Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling

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Estimating seasonal variations of leaf area index using litterfall collection and optical methods in four mixed evergreen–deciduous forests (2015, background): “…total green leaf area per unit ground surface area (Chen and Black, 1992), is one of the most important plant canopy structural parameters that controls fluxes of carbon, energy, and water in terrestrial ecosystems (Weiss et al., 2004; Sonnentag et al., 2007; Behera et al., 2010; Ryu et al., 2012).”
Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions (2021, methodology): “In addition, LAI is a useful tool to evaluate the fraction of absorbed photosynthetically active radiation, which is required to model canopy photosynthesis [19].”
Improving leaf area index (LAI) estimation by correcting for clumping and woody effects using terrestrial laser scanning (2018, background): “, 2005), and also the theories behind these techniques are mature (Chen et al., 2006; Weiss et al., 2004).”
Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index (2018, methodology): “=P θ( ) nG nTi i i i gap, (B.2) The classification of pixels as gap or non-gap was done by thresholding the RGB image into a black (non-gap, nTi− nGi) and white (gap, nGi) image (Woodgate et al., 2015b; Thimonier and Sedivy, 2010; Weiss et al., 2004).”