首页|Uncertainty in the evaluation of photosynthetic canopy traits using the green leaf area index

Uncertainty in the evaluation of photosynthetic canopy traits using the green leaf area index

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The green leaf area index (GLAI) has been widely used in agriculture, forestry, and environmental sciences for the analysis and modeling of many biophysical processes of vegetation, including the attenuation of light through the canopy, transpiration, photosynthesis, and carbon and nutrient cycles. Nevertheless, its usefulness is hampered by the uncertainty introduced through the lack of quantitative information on leaf biochemistry, particularly leaf chlorophyll content, in its computation. Thus far, this uncertainty has not been properly recognized nor quantified. The main goal of this study was to quantify the uncertainty of GLAI as used in the estimation of key photosynthetic canopy traits, namely canopy chlorophyll content (CCC). This uncertainty was assessed through the evaluation of the relationship between GLAI and CCC in structurally and functionally contrasting crop species (Zea mays L., Glycine max (L.) Merr., and Oryza sativa L). Results show that for the same GLAI value, CCC varied 2- to 3-fold due mainly to the variability of leaf chlorophyll content. Therefore, we suggest using the absorption coefficient in the red-edge region of the electromagnetic spectrum as an alternative to GLAI for the evaluation of CCC and other important photosynthetic canopy traits. The absorption coefficient in this spectral region is particularly suitable as it has been successfully related with the gross primary productivity of vegetation canopies, the quantum yield of photosynthesis, and is sensitive to the repositioning of chloroplasts within leaf cells in response to water stress.

Green leaf area indexChlorophyll contentAbsorptionReflectanceGROSS PRIMARY PRODUCTIVITYCHLOROPHYLL CONTENTVEGETATION INDEXESMAIZELAIEFFICIENCYINTERCEPTIONINDICATORSTRESS

Gitelson, Anatoly、Inoue, Yoshio、Arkebauer, Timothy、Schlemmer, Michael、Schepers, James

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Univ Nebraska Lincoln

Michigan State Univ

Univ N Carolina

Bayer CropSci

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2022

Agricultural and Forest Meteorology

Agricultural and Forest Meteorology

SCI
ISSN:0168-1923
年,卷(期):2022.320
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