ArticlePlant methods2018
Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model.
Article in Plant methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Morpho-physiological and biochemical responses and genomic stability of sweet corn (Zea Mays L. saccharata) to potassium ferrite nano-fertilizer.BMC plant biology · 2026Article
- DeepSpecN: A new hybrid method combining PROSPECT-PRO and Conv-Transformer to estimate leaf nitrogen content from leaf reflectance.Plant phenomics (Washington, D.C.) · 2025Article
- Article
- Bio-Master: Design and Validation of a High-Throughput Biochemical Profiling Platform for Crop Canopies.Plant phenomics (Washington, D.C.) · 2023Article
- The impact of nanofertilizer on agro-morphological criteria, yield, and genomic stability of common bean (Phaseolus vulgaris L.).Scientific reports · 2022Article
- Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield.Field crops research · 2022Article
- Revealing the Complex Relationship Among Hyperspectral Reflectance, Photosynthetic Pigments, and Growth in Norway Spruce Ecotypes.Frontiers in plant science · 2022Article
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6 authors.
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Abstract
backgroundLeaf biochemical composition corresponds to traits related to the plant state and its functioning. This study puts the emphasis on the main leaf absorbers: chlorophyll a and b ([Formula: see text]), carotenoids ([Formula: see text]), water ([Formula: see text]) and dry mater ([Formula: see text]) contents. Two main approaches were used to estimate [[Formula: see text] [Formula: see text], [Formula: see text], [Formula: see text]] in a non-destructive way using spectral measurements. The first one consists in building empirical relationships from experimental datasets using either the raw reflectances or their combination into vegetation indices (VI). The second one relies on the inversion of physically based models of leaf optical properties. Although the first approach is commonly used, the calibration of the empirical relationships is generally conducted over a limited dataset. Consequently, poor predictions may be observed when applying them on cases that are not represented in the training dataset, i.e. when dealing with different species, genotypes or under contrasted environmental conditions. The retrieval performances of the selected VIs were thus compared to the ones of four PROSPECT model versions based on reflectance data acquired at two phenological stages, over six wheat genotypes grown under three different nitrogen fertilizations and two sowing density modalities. Leaf reflectance was measured in the lab with a spectrophotometer equipped with an integrating sphere, the leaf being placed in front of a white Teflon background to increase the sensitivity to leaf biochemical composition. Destructive measurements of [[Formula: see text] [Formula: see text], [Formula: see text], [Formula: see text]] were performed concurrently.
resultsThe destructive measurements demonstrated that the carotenoid, [Formula: see text], and chlorophyll, [Formula: see text], contents were strongly correlated (r
conclusionsPROSPECT model inversion and empirical VI approach provide similar retrieval performances and are useful methods to estimate leaf biochemical composition from spectral measurements. However, the PROSPECT model inversion gives potential access to additional traits on surface reflectivity and leaf internal structure. This study suggests that non-destructive estimation of leaf chlorophyll and water contents is a relevant method to provide leaf traits with relatively high throughput.
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