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Effects of leaf age for four growth stages of cotton and corn plants on leaf reflectance, structure, thickness, water and chlorophyll concentrations and selection of wavelengths for crop discrimination.
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Inversion of vegetation canopy reflectance models for estimating agronomic variables. V - Estimation of leaf area index and average leaf angle using measured canopy reflectances
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Decoupling Contributions from Canopy Structure and Leaf Optics is Critical for Remote Sensing Leaf Biochemistry (Reply to Townsend, et al.)
Townsend et al. (1) agree that we explained that the apparent relationship (2) between foliar nitrogen (%N) and near-infrared (NIR) canopy reflectance was largely attributable to structure (which is in turn caused by variation in fraction of broadleaf canopy). Our conclusion that the observed correlation with %N was spurious (i.e., lacking a causal basis) is, thus, clearly justified: we demonstrated that structure explained the great majority of observed correlation, where the structural influence was derived precisely via reconciling the observed correlation with radiative-transfer theory. What this also suggests is that such correlations, although observed, do not uniquely provide information on canopy biochemical constituents.
Characterizing leaf-scale fluorescence with spectral invariants
Sun-induced chlorophyll fluorescence (SIF) is increasingly recognized as a non-destructive probe for tracking terrestrial photosynthesis. Emerging developments in spectral invariants theory provide an innovative and efficient approach for representing SIF radiative transfer processes at the canopy scale. However, modeling leaf-scale fluorescence based on the spectral invariants properties (SIP) remains underexplored. In this study, the spectral invariants theory is employed for the first time to model the leaf-scale total, backward and forward fluorescence (leaf-SIP SIF). The leaf-SIP SIF model separates the leaf-scale radiative transfer process into two distinct components: the wavelength-dependent one associated with leaf biochemical properties, and the wavelength-independent component linked to leaf structural characteristics. The leaf structure-related effects are characterized by two spectrally invariant parameters: the photon recollision probability (p) and the scattering asymmetry parameter (q), which are parameterized using the directly measurable leaf dry matter. Evaluation against field measurements shows that the proposed leaf-SIP SIF model has a good performance, with coefficient of determination (R 2 ) of 0.89, 0.89, 0.90 and root mean squared errors (RMSE) of 1.28, 0.69, 0.74 Wm -2 µm -1 sr -1 , respectively for the total, backward, and forward fluorescence (660–800 nm). The leaf-SIP SIF model with a more concise formulation demonstrates comparable performance with the widely used Fluspect model. Further, the leaf-SIP SIF model provides a simple and efficient approach for simulating leaf-scale fluorescence, with the potential to be integrated into a unified SIP-based model framework for simulating the radiative transfer processes across the soil-leaf-canopy-atmosphere continuum.
Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site
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 (C 3 or C 4 ), 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 (adjusted R 2 = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjusted R 2 = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjusted R 2 = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies ( R 2 = 0.65 to <0.01), but R 2 was >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.
Impact of vertical and seasonal variation in leaf traits on simulating soybean canopy photosynthesis via 1D and 3D modeling
Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax 25 ), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax 25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax 25 profile achieved comparable performance to the scheme with the measured Vcmax 25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R 2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R 2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.
Simulations of Seasonal and Latitudinal Variations in Leaf Inclination Angle Distribution: Implications for Remote Sensing
The leaf inclination angle distribution (LAD) is an important characteristic of vegetation canopy structure affecting light interception within the canopy. However, LADs are difficult and time consuming to measure. To examine possible global patterns of LAD and their implications in remote sensing, a model was developed to predict leaf angles within canopies. Canopies were simulated using the SAIL radiative transfer model combined with a simple photosynthesis model. This model calculated leaf inclination angles for horizontal layers of leaves within the canopy by choosing the leaf inclination angle that maximized production over a day in each layer. LADs were calculated for five latitude bands for spring and summer solar declinations. Three distinct LAD types emerged: tropical, boreal, and an intermediate temperate distribution. In tropical LAD, the upper layers have a leaf angle around 35 with the lower layers having horizontal inclination angles. While the boreal LAD has vertical leaf inclination angles throughout the canopy. The latitude bands where each LAD type occurred changed with the seasons. The different LADs affected the fraction of absorbed photosynthetically active radiation (fAPAR) and Normalized Difference Vegetation Index (NDVI) with similar relationships between fAPAR and leaf area index (LAI), but different relationships between NDVI and LAI for the different LAD types. These differences resulted in significantly different relationships between NDVI and fAPAR for each LAD type. Since leaf inclination angles affect light interception, variations in LAD also affect the estimation of leaf area based on transmittance of light or lidar returns.
Measurement of leaf relative water content by infrared reflectance
From basic considerations and Beer's law, a leaf water content index incorporating reflectances of wavelengths from 0.76 to 0.90 microns and from 1.55 to 1.75 microns was developed that relates leaf reflectance to leaf relative water content. For the leaf succulent, Agave deserti, the leaf water content index was not significantly different from the relative water content for either individual leaves or an entire plant. Also, the relative water contents of intact plants of Encelia farinosa and Hilaria rigida in the field were estimated by the leaf water content index; variations in the proportion of living to dead leaf area could cause large errors in the estimate of relative water content. Thus, the leaf water content index may be able to estimate average relative water content of canopies when TM4 and TM5 are measured at a known relative water content and fraction of dead leaf material.
Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical property
The spectral variation of canopy reflectance is mostly governed by the absorption properties of the elements, hence the leaves, since their intrinsic scattering properties show very little spectral variation. The relationship between canopy reflectance and leaf reflectance measured at the red edge over sugar beet canopies was used to simulate canopy reflectance from leaf reflectance spectra measured over the whole spectral domain. The results show that the spectral analogies found allows accurate reconstruction of canopy reflectance spectra. Explicit assumptions about the very low spectral variation of leaf intrinsic scattering properties are thus indirectly justified. The sensitivity of canopy reflectance (rho(sub c)) to leaf optical properties can then be investigated from concurrent spectral variations of canopy (delta rho(sub c)/delta lambda) and leaf reflectance (delta rho(sub l)/delta lambda): (delta rho(sub c))/(delta rho(sub l)) = ((delta rho(sub c))/(delta lambda) ((delta rho( sub l))/(delta lambda))(sup -1)). This expression is strictly valid only when the optical properties of the soil background or the other vegetation elements such as bark are either spectrally flat or do not contribute significantly to canopy reflectance. Simulations using the SAIL and PROSPECT models demonstrate that the sensitivity of canopy reflectance to leaf reflectance is significant for large vegetation cover fractions in spectral domains where absorption is low. In these conditions, multiple, scattering enhances the leaf absorption features by a factor that can be greater than 2.0. To override the limitations of the SAIL model for the description of the canopy architecture, we tested the previous findings on experimental data. Concurrent canopy and leaf reflectance spectra were measured for a range of sugar beet canopies. The results show good agreement with the theoretical findings. Conclusions are drawn about the applicability of these findings, with particular attention to the potential detectability of leaf biochemical composition from canopy reflectance sensed from space.
Leaf-level physiological strategies related to productivity and plasticity of Populus in the Southeastern United States
Introduction: Populus and its hybrids are attractive bioenergy crops and the southeastern United States has broad ability to supply bioenergy markets with woody biomass. Breeding and hybridization have led to superior eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars adapted to a wide variety of site types not suited for agricultural production. In order to maximize productivity and minimize inputs, genotypes need to efficiently use available site resources and tolerate environmental stresses. In addition, we need to determine plasticity of traits and their coordination across sites to select traits that will broadly characterize genotypes. Therefore, our study objectives were to determine (1) which leaf traits were correlated with growth, (2) if traits and genotypes exhibited significant plasticity across sites, and (3) how traits were coordinated within and across sites and Populus taxa. Methods: We measured trees at two sites in northeastern Mississippi, United States: one upland and one alluvial terrace site. Genotypes included eastern cottonwoods as well as F 1 crosses of eastern cottonwood and P. maximowiczii (Henry), P. nigra (L.) and P. trichocarpa (Torr. & Gray). Results: We found that sites differed in which leaf traits were correlated with productivity; with water use efficiency specifically being positively correlated with growth at an alluvial terrace site, but negatively correlated with growth at an upland site. Tree height growth, leaf isotope composition (δ 13 C and δ 15 N), as well as leaf mass per area (LMA) exhibited the least plasticity across sites, while physiological gas exchange parameters and leaf nitrogen concentration exhibited the highest plasticity. Broadly across taxa, leaf carbon isotope ratios were correlated with intrinsic water use efficiency, and stomatal conductance was positively correlated with photosynthetic nitrogen use efficiency across sites, while leaf nitrogen isotope ratios exhibited contrasting relationships with leaf nitrogen concentration. Discussion: Overall, these results allow us to refine selections of productive genotypes based on site conditions and site-specific relationships with physiological parameters to better match Populus taxa with sites and landowner objectives.
Using leaf and stomatal traits to predict biomass production and water use efficiency in Populus
Climate change is reshaping ecosystems, driving plants to adapt through leaf-trait plasticity that reflects strategies for growth and water use. Predicting biomass production and intrinsic water use efficiency (iWUE) remains challenging because of genetic, taxonomic, and environmental variability. Here, we used eastern cottonwood and Populus hybrids as a model system to test whether easily measurable leaf traits can serve as reliable predictors of performance, and whether adding stomatal and biochemical traits improves predictive power. Across two field sites in Mississippi, leaf mass per area (LMA), biomass production, iWUE, leaf area, and foliar nitrogen ( N %) differed significantly among taxa and sites, while other traits were conserved. Factorial analysis of mixed data (FAMD) revealed distinct clustering of taxa and sites, indicating coordinated variation among leaf and stomatal traits. Pairwise correlations highlighted fundamental trade-offs, with biomass positively related to LMA and petiole length but negatively associated with iWUE, N %, and carbon isotopic ratios (δ 13 C). Leaf temperature and leaf angle varied among taxa and were significantly correlated with LMA and petiole length, suggesting mechanisms of heat dissipation and leaf movability that link simple traits to gas exchange and productivity. Weighted multiple linear regression models explained 80%–91% of variation in biomass production and iWUE. Models using only LMA, petiole length, and stomatal metrics performed nearly as well as those incorporating N %, and δ 13 C, with complex traits adding approximately 10% explanatory power. These results demonstrate that simple morphological traits capture integrated functional trade-offs, while complex traits refine predictions. This tiered approach provides an efficient framework for selecting high-yielding, water-efficient genotypes of Populus and other hardwood species, offering practical pathways to enhance carbon uptake and iWUE under climate change.
Spectral reflectance relationships to leaf water stress
Spectral reflectance data were collected from detached snapbean leaves in the laboratory with a multiband radiometer. Four experiments were designed to study the spectral response resulting from changes in leaf cover, relative water content of leaves, and leaf water potential. Spectral regions included in the analysis were red (630-690 nm), NIR (760-900 nm), and mid-IR (2.08-2.35 microns). The red and mid-IR bands showed sensitivity to changes in both leaf cover and relative water content of leaves. The NIR was only highly sensitive to changes in leaf cover. Results provided evidence that mid-IR reflectance was governed primarily by leaf moisture content, although soil reflectance was an important factor when leaf cover was less than 100 percent. High correlations between leaf water potentials and reflectance were attributed to covariances with relative water content of leaves and leaf cover.
Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset
Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for >700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.
Unveiling the Transferability of PLSR Models for Leaf Trait Estimation: Lessons from a Comprehensive Analysis with a Novel Global Dataset
Leaf traits are essential for understanding many physiological and ecological processes. Partial least-squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47,393 records for >700 species and eight PFTs at 101 globally-distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55% and 7.0-33.12%) compared to nonspatial random cross-validation (NRCV). The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs and time due to biased validation schemes and provide guidance for future field sampling strategies and remote sensing applications.
Soft algebras for leaf amplitudes
Celestial MHV amplitudes are comprised of non-distributional leaf amplitudes associated to an AdS3 leaf of a foliation of flat spacetime. It is shown here that the leaf amplitudes are governed by the same infinite-dimensional soft ‘S-algebra’ as their celestial counterparts. Moreover, taking the soft limit of the smooth three-point MHV leaf amplitude yields a nondegenerate minus-minus two-point leaf amplitude. The two- and three-point MHV leaf amplitudes are used to compute the plus-minus-minus leaf operator product coefficients.
Genetic analyses of leaf traits in an interspecific Zoysia japonica × Zoysia matrella F2 population
Zoysiagrass (Zoysia spp.) is an important warm-season turfgrass cultivated across tropical, subtropical, and temperate regions of the world. The genus is characterized by the presence of salt-secreting glands on the adaxial leaf surface, which contribute to its high salt tolerance. In this study, we analyzed an interspecific F2 population, derived from selfing an F1 from a cross between Z. japonica acc. Meyer and Z. matrella acc. PI 231146, for variation in adaxial salt gland density, leaf width, and vein count. Using composite interval mapping with a previously constructed genetic map as a framework, we identified three quantitative trait loci (QTL) for leaf width, two QTL for vein count, and two QTL for salt gland density. We complemented the QTL analysis with bulked segregant RNA-seq (BSR-seq) to identify shared genomic regions and candidate genes for leaf width and salt gland density. BSR-seq identified four trait-associated regions, but only a single region identified for leaf width on Chr08 overlapped with a QTL for the same trait. We highlight putative candidate genes underlying the leaf width and salt gland density QTL and discuss their potential roles in leaf development. Together, the QTL and candidate genes provide an important resource for breeding stress-resilient Zoysia germplasm.
Changes in leaf economic trait relationships across a precipitation gradient are related to differential gene expression in a C 4 perennial grass
Summary The leaf economics spectrum (LES) describes a suite of functional traits that consistently covary at large spatial and taxonomic scales. Despite its importance at these larger scales, few studies have examined the major drivers of intraspecific variation in the LES – phenotypic plasticity and standing genetic variation. Using experimental precipitation manipulations, we examined whether covariation among leaf economics traits and selection on leaf economics traits and trait combinations change as diverse genotypes of the widespread perennial grass Panicum virgatum are exposed to differences in precipitation. We also used RNA‐Seq to examine whether groups of co‐expressed genes that align with leaf economics traits function in processes hypothesized to underlie the LES. Water availability impacted leaf economics trait covariation in important ways – covariation between leaf economics traits and selection on covariation between traits (i.e. correlational selection) tended to be strongest when water availability was high. Additionally, many genes associated with leaf economics traits functioned in processes that may explain how the LES originates, such as chloroplasts, cell walls, and nitrogen metabolism. Water availability is likely an important modulator of selection and evolution of the LES in P. virgatum that can be better understood by examining gene expression.