Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation

By Chaoyang Wu, Zheng Niu, Quan Tang, Wenjiang Huang. 2008

837 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/8dd9a14770c68214fe8436a89f433d5c26c6eb0a

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In Ecology

In Global and Planetary Change

In Plant Science

Citations in the corpus

Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages (2010, neutral): “705 )] Wu et al. (2008) OSAVI(705, 750) (1 ? 0.16)(R”
Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method (2016, background): “Chlorophyll content is an important indicator of crop productivity, photosynthetic capacity, nutrition level and health status (Mutanga, Skidmore, and Prins 2004; Wu et al. 2008; Houborg, Soegaard, and Boegh 2007).”
A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks (2018, background): “The croplands usually have high nitrogen/chlorophyll concentration that may not be fully captured by the four broad-band reflectances (Wu et al., 2008).”
Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices (2010, background): “Wu et al. (2008) proved that integrated indices transformed chlorophyll absorption ratio index (TCARI/OSAVI) [705,750] and MCARI/OSAVI [705,750] have better linearity with chlorophyll content together with high correlation: R2 of 0.8808 and 0.9406 respectively.”
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): “…as they were found to be accurate predictors of C ab at the leaf level (Gitelson et al., 2006, 2003, 1996; Gitelson and Merzlyak, 1994; Schlemmer et al., 2013) ) also hold a great potential for LAI prediction (Comar et al., 2012; Daughtry et al., 2000; Haboudane et al., 2004; Wu et al., 2008).”
Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments (2018, methodology): “…indices based on soil line (Rondeaux et al. 1996; Wu et al. 182 2008), chlorophyll absorption ratio indices (Kim et al. 1994; Daughtry et al. 2000; Wu et al. 2008; 183 Guan and Liu 2009) and integrated forms (Daughtry et al. 2000; Wu et al. 2008; Guan and Liu 184 2009) were evaluated in this study.”
Leaf chlorophyll content as a proxy for leaf photosynthetic capacity (2017, background): “At local scales, modelled ChlLeaf results have shown some success using empirical vegetation indices (Wu et al., 2008); however, the empirical nature of these relationships means that they have been difficult to apply over larger spatial extents, time intervals and different species (Croft et al.”
Vegetation classification method with biochemical composition estimated from remote sensing data (2011, methodology): “At present, biochemical composition estimation from remote-sensing data usually includes three main methods: sensitive spectral bands (Huang et al. 2004), vegetation index (Wu et al. 2008) and model inversion (Kempeneers et al. 2008).”
An investigation into robust spectral indices for leaf chlorophyll estimation (2011, background): “These indices have well researched combinations of wavebands that have evidently been selected in order to minimise LAI interference and pick out any changes in canopy chlorophyll, most times at low concentrations and/or against soil background (Daughtry et al., 2000; Haboudane et al., 2002; Wu et a”