodology): “1995), artificial neural networks (Atzberger et al. 2003; Combal et al. 2003), and support-vector-machine regressions (Mountrakis, Im, and Ogole 2011).”
Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image (2014, methodology): “A priori information allows restricting the variable space to a smaller subspace, thus facilitating the inversion (Combal et al., 2002).”
Review of optical-based remote sensing for plant trait mapping (2013, methodology): “The major drawback of the physical methods is that different combinations of RTM input parameters may produce the same reflectance spectra, which makes estimation of canopy properties from RS data ambiguous (Baret and Buis, 2008; Combal et al., 2003).”
Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation (2007, background): “However, the problems of model uncertainties and model equifinality may require the use of considerable a priori knowledge of the leaves and canopies under investigation (Combal et al. 2002) which can limit the practicality of this approach for operational use.”
Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat (2010, background): “…forward mode) the spectral signature of the canopy under tudy (Jacquemoud et al., 2000; Darvishzadeh et al., 2008b) and (2) he ill-posedness of the inverse problem due to counterbalancing ffects between ALA, LAI and soil brightness (Combal et al., 2003; tzberger, 2004; Darvishzadeh et al., 2008a).”
Canopy spectral invariants, Part 2: Application to classification of forest types from hyperspectral data (2011, methodology): “In this paper we propose a physically based approach for separation of dominant forest type using hyperspectral data.”
Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 (2013, methodology): “These methods provide a physically-based linkage between biophysical variables and vegetation canopy reflectance at different wavelengths [24–26].”
Determination of vegetation cover fraction by inversion of a four-parameter model based on isoline parametrization (2007, background): “Two kinds of inversion models are distinguished, those dealing only with one parameter (Baret et al., 1995; Kuusk, 1991a, 1995; Verstraete et al., 1990) and those dealing with many canopy parameters (Baret & Buis, 2007; Combal et al., 2002; Kimes et al., 2000).”