Prediction of leaf area index in almonds by vegetation indexes

A simplified methodology for the correction of Leaf Area Index (LAI) measurements obtained by ceptometer with reference to Pinus Portuguese forests

“…and Zarate-Valdez et al (2012) refer to LAI as a dimensionless variable, which was originally defined as the total one-sided area of photosynthetic tissue per unit ground surface area. Gower & Norman (1991) defined LAI as the projected leaf area per unit ground area, which means half of the total leaf area per unit of ground surface (Chen & Cihlar 1995).…”

“…As reported by Zarate-Valdez et al (2012), this parameter is essential for modeling the processes occurring in the soil-plant-atmosphere continuum. LAI is highly related to rates of evapotranspiration and photosynthesis, forest production and site water balance (Larcher 1977).…”

Hemispherical photography to estimate biophysical variables of cotton

“…Despite this, it is important to notice that, in this study, even when LAI was estimated by destructive method, previously considered as standard, cotton yield did not present good relationships with LAI for both evaluation periods. Crop yield is directly related to the quantity and quality of the vegetation, and LAI can be used as a good yield estimator, being obtained by hemispherical images and used to assess forests and others vegetated covers, such as perennial crops (Jonckheere et al, 2005;Simões et al, 2007;Zarate-Valdez et al, 2012). Considering that the CI-110 measures the light intercepted by the canopy, which directly influences LAI and biomass, this method has also been used not only for the estimation of crop yield, but also for nutritional assessment (Zarco-Tejada et al, 2005;Glatthorn & Beckschäfer, 2014).…”

“…LAI enables the achievement of some canopy structural parameters, such as the aerial biomass as well as the primary productivity from the photosynthetically active surface (Chirici et al, 2007). Therefore, the reliable estimation capacity of this parameter can also contribute to assessing crop yields (MacFarlane et al, 2007;Zarate-Valdez et al, 2012).…”

The novel method for LAI inversion using Lidar and hyperspectral data

“…It could not only deal with the difficulty of Spectral discrimination (such as "the same thing with different spectrum, and the same spectrum but different thing"), but also was simple, feasible, and general [20]. Two main methods which establish the relationships between LAI and satellite observed spectral canopy reflectance are widely used for LAI retrieval from remote sensing data, including vegetation indexbased empirical regression method and physical model-based method [21,22] . Lefsky, et al (2001) pointed out that in spite of the laser radar being sample data, it will greatly enhance the forestry measurement by integrating other grid remote sensing data owing to the very high precision [23].…”