Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves

By Anatoly A. Gitelson, G. Keydan, M. Merzlyak. 2006

727 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/9d6ce96e31b64585a9c647d1a42c9ca8e6487f2d

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In Analytical Chemistry

In Ecology

In Plant Science

Citations in the corpus

Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection (2013, methodology): “Spectral indices are often formulated heuristically through simple transforms, including difference, ratio, and high-order derivatives, in attempts to enhance desirable signals and subdue confounding factors (Garbulsky et al., 2011; Gitelson et al., 2006).”
Response of Chlorophyll, Carotenoid and SPAD-502 Measurement to Salinity and Nutrient Stress in Wheat (Triticum aestivum L.) (2017, background): “The leaf chlorophyll content provides a key indicator of the photosynthetic capacity [2,9], and in combination with measurements such as leaf area index has been found to be a critical proxy for vegetation productivity [10] and prevailing stress in vegetation [11–13].”
HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging (2015, background): “Chlorophyll green index Chlgreen 1⁄4 R790 R550 − 1 Chlorophyll (Leaf) [32]”
Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment (2015, background): “…(2) the reflectance decrease at 550 nm is mainly associated with adjustments of photosynthetic and photoprotective pigments, such as anthocyanin (Gitelson et al., 2006; Steele et al., 2009); and (3) the reflectance increase around 1500 nm is strongly affiliated with a direct decrease in leaf…”
Vegetation classification method with biochemical composition estimated from remote sensing data (2011, unknown): “2002 , 2006 ), 708 and 860 nm (Datt 1998 ) bands were used.”
PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle (2017, methodology): “These methods included spectral indices and machine learning algorithms such as support vector regression (Gitelson et al., 2001, 2006, 2009; van den Berg and Perkins, 2005; Pfündel et al., 2007).”
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, methodology): “It is worth noting that these wavebands could also be optimized for a given situation following the procedure implemented by Clevers and Gitelson (2013), Gitelson et al. (2003, 2006), and Schlemmer et al. (2013).”