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

Deciphering the variability of leaf phosphorus-allocation strategies using leaf economic traits and reflectance spectroscopy across diverse forest types

Allocation of leaf phosphorus (P) among different functional fractions represents a crucial adaptive strategy for optimizing P use. However, it remains challenging to monitor the variability in leaf P fractions and, ultimately, to understand P-use strategies across diverse plant communities. We explored relationships between five leaf P fractions (orthophosphate P, Pi; lipid P, PL; nucleic acid P, PN; metabolite P, PM; and residual P, PR) and 11 leaf economic traits of 58 woody species from three biomes in China, including temperate, subtropical and tropical forests. Then, we developed trait-based models and spectral models for leaf P fractions and compared their predictive abilities. We found that plants exhibiting conservative strategies increased the proportions of PN and PM, but decreased the proportions of Pi and PL, thus enhancing photosynthetic P-use efficiency, especially under P limitation. Spectral models outperformed trait-based models in predicting cross-site leaf P fractions, regardless of concentrations (R2 = 0.50-0.88 vs 0.34-0.74) or proportions (R2 = 0.43-0.70 vs 0.06-0.45). These findings enhance our understanding of leaf P-allocation strategies and highlight reflectance spectroscopy as a promising alternative for characterizing large-scale leaf P fractions and plant P-use strategies, which could ultimately improve the physiological representation of the plant P cycle in land surface models.

JA, Larson A, Townsend PA, Montgomery R, Cavender‐Bares J, Reich PB. All the light we cannot see: Climate manipulations leave short and long-term imprints in spectral reflectance of trees

Anthropogenic climate change, particularly changes in temperature and precipitation, affects plants in multiple ways. Because plants respond dynamically to stress and acclimate to changes in growing conditions, diagnosing quantitative plant-environment relationships is a major challenge. One approach to this problem is to quantify leaf responses using spectral reflectance, which provides rapid, inexpensive, and nondestructive measurements that capture a wealth of information about genotype as well as phenotypic responses to the environment. However, it is unclear how warming and drought affect spectra. To address this gap, we used an open-air field experiment that manipulates temperature and rainfall in 36 plots at two sites in the boreal-temperate ecotone of northern Minnesota, USA. We collected leaf spectral reflectance (400-2400 nm) at the peak of the growing season for three consecutive years on juveniles (two to six years old) of five tree species planted within the experiment. We hypothesized that these mid-season measurements of spectral reflectance capture a snapshot of the leaf phenotype encompassing a suite of physiological, structural, and biochemical responses to both long- and short-time scale environmental conditions. We show that the imprint of environmental conditions experienced by plants hours to weeks before spectral measurements is linked to regions in the spectrum associated with stress, namely the water absorption regions of the near-infrared and short-wave infrared. In contrast, the environmental conditions plants experience during leaf development leave lasting imprints on the spectral profiles of leaves, attributable to leaf structure and chemistry (e.g., pigment content and associated ratios). Our analyses show that after accounting for baseline species spectral differences, spectral responses to the environment do not differ among the species. This suggests that building a general framework for understanding forest responses to climate change through spectral metrics may be possible, likely having broader implications if the common responses among species detected here represent a widespread phenomenon. Consequently, these results demonstrate that examining the entire spectrum of leaf reflectance for environmental imprints in contrast to single features (e.g., indices and traits) improves inferences about plant-environment relationships, which is particularly important in times of unprecedented climate change.

Linking leaf dark respiration to leaf traits and reflectance spectroscopy across diverse forest types

Leaf dark respiration (Rdark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (Vcmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed. Here, we analyzed Rdark variability and its associations with Vcmax and other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative. We found that leaf magnesium and calcium concentrations were more significant in explaining cross-site Rdark than commonly used traits like LMA, N and P concentrations, but univariate trait-Rdark relationships were always weak (r2 ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait-Rdark relationships, accurately predicted cross-site Rdark (r2 = 0.65) and pinpointed the factors contributing to Rdark variability. Our findings reveal a few novel traits with greater cross-site scalability regarding Rdark, challenging the use of empirical trait-Rdark relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating Rdark, which could ultimately improve process modeling of terrestrial plant respiration.

Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains

High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quantification of wheat nutrients with VIS-NIR (400-1700 nm) hyperspectral imaging is proposed in this study. Stepwise linear regression (SLR) was used to predict hundreds of nutrients accurately (R2 > 0.6); results improved when the hyperspectral data was processed with the first derivative. Knockout materials were also used to verify their practical application value. Various nutrients' characteristic wavelengths were mainly concentrated in the visible regions of 400-500 nm and 900-1000 nm. Finally, we proposed an improved pix2pix conditional generative network model to visualize the nutrients distribution and showed better results compared with the original. This research highlights the potential of hyperspectral technology in high-throughput and non-destructive determination and visualization of grain nutrients with deep learning.

Current and emerging tools and strategies for the identification of bioactive natural products in complex mixtures

Covering: up to 2024The prompt identification of (bio)active natural products (NPs) from complex mixtures poses a significant challenge due to the presence of numerous compounds with diverse structures and (bio)activities. Thus, this review provides an overview of current and emerging tools and strategies for the identification of (bio)active NPs in complex mixtures. Traditional approaches of bioassay-guided fractionation (BGF), followed by nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis for compound structure elucidation, continue to play an important role in the identification of active NPs. However, recent advances (2018-2024) have led to the development of novel techniques such as (bio)chemometric analysis, dereplication and combined approaches, which allow efficient prioritization for the elucidation of (bio)active compounds. For researchers involved in the search for bioactive NPs and who want to speed up their discoveries while maintaining accurate identifications, this review highlights the strengths and limitations of each technique and provides up-to-date insights into their combined use to achieve the highest level of confidence in the identification of (bio)active natural products from complex matrices.

Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet's randomized t-test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding R 2 [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments.

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 R2 (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.

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal

PREMISE

Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data.

METHODS

Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model.

RESULTS

Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species.

CONCLUSIONS

Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

Rapid quantification of biological nitrogen fixation using optical spectroscopy

Biological nitrogen fixation (BNF) provides a globally important input of nitrogen (N); its quantification is critical but technically challenging. Leaf reflectance spectroscopy offers a more rapid approach than traditional techniques to measure plant N concentration ([N]) and isotopes (δ15N). Here we present a novel method for rapidly and inexpensively quantifying BNF using optical spectroscopy. We measured plant [N], δ15N, and the amount of N derived from atmospheric fixation (Ndfa) following the standard traditional methodology using isotope ratio mass spectrometry (IRMS) from tissues grown under controlled conditions and taken from field experiments. Using the same tissues, we predicted the same three parameters using optical spectroscopy. By comparing the optical spectroscopy-derived results with traditional measurements (i.e. IRMS), the amount of Ndfa predicted by optical spectroscopy was highly comparable to IRMS-based quantification, with R2 being 0.90 (slope=0.90) and 0.94 (slope=1.02) (root mean square error for predicting legume δ15N was 0.38 and 0.43) for legumes grown in glasshouse and field, respectively. This novel application of optical spectroscopy facilitates BNF studies because it is rapid, scalable, low cost, and complementary to existing technologies. Moreover, the proposed method successfully captures the dynamic response of BNF to climate changes such as warming and drought.

Non-Destructive Monitoring of Foliar Fungicide Efficacy with Hyperspectral Sensing in Grapevine

Frequent fungicide applications are required to manage grapevine powdery mildew (Erysiphe necator). However, this practice is costly and has led to widespread fungicide resistance. A method of monitoring in-field fungicide efficacy could help growers maximize spray-interval length, thereby reducing costs and the rate of fungicide resistance emergence. The goal of this study was to evaluate if hyperspectral sensing in the visible to shortwave infrared range (400 to 2,400 nm) can quantify foliar fungicide efficacy on grape leaves. Commercial formulations of metrafenone, Bacillus mycoides isolate J (BmJ), and sulfur were applied on Chardonnay grapevines in vineyard or greenhouse settings. Foliar reflectance was measured with handheld hyperspectral spectroradiometers at multiple days post-application. Fungicide efficacy was estimated as a proxy for fungicide residue and disease control measured with the Blackbird microscopy imaging robot. Treatments could be differentiated from the untreated control with an accuracy of 73.06% for metrafenone, 67.76% for BmJ, and 94.10% for sulfur. The change in spectral reflectance was moderately correlated with the cube root of the area under the disease progress curve for metrafenone- and sulfur-treated samples (R2 = 0.38 and 0.36, respectively) and with sulfur residue (R2 = 0.42). BmJ treatment impacted foliar physiology by enhancing the leaf mass/area and reducing the nitrogen and total phenolic content as estimated from spectral reflectance. The results suggest that hyperspectral sensing can be used to monitor in-situ fungicide efficacy, and the prediction accuracy depends on the fungicide and the time point measured. The ability to monitor in-situ fungicide efficacy could facilitate more strategic fungicide applications and promote sustainable grapevine protection. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Hyperspectral Proximal Sensing for Estimating Photosynthetic Capacities at Leaf and Canopy Scales

Agronomists, plant breeders, and plant biologists have been promoting the need to develop high-throughput methods to measure plant traits of interest for decades. Measuring these plant traits or phenotypes is often a bottleneck since skilled personnel, resources, and ample time are required. Additionally, plant phenotypic traits from only a select number of breeding lines or varieties can be quantified because the "gold standard" measurement of a desired trait cannot be completed in a timely manner. As such, numerous approaches have been developed and implemented to better understand the biology and production of crops and ecosystems. In this chapter, we explain one of the recent approaches leveraging hyperspectral measurements to estimate different aspects of photosynthesis. Notably, we outline the use of hyperspectral radiometer and imaging to rapidly estimate two of the rate-limiting steps of photosynthesis: the maximum rate of the carboxylation of Rubisco (Vcmax) and the maximum rate of electron transfer or regeneration of RuBP (Jmax).

Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue

This article is an Editorial to the Virtual issue on ‘Remote sensing’ that includes the following papers Chavana‐Bryant et al. (2017), Coupel‐Ledru et al. (2022), Cushman & Machado (2020), Disney (2019), D'Odorico et al. (2020), Dong et al. (2022), Fischer et al. (2019), Gamon et al. (2023), Gu et al. (2019), Guillemot et al. (2020), Jucker (2021), Koh et al. (2022), Konings et al. (2019), Kothari et al. (2023), Martini et al. (2022), Richardson (2019), Santini et al. (2021), Schimel et al. (2019), Serbin et al. (2019), Smith et al. (2019, 2020), Still et al. (2021), Stovall et al. (2021), Wang et al. (2020), Wong et al. (2020), Wu et al. (2021), Wu et al. (2017), Wu et al. (2018), Wu et al. (2019), Xu et al. (2021), Yan et al. (2021). Access the Virtual Issue at www.newphytologist.com/virtualissues.

Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common-garden trials of interior spruce (Picea engelmannii × glauca)

Common-garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common-garden trials of interior spruce (Picea engelmannii × glauca) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height (R2  = .98-.99, root mean square error [RMSE] = 0.06-0.10 m) and diameter at breast height (DBH, R2  = .71-.97, RMSE = 2.57-3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant (p < .05) clines were present for spectral traits at all sites along all PCs. Spectral traits showed stronger clinal variation than structural traits along temperature and elevational gradients and along moisture gradients at wet, coastal sites, but not at dry, interior sites. Spectral traits may capture patterns of local adaptation to temperature and montane growing seasons which are distinct from moisture-limited patterns in stem growth. This work demonstrates that multispectral indices improve the assessment of local adaptation and that spectral and structural traits from drone remote sensing produce reliable proxies for ground-measured height and DBH. This phenotyping framework contributes to the analysis of common-garden trials towards a mechanistic understanding of local adaptation to climate.

Scalable Early Detection of Grapevine Viral Infection with Airborne Imaging Spectroscopy

The U.S. wine and grape industry loses $3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3). Current detection methods are labor-intensive and expensive. GLRaV-3 has a latent period in which the vines are infected but do not display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. The NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation was deployed to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon after image acquisition. In September of both 2020 and 2021, industry collaborators scouted 317 hectares on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Symptomatic grapevines identified in 2021 were assumed to have been latently infected at the time of image acquisition. Random forest models were trained on a spectroscopic signal of noninfected and GLRaV-3 infected grapevines balanced with synthetic minority oversampling of noninfected and GLRaV-3 infected grapevines. The models were able to differentiate between noninfected and GLRaV-3 infected vines both pre- and postsymptomatically at 1 to 5 m resolution. The best-performing models had 87% accuracy distinguishing between noninfected and asymptomatic vines, and 85% accuracy distinguishing between noninfected and asymptomatic + symptomatic vines. The importance of nonvisible wavelengths suggests that this capacity is driven by disease-induced changes to plant physiology. The results lay a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring in grapevine and other crop species. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

The effect of the vertical gradients of photosynthetic parameters on the CO2 assimilation and transpiration of a Panamanian tropical forest

Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. We compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama and then quantified the impact of the observed gradients on simulated canopy-scale CO₂ and water fluxes. Comparison between observed and TBM trait gradients showed divergence that impacted canopy-scale simulations of water vapor and CO₂ exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top-of-canopy, leaf-level water-use efficiency was markedly higher at the top-of-canopy, and the decrease in maximum carboxylation rate from the top-of-canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within-individual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests.

Predicting leaf traits across functional groups using reflectance spectroscopy

Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R²  > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R²  = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology

BACKGROUND

Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF).

RESULTS

We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions.

CONCLUSIONS

TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.

Plastic response of leaf traits to N deficiency in field-grown maize

Nitrogen (N) utilization for crop production under N deficiency conditions is subject to a trade-off between maintaining specific leaf N content (SLN) important for radiation-use efficiency versus maintaining leaf area (LA) development, important for light capture. This paper aims to explore how maize deals with this trade-off through responses in SLN, LA and their underlying traits during the vegetative and reproductive growth stages. In a 10-year N fertilization trial in Jilin province, Northeast China, three N fertilizer levels have been maintained: N deficiency (N0), low N supply (N1) and high N supply (N2). We analysed data from years 8 and 10 of this experiment for two common hybrids. Under N deficiency, maize plants maintained LA and decreased SLN during vegetative stages, while both LA and SLN decreased comparably during reproductive stages. Canopy SLA (specific leaf area, cm² g^-1) decreased sharply during vegetative stages and slightly during reproductive stages, mainly because senesced leaves in the lower canopy had a higher SLA. In the vegetative stage, maize maintained LA at low N by maintaining leaf biomass (albeit hence having N content/mass) and slightly increasing SLA. These responses to N deficiency were stronger in maize hybrid XY335 than in ZD958. We conclude that the main strategy of maize to cope with low N is to maintain LA, mainly by increasing SLA throughout the plant but only during the vegetative growth phase.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Leaf spectroscopy reveals divergent inter- and intra-species foliar trait covariation and trait-environment relationships across NEON domains

Concurrent measurement of multiple foliar traits to assess the full range of trade-offs among and within taxa and across broad environmental gradients is limited. Leaf spectroscopy can quantify a wide range of foliar functional traits, enabling assessment of interrelationships among traits and with the environment. We analyzed leaf trait measurements from 32 sites along the wide eco-climatic gradient encompassed by the US National Ecological Observatory Network (NEON). We explored the relationships among 14 foliar traits of 1103 individuals across and within species, and with environmental factors. Across all species pooled, the relationships between leaf economic traits (leaf mass per area, nitrogen) and traits indicative of defense and stress tolerance (phenolics, nonstructural carbohydrates) were weak, but became strong within certain species. Elevation, mean annual temperature and precipitation weakly predicted trait variation across species, although some traits exhibited species-specific significant relationships with environmental factors. Foliar functional traits vary idiosyncratically and species express diverse combinations of leaf traits to achieve fitness. Leaf spectroscopy offers an effective approach to quantify intra-species trait variation and covariation, and potentially could be used to improve the characterization of vegetation in Earth system models.

Sources and consequences of mismatch between leaf disc and whole-leaf leaf mass per area (LMA)

PREMISE

Leaf mass per area (LMA), which is an important functional trait in leaf economic spectrum and plant growth analysis, is measured from leaf discs or whole leaves. Differences between the measurement methods may lead to large differences in the estimates of LMA values.

METHODS

We examined to what extent estimates of LMA based on whole leaves match those based on discs using 334 woody species from a wide range of biomes (tropics, subtropics, savanna, and temperate), whether the relationship varied by leaf morphology (tissue density, leaf area, leaf thickness), punch size (0.6- and 1.0-cm diameter), and whether the extent of intraspecifc variation for each species matches.

RESULTS

Disc-based estimates of species mean LMA matched the whole-leaf estimates well, and whole-leaf LMA tended to be 9.69% higher than leaf-disc LMA. The ratio of whole-leaf LMA to leaf-disc LMA was higher for species with higher leaf tissue density and larger leaves, and variance in the ratio was greater for species with lower leaf tissue density and thinner leaves. Estimates based on small leaf discs also inflated the ratio. The extent of the intraspecific variation only weakly matched between whole-leaf and disc-based estimates (R²  = 0.08).

CONCLUSIONS

Our results suggest that simple conversion between whole-leaf and leaf-disc LMA is difficult for species obtained with a small leaf punch, but it should be possible for species obtained with a large+ leaf punch. Accurately representing leaf traits will likely require careful selection between leaf-disc and whole-leaf traits depending on the objectives. Quantifying intraspecific variation using leaf discs should be also considered with caution.

Can we improve the chilling tolerance of maize photosynthesis through breeding?

Chilling tolerance is necessary for crops to thrive in temperate regions where cold snaps and lower baseline temperatures place limits on life processes; this is particularly true for crops of tropical origin such as maize. Photosynthesis is often adversely affected by chilling stress, yet the maintenance of photosynthesis is essential for healthy growth and development, and most crucially for yield. In this review, we describe the physiological basis for enhancing chilling tolerance of photosynthesis in maize by examining nine key responses to chilling stress. We synthesize current knowledge of genetic variation for photosynthetic chilling tolerance in maize with respect to each of these traits and summarize the extent to which genetic mapping and candidate genes have been used to understand the genomic regions underpinning chilling tolerance. Finally, we provide perspectives on the future of breeding for photosynthetic chilling tolerance in maize. We advocate for holistic and high-throughput approaches to screen for chilling tolerance of photosynthesis in research and breeding programmes in order to develop resilient crops for the future.

Digital plant pathology: a foundation and guide to modern agriculture

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

Linking Land Use and Plant Functional Diversity Patterns in Sabah, Borneo, through Large-Scale Spatially Continuous Sentinel-2 Inference

Global biodiversity losses erode the functioning of our vital ecosystems. Functional diversity is increasingly recognized as a critical link between biodiversity and ecosystem functioning. Satellite earth observation was proposed to address the current absence of information on large-scale continuous patterns of plant functional diversity. This study demonstrates the inference and spatial mapping of functional diversity metrics through satellite remote sensing over a large key biodiversity region (Sabah, Malaysian Borneo, ~53,000 km2) and compares the derived estimates across a land-use gradient as an initial qualitative assessment to test the potential merits of the approach. Functional traits (leaf water content, chlorophyll-a and -b, and leaf area index) were estimated from Sentinel-2 spectral reflectance using a pre-trained neural network on radiative transfer modeling simulations. Multivariate functional diversity metrics were calculated, including functional richness, divergence, and evenness. Spatial patterns of functional diversity were related to land-use data distinguishing intact forest, logged forest, and oil palm plantations. Spatial patterns of satellite remotely sensed functional diversity are significantly related to differences in land use. Intact forests, as well as logged forests, featured consistently higher functional diversity compared to oil palm plantations. Differences were profound for functional divergence, whereas functional richness exhibited relatively large variances within land-use classes. By linking large-scale patterns of functional diversity as derived from satellite remote sensing to land-use information, this study indicated initial responsiveness to broad human disturbance gradients over large geographical and spatially contiguous extents. Despite uncertainties about the accuracy of the spatial patterns, this study provides a coherent early application of satellite-derived functional diversity toward further validation of its responsiveness across ecological gradients.

Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck

To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R ² = 0.48, RMSE = 5.24 μmol m^-2 s^-1 and stomatal conductance: R ² = 0.36, RMSE = 0.14 mol m^-2 s^-1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R ² = 0.3, p < 0.05; R ² = 0.29, p < 0.05) and at 7 days after anthesis (R ² = 0.15, p < 0.05; R ² = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO₂ . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

Plant Disease Sensing: Studying Plant-Pathogen Interactions at Scale

Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste. The nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease, offers great promise to extend monitoring to previously unachievable resolutions, a basis to construct multiscale surveillance networks for early warning, alert, and response at low latency, an opportunity to mitigate loss while optimizing protection, and a dynamic new dimension to agricultural systems biology. Despite its revolutionary potential, plant disease sensing remains an underdeveloped discipline, with challenges facing both fundamental study and field application. This article offers a perspective on the current state and future of plant disease sensing, highlights remaining gaps to be filled, and presents a bold vision for the future of global agriculture.

Reading light: leaf spectra capture fine-scale diversity of closely related, hybridizing arctic shrubs

Leaf reflectance spectroscopy is emerging as an effective tool for assessing plant diversity and function. However, the ability of leaf spectra to detect fine-scale plant evolutionary diversity in complicated biological scenarios is not well understood. We test if reflectance spectra (400-2400 nm) can distinguish species and detect fine-scale population structure and phylogenetic divergence - estimated from genomic data - in two co-occurring, hybridizing, ecotypically differentiated species of Dryas. We also analyze the correlation among taxonomically diagnostic leaf traits to understand the challenges hybrids pose to classification models based on leaf spectra. Classification models based on leaf spectra identified two species of Dryas with 99.7% overall accuracy and genetic populations with 98.9% overall accuracy. All regions of the spectrum carried significant phylogenetic signal. Hybrids were classified with an average overall accuracy of 80%, and our morphological analysis revealed weak trait correlations within hybrids compared to parent species. Reflectance spectra captured genetic variation and accurately distinguished fine-scale population structure and hybrids of morphologically similar, closely related species growing in their home environment. Our findings suggest that fine-scale evolutionary diversity is captured by reflectance spectra and should be considered as spectrally-based biodiversity assessments become more prevalent.

Leaf reflectance can surrogate foliar economics better than physiological traits across macrophyte species

BACKGROUND

Macrophytes are key players in aquatic ecosystems diversity, but knowledge on variability of their functional traits, among and within species, is still limited. Remote sensing is a high-throughput, feasible option for characterizing plant traits at different scales, provided that reliable spectroscopy models are calibrated with congruous empirical data, but existing applications are biased towards terrestrial plants. We sampled leaves from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). We explored optimal spectral band combinations and established non-parametric reflectance-based models for selected traits, eventually showing how airborne hyperspectral data could capture spatial-temporal macrophyte variability.

RESULTS

Our key finding is that structural-leaf dry matter content, leaf mass per area-and biochemical-chlorophyll-a content and chlorophylls to carotenoids ratio-traits can be surrogated by leaf reflectance with normalized error under 17% across macrophyte species. On the other hand, the performance of reflectance-based models for photophysiological traits substantively varies, depending on macrophyte species and target parameters.

CONCLUSIONS

Our main results show the link between leaf reflectance and leaf economics (structure and biochemistry) for aquatic plants, thus envisioning a crucial role for remote sensing in enhancing the level of detail of macrophyte functional diversity analysis to intra-site and intra-species scales. At the same time, we highlighted some difficulties in establishing a general link between reflectance and photosynthetic performance under high environmental heterogeneity, potentially opening further investigation directions.

Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy

Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO₂ absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO₂ absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO₂ exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO₂ fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration (R_dark25), the maximum rate of carboxylation by the enzyme Rubisco (V_cmax25), the maximum rate of electron transport (J_max25), and the triose phosphate utilization rate (T_p25), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for V_cmax25, J_max25, T_p25 and R_dark25 (RMSE of 13, 20, 1.5 and 0.3 μmol m^-2 s^-1, and R² of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V_c,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V_c,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V_c,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V_c,max and the four morphological and biochemical traits (R²  ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V_c,max relationships across three forest biomes into a single robust model for V_c,max (R²  = 0.77), and also accurately estimated the four traits (R²  = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V_c,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V_c,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression

Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.

Detection of the metabolic response to drought stress using hyperspectral reflectance

Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.

Coupling Relationship of Leaf Economic and Hydraulic Traits of Alhagisparsifolia Shap. in a Hyper-Arid Desert Ecosystem

In this study, Alhagisparsifolia Shap. was used to test the hypothesis that leaf economic and hydraulic traits are coupled in plants in a hyper-arid region. Five economic traits and six hydraulic traits were examined to explore the relationship. Results showed that the stomatal density (SD) on both surfaces was coupled with maximum stomatal conductance to water vapor (g_wmax) and leaf tissue density (TD). SD on adaxial surface (SD_aba) was significantly positively related to vein density (VD) but negatively related to leaf thickness (LT) and stomatal length on adaxial surface (SL_ada). Nitrogen concentration based on mass (N_mass) was significantly negatively correlated with leaf mass per area (LMA), LT, and VD, whereas nitrogen concentration based on area (N_area) was significantly positively related to LMA and TD. Mean annual precipitation (MAP) contributed the most to the changes in LT and stomatal length (SL). Soil salt contributed the most to TD, SD, and g_wmax. Soli nutrients influenced the most of LMA and VD. Mean annual temperature contributed the most to N_mass and N_area. In conclusion, the economics of leaves coupled with their hydraulic traits provides an economical and efficient strategy to adapt to the harsh environment in hyper-arid regions.

Impact of Modeling Abstractions When Estimating Leaf Mass per Area and Equivalent Water Thickness over Sparse Forests Using a Hybrid Method

Equivalent water thickness (EWT) and leaf mass per area (LMA) are important indicators of plant processes, such as photosynthetic and potential growth rates and health status, and are also important variables for fire risk assessment. Retrieving these traits through remote sensing is challenging and often requires calibration with in situ measurements to provide acceptable results. However, calibration data cannot be expected to be available at the operational level when estimating EWT and LMA over large regions. In this study, we assessed the ability of a hybrid retrieval method, consisting of training a random forest regressor (RFR) over the outputs of the discrete anisotropic radiative transfer (DART) model, to yield accurate EWT and LMA estimates depending on the scene modeling within DART and the spectral interval considered. We show that canopy abstractions mostly affect crown reflectance over the 0.75–1.3 μm range. It was observed that excluding these wavelengths when training the RFR resulted in the abstraction level having no effect on the subsequent LMA estimates (RMSE of 0.0019 g/cm2 for both the detailed and abstract models), and EWT estimates were not affected by the level of abstraction. Over AVIRIS-Next Generation images, we showed that the hybrid method trained with a simplified scene obtained accuracies (RMSE of 0.0029 and 0.0028 g/cm2 for LMA and EWT) consistent with what had been obtained from the test dataset of the calibration phase (RMSE of 0.0031 and 0.0032 g/cm2 for LMA and EWT), and the result yielded spatially coherent maps. The results demonstrate that, provided an appropriate spectral domain is used, the uncertainties inherent to the abstract modeling of tree crowns within an RTM do not significantly affect EWT and LMA accuracy estimates when tree crowns can be identified in the images.

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

Current and near-term advances in Earth observation for ecological applications

There is an unprecedented array of new satellite technologies with capabilities for advancing our understanding of ecological processes and the changing composition of the Earth's biosphere at scales from local plots to the whole planet. We identified 48 instruments and 13 platforms with multiple instruments that are of broad interest to the environmental sciences that either collected data in the 2000s, were recently launched, or are planned for launch in this decade. We have restricted our review to instruments that primarily observe terrestrial landscapes or coastal margins and are available under free and open data policies. We focused on imagers that passively measure wavelengths in the reflected solar and emitted thermal spectrum. The suite of instruments we describe measure land surface characteristics, including land cover, but provide a more detailed monitoring of ecosystems, plant communities, and even some species then possible from historic sensors. The newer instruments have potential to greatly improve our understanding of ecosystem functional relationships among plant traits like leaf mass area (LMA), total nitrogen content, and leaf area index (LAI). They provide new information on physiological processes related to photosynthesis, transpiration and respiration, and stress detection, including capabilities to measure key plant and soil biophysical properties. These include canopy and soil temperature and emissivity, chlorophyll fluorescence, and biogeochemical contents like photosynthetic pigments (e.g., chlorophylls, carotenoids, and phycobiliproteins from cyanobacteria), water, cellulose, lignin, and nitrogen in foliar proteins. These data will enable us to quantify and characterize various soil properties such as iron content, several types of soil clays, organic matter, and other components. Most of these satellites are in low Earth orbit (LEO), but we include a few in geostationary orbit (GEO) because of their potential to measure plant physiological traits over diurnal periods, improving estimates of water and carbon budgets. We also include a few spaceborne active LiDAR and radar imagers designed for quantifying surface topography, changes in surface structure, and 3-dimensional canopy properties such as height, area, vertical profiles, and gap structure. We provide a description of each instrument and tables to summarize their characteristics. Lastly, we suggest instrument synergies that are likely to yield improved results when data are combined.

Foliar functional traits from imaging spectroscopy across biomes in eastern North America

Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1-19.4%; coefficient of determination, 0.28-0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28-81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental-scale functional diversity.

A multi-omics approach to solving problems in plant disease ecology

The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions.