Traditional genetic surveys—based on DNA sequencing or microsatellite analysis—are accurate but too costly and time-consuming for large-scale biodiversity monitoring. Remote sensing captures phenotypic traits shaped by both genes and environment, yet distinguishing genetically encoded signals from plastic responses has proven difficult. Environmental variability constantly modifies trait expression, masking the subtle spectral signatures that might reflect underlying genetic adaptation. Based on these challenges, there is a pressing need for in-depth research that systematically disentangles heritable spectral signals from environmental noise across natural populations.

A team of researchers from Yangzhou University (China), the University of Toronto Mississauga (Canada), and Agriculture and Agri-Food Canada (Canada) has published (DOI: 10.1016/j.plaph.2026.100168) a new study in Plant Phenomics in 2026, exploring whether hyperspectral reflectance can capture genetic diversity in wild bermudagrass (Cynodon dactylon) across Hainan Island, China. The team collected leaf and canopy spectra from 14 geographically diverse populations, combined with genome-wide single nucleotide polymorphism (SNP) data from over 75,000 markers, to test how spectral variation aligns with genetic divergence under both natural and controlled conditions.

The researchers collected three complementary spectral datasets: natural leaf spectra, natural canopy spectra, and leaf spectra from plants grown in a common garden experiment that minimized environmental variation. This multi-layered design allowed them to separate genetic effects from plastic responses. The key finding emerged in the shortwave infrared (SWIR) region between 1,900 and 2,400 nanometers—leaf spectral-genetic correlations reached r = 0.4 to 0.7 (P < 0.05) in this range, indicating that SWIR reflectance carries a robust heritable signal. Environmental factors including annual mean temperature, total precipitation, elevation, solar radiation, and water vapor content all showed significant linear correlations (r > 0.4, P < 0.05) with leaf spectral-genetic similarity, confirming that these SWIR bands reflect genuine genetic adaptation rather than mere phenotypic plasticity. In contrast, canopy-level spectra showed negligible genetic correlations (P > 0.05), as structural complexity and background interference diluted the signal. Using partial least squares discriminant analysis (PLS-DA), the team achieved 86.76% accuracy in classifying populations by their leaf spectra—substantially higher than the 41.74% accuracy from canopy data. Even when classifying samples into eight genetic clusters, leaf spectra reached 83.32% accuracy, confirming that leaf-level spectroscopy can distinguish genetically distinct groups in the field.

The authors said: "We were surprised to find that the SWIR region—often overlooked in favor of the more commonly studied visible and near-infrared bands—carries the clearest genetic signal. This makes sense optically: SWIR reflectance is sensitive to leaf water content, lignin, cellulose, and other structural compounds that are under stronger genetic control and less prone to short-term environmental fluctuations." They added: "Our common garden experiment was critical—it confirmed that these spectral patterns persist even when environmental noise is removed, which gives us confidence that we are truly seeing heritable traits, not just day-to-day plastic responses to weather or soil conditions."

The findings have direct implications for conservation and precision breeding. Leaf reflectance spectroscopy could enable rapid, non-destructive screening of genetic diversity in wild populations, helping identify resilient genotypes for restoration projects without the expense of full genomic sequencing. The study also provides a roadmap for prioritizing SWIR and near-infrared (NIR) bands in future remote sensing missions aimed at biodiversity monitoring. While canopy-level scaling remains challenging due to structural complexity, the strong leaf-level signal offers a practical foundation for drone- or ground-based surveys. As climate change accelerates, such tools could become essential for mapping adaptive genetic variation across landscapes and guiding conservation efforts where genetic resilience is most urgently needed.

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References

DOI

10.1016/j.plaphe.2026.100168

Original Source URL

https://doi.org/10.1016/j.plaphe.2026.100168

Funding information

Financing was granted by National Natural Science Foundation of China (32171672).

About Plant Phenomics

Plant Phenomics publishes breakthrough research that integrates genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics and computer science to advance plant phenotyping—from the cellular level to entire populations. The journal tackles key scientific challenges in phenomics by showcasing cuttingedge technologies for data acquisition, management, interpretation and modelling. Its scope spans practical applications across all plant sciences, aiming to translate novel phenotyping tools into realworld solutions for breeding, crop management and fundamental plant biology. By fostering interdisciplinary innovation, Plant Phenomics serves as a hub for researchers developing highthroughput, accurate and scalable methods to decipher complex plant traits, ultimately accelerating progress in agriculture and plant science.