Derivation of the red edge index using the MERIS standard band setting

By Jan G. P. W. Clevers, S. M. D. Jong, G. Epema, F. D. V. D. Meer, Wim Bakker, Andrew K. Skidmore, K. Scholte. 2002

211 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Atmospheric Science, Ecology

View Paper: https://www.semanticscholar.org/paper/9090cff65f8ba545c3cd5cacf8b67c1e8a48d8c9

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Citations in the corpus

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling (2007, methodology): “, 1990), fitted high-order polynomials, linear interpolation (Clevers et al., 2002; Guyot et al., 1988) and Langrangian interpolation (Dawson and Curran, 1998).”
Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations (2011, methodology): “Some of these bands correspond to known absorption features cited by Curran (1989) and Kumar et al.”
Remote sensing for wetland classification: a comprehensive review (2018, background): “The reflectance value in this band is related to vegetation biochemical parameters (e.g. chlorophyll content), biophysical parameters (e.g. Leaf Area Index), and water deficit in vegetation biomass (Filella and Penuelas 1994; Clevers et al. 2002; Mutanga, Adam, and Cho 2012).”
Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley (2016, background): “NIR/Red Edge indices are also related to tissue pigment concentration and the crop nutrition status (Clevers et al., 2002).”
Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression (2013, background): “It is well known that appropriate spectral transformation techniques can remove noise and enhance absorption features of biochemicals, thereby improving the accuracy of the regression models (Clevers et al. 2002; Majeke, van Aardt, and Cho 2008).”
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, methodology): “…been proposed to use this region as a sensitive indicator, including: (1) high-order curve fitting (Baret et al., 1992; Broge and Leblanc, 2001; Clevers et al., 2002); (2) inverted Gaussian models (Miller et al., 1990); (3) linear interpolation and extrapolation (Guyot et al., 1988); (4)…”