ArticlePlant methods2023
Evaluating potential of leaf reflectance spectra to monitor plant genetic variation.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 31 citations in OpenAlex.
- Spectral network analysis illuminates coordinated plant traits across a climate gradient.The New phytologist · 2026Article
- VIS-NIR-SWIR Proximal Spectroradiometry Coupled with Machine Learning and Deep Learning for Ornamental Plant Identification and Classification.ACS omega · 2026Article
- Methodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.Plants (Basel, Switzerland) · 2026Article
- Handheld hyperspectral imaging dataset of annual sowthistle and little mallow under abiotic stress for machine learning.Data in brief · 2026Article
- Genome-wide associations of leaf spectral variation in MAGIC lines of Nicotiana attenuata.Ecology · 2026Article
- Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning.Scientific reports · 2026Article
- Assisting species differentiation and taxonomic classification by hyperspectral imaging: an example from the parasitic plant realm.Plant methods · 2026Article
- Intraspecific and intraindividual trait variability decrease with tree richness in a subtropical tree biodiversity experiment.Nature communications · 2025Article
- Needle- and Canopy-Level Genetic Variation in Scots Pine (Evolutionary applications · 2025Article
- Disentangling heritability and plasticity effects on Populus fremontii leaf reflectance across a temperature gradient.Oecologia · 2025Article
- Foliar spectral signatures reveal adaptive divergence in live oaks (Quercus section Virentes) across species and environmental niches.The New phytologist · 2025Article
- Spectral biology across scales in changing environments.Ecology · 2025Review
- Tree diversity shapes the spectral signature of light transmittance in developing forests.Ecology · 2025Article
- Hyperspectral Leaf Reflectance Detects Interactive Genetic and Environmental Effects on Tree Phenotypes, Enabling Large-Scale Monitoring and Restoration Planning Under Climate Change.Plant, cell & environment · 2025Article
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Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution ("hyperspectral") spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation-information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400-2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
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