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1. Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site

   https://doi.org/10.1002/ecs2.70257  

   Pau, Stephanie; Slapikas, Ryan; Ho, Che‐Ling; Bayliss, Shannon_L J; Donnelly, Ryan C; Abdullahi, Adam; Helliker, Brent R; Nippert, Jesse B; Riley, William J; Still, Christopher J; et al (April 2025, Ecosphere)

   Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C₃or C₄), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjustedR² = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjustedR² = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjustedR² = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies (R² = 0.65 to <0.01), butR²was >0.80 for certain lineages and sites. The relationship between Vc_max, a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients. 

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2. Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

   https://doi.org/10.1002/ppj2.20106  

   Tross, Michael_C; Grzybowski, Marcin_W; Jubery, Talukder_Z; Grove, Ryleigh_J; Nishimwe, Aime_V; Torres‐Rodriguez, J_Vladimir; Sun, Guangchao; Ganapathysubramanian, Baskar; Ge, Yufeng; Schnable, James_C (June 2024, The Plant Phenome Journal)

   Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize (Zea mays) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest. 

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3. From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance

   https://doi.org/10.1111/nph.16123  

   Serbin, Shawn P.; Wu, Jin; Ely, Kim S.; Kruger, Eric L.; Townsend, Philip A.; Meng, Ran; Wolfe, Brett T.; Chlus, Adam; Wang, Zhihui; Rogers, Alistair (September 2019, New Phytologist)

   Summary Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long‐standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking.We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m⁻². Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad‐ and needleleaf species, and upper‐ and lower‐canopy (i.e. sun and shade) growth environments.Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R² = 0.89; root mean square error (RMSE) = 15.45 g m⁻²).Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes. 

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4. Predicting Urban Trees’ Functional Trait Responses to Heat Using Reflectance Spectroscopy

   https://doi.org/10.3390/rs16132291  

   Kyaw, Thu Ya; Alonzo, Michael; Baker, Matthew E; Eisenman, Sasha W; Caplan, Joshua S (July 2024, Remote Sensing)

   Plant traits are often measured in the field or laboratory to characterize stress responses. However, direct measurements are not always cost effective for broader sampling efforts, whereas indirect approaches such as reflectance spectroscopy could offer efficient and scalable alternatives. Here, we used field spectroscopy to assess whether (1) existing vegetation indices could predict leaf trait responses to heat stress, or if (2) partial least squares regression (PLSR) spectral models could quantify these trait responses. On several warm, sunny days, we measured leaf trait responses indicative of photosynthetic mechanisms, plant water status, and morphology, including electron transport rate (ETR), photochemical quenching (qP), leaf water potential (Ψleaf), and specific leaf area (SLA) in 51 urban trees from nine species. Concurrent measures of hyperspectral leaf reflectance from the same individuals were used to calculate vegetation indices for correlation with trait responses. We found that vegetation indices predicted only SLA robustly (R2 = 0.55), while PLSR predicted all leaf trait responses of interest with modest success (R2 = 0.36 to 0.58). Using spectral band subsets corresponding to commercially available drone-mounted hyperspectral cameras, as well as those selected for use in common multispectral satellite missions, we were able to estimate ETR, qP, and SLA with reasonable accuracy, highlighting the potential for large-scale prediction of these parameters. Overall, reflectance spectroscopy and PLSR can identify wavelengths and wavelength ranges that are important for remote sensing-based modeling of important functional trait responses of trees to heat stress over broad ranges. 

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5. Using the rapid A‐C _i response (RACiR) in the Li‐Cor 6400 to measure developmental gradients of photosynthetic capacity in poplar

   https://doi.org/10.1111/pce.13436  

   Lawrence, Erica H.; Stinziano, Joseph R.; Hanson, David T. (October 2018, Plant, Cell & Environment)

   Abstract The rapid A‐C_iresponse (RACiR) technique alleviates limitations of measuring photosynthetic capacity by reducing the time needed to determine the maximum carboxylation rate (V_cmax) and electron transport rate (J_max) in leaves. Photosynthetic capacity and its relationships with leaf development are important for understanding ecological and agricultural productivity; however, our current understanding is incomplete. Here, we show that RACiR can be used in previous generation gas exchange systems (i.e., the LI‐6400) and apply this method to rapidly investigate developmental gradients of photosynthetic capacity in poplar. We compared RACiR‐determined V_cmaxand J_maxas well as respiration and stomatal conductance (g_s) across four stages of leaf expansion inPopulus deltoidesand the poplar hybrid 717‐1B4 (Populus tremula × Populus alba). These physiological data were paired with leaf traits including nitrogen concentration, chlorophyll concentrations, and specific leaf area. Several traits displayed developmental trends that differed between the poplar species, demonstrating the utility of RACiR approaches to rapidly generate accurate measures of photosynthetic capacity. By using both new and old machines, we have shown how more investigators will be able to incorporate measurements of important photosynthetic traits in future studies and further our understanding of relationships between development and leaf‐level physiology. 

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