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

The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air- and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, M area ), and isotopic composition (δ 15 N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle-leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7-4.4% for nitrogen ( N mass ), 42-54% for carbon ( C mass ), 17-58% for fiber (acid-digestible fiber, ADF), 7-44% for lignin (acid-digestible lignin, ADL), 3-31% for cellulose, 17-265 g/m 2 for M area , and −9.4‰ to 0.8‰ for δ 15 N. The calibrations were developed using a partial least-squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R 2 and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for N mass , R 2 = 0.77 (0.73) and RMSE = 0.88% (0.95%) for C mass , R 2 = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R 2 = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R 2 = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R 2 = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ 15 N, and R 2 = 0.88 (0.87) with RMSE = 17.2 g/m 2 (22.8 g/m 2 ) for M area . This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost-effective, easily replicated method.

• Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (Vc,max), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait–Vc,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 (Vc,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 Vc,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–Vc,max relationships across three forest biomes into a single robust model for Vc,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–Vc,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising Vc,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

More than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non‐destructive measurements on pressed, intact leaves, like those in herbarium collections. Using 618 samples from 68 species, we used partial least‐squares regression to build models linking pressed‐leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh‐ or ground‐leaf spectra of the same samples. The traits our pressed‐leaf models could estimate best were LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water‐related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514–0.790; %RMSE = 12.8–19.6). Remaining elements were predicted poorly (R2 < 0.5, %RMSE > 20). For most chemical traits, pressed‐leaf models performed better than fresh‐leaf models, but worse than ground‐leaf models. Pressed‐leaf models were worse than fresh‐leaf models for estimating LMA and LDMC, but better than ground‐leaf models for LMA. Finally, in a subset of samples, we used partial least‐squares discriminant analysis to classify specimens among 10 species with near‐perfect accuracy (>97%) from pressed‐ and ground‐leaf spectra, and slightly lower accuracy (>93%) from fresh‐leaf spectra. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed‐leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far‐reaching implications for capturing the wide range of functional and taxonomic information in the world’s preserved plant collections. Résumé Plus que jamais, les écologistes cherchent à utiliser des collections d'herbiers pour estimer les traits fonctionnels des plantes dans le passé et à travers des biomes. Cependant, plusieurs mesures de traits sont destructives et pourraient ne pas être effectuées sur des spécimens de grande valeur. De plus en plus, les chercheuses et chercheurs utilisent la spectroscopie de réflectance pour estimer des traits des feuilles fraîches ou broyées, et pour délimiter ou identifier les espèces. Nous étendons ici ces travaux en réalisant des mesures non‐destructives avec des feuilles entières et pressées. À partir de 618 échantillons provenant de 68 espèces, nous avons utilisé la régression des moindres carrés partiels pour construire des modèles liant les spectres de réflectance des feuilles pressées avec un large ensemble de traits, incluant la masse foliaire spécifique (‘leaf mass per area,’ LMA), la teneur en matière sèche des feuilles (‘leaf dry matter content,’ LDMC), l'épaisseur d'eau équivalente, les fractions de carbone, des pigments et douze éléments. Nous avons comparé ces modèles à ceux entraînés sur les spectres des feuilles fraîches ou broyées provenant des mêmes échantillons. Les traits les mieux estimés par nos modèles sur des feuilles pressées étaient la LMA (R2 = 0.932; %REQM = 6.56), le carbone (R2 = 0.855; %REQM = 9.03) et la cellulose (R2 = 0.803; %REQM = 12.2), suivis des traits liés à l'eau, de certains éléments nutritifs (Ca, Mg, N et P), des autres fractions de carbone et des pigments (tous les R2 = 0.514–0.790; %REQM = 12.8–19.6). Les autres éléments nutritifs ne pouvaient pas être bien estimés (R2 < 0.5, %RMSE >20). Pour la plupart des traits chimiques, les modèles sur des feuilles pressées étaient plus performants que ceux de feuilles fraîches, mais moins performants que ceux à partir de feuilles broyées. Les modèles sur des feuilles pressées performaient moins bien que ceux sur des feuilles fraîches pour estimer la LMA et la LDMC, mais performaient mieux que ceux sur des feuilles broyées pour la LMA. Finalement, pour un sous‐ensemble d'échantillons, nous avons utilisé l'analyse discriminante des moindres carrés partiels et réussi à classifier les spécimens parmi 10 espèces avec une précision presque parfaite (>97%) à partir des spectres des feuilles pressées ou broyées, et avec une précision légèrement plus basse (>93%) à partir de feuilles fraîches. Nos résultats démontrent que l'application de la spectroscopie sur des feuilles pressées est une approche non‐destructive prometteuse pour estimer des traits fonctionnels et pour identifier des espèces. Les spectres des feuilles pressées semblent combiner les avantages des feuilles fraîches et de celles broyées: comme les feuilles fraîches, elles conservent une partie de l'expression spectrale de la structure foliaire; comme les feuilles broyées, elles contournent l'effet masquant de l'absorption par l'eau. Notre étude a des implications importantes pour l'acquisition de données fonctionnelles et taxonomiques à partir des collections de plantes préservées à travers le monde.

Leaf age structures the phenology and development of plants, as well as the evolution of leaf traits over life histories. However, a general method for efficiently estimating leaf age across forests and canopy environments is lacking. Here, we explored the potential for a statistical model, previously developed for Peruvian sunlit leaves, to consistently predict leaf ages from leaf reflectance spectra across two contrasting forests in Peru and Brazil and across diverse canopy environments. The model performed well for independent Brazilian sunlit and shade canopy leaves (R 2 = 0.75–0.78), suggesting that canopy leaves (and their associated spectra) follow constrained developmental trajectories even in contrasting forests. The model did not perform as well for mid-canopy and understory leaves (R 2 = 0.27–0.29), because leaves in different environments have distinct traits and trait developmental trajectories. When we accounted for distinct environment–trait linkages – either by explicitly including traits and environments in the model, or, even better, by re-parameterizing the spectra-only model to implicitly capture distinct trait-trajectories in different environments – we achieved a more general model that well-predicted leaf age across forests and environments (R 2 = 0.79). Fundamental rules, linked to leaf environments, constrain the development of leaf traits and allow for general prediction of leaf age from spectra across species, sites and canopy environments.

Nitrogen is a major nutrient regulating the physiological processes of plants. Although various partial least squares regression (PLSR) models have been proposed to estimate the leaf nitrogen content (LNC) from hyperspectral data with good accuracies, they are unfortunately not robust and are often not applicable to novel datasets beyond which they were developed. Selecting informative bands has been reported to be critical to refining the performance of the PLSR model and improving its robustness for general applications. However, no consensus on the optimal band selection method has yet been reached because the calibration and validation datasets are very often limited to a few species with small sample sizes. In this study, we address the question based on a relatively comprehensive joint dataset, including a simulation dataset generated from the recently developed leaf scale radiative transfer model (PROSPECT-PRO) and two public online datasets, for assessing different informative band selection techniques on the informative band selection. The results revealed that the goodness-of-fit of PLSR models to estimate LNC could be greatly improved by coupling appropriate band-selection methods rather than using full bands instead. The PLSR models calibrated from the simulation dataset with informative bands selected by genetic algorithm (GA) and uninformative variable elimination (UVE) method were reliable for retrieving the LNC of the two independent field-measured datasets as well. Particularly, GA was more effective to capture the informative bands for retrieving LNC from hyperspectral data. These findings should provide valuable insights for building robust PLSR models for retrieving LNC from hyperspectral remote sensing data.

Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R2), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE–PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R2 = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE–PLS from all 501 wavebands over the 400–900 nm range. These findings suggest that ISE–PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.

Mango peel, a byproduct from the mango processing industry, is a potential source of food-grade mango peel pectin (MPP). Nonetheless, the influence of fruit physical characteristics and phytochemicals of peels on their correspondent pectin level has never been examined, particularly when high-quality food additives are of commercial need. Subsequently, the ultimate aim of the present study was to comprehend their relationship using chemometric data analyses as part of raw material sourcing criteria. Principal component analysis (PCA) advised that mangoes of ‘mahachanok’ and ‘nam dok mai’ could be distinguished from ‘chok anan’ and ‘kaew’ on the basis of physiology, peel morphology, and phytochemical characteristics. Only pectin extracted from mango var. ‘chok anan’ was classified as low-methoxyl type (Mox value ~4%). Using the partial least-squares (PLS) regression, the multivariate correlation between the fruit and peel properties and the degree of esterification (DE) value was reported at R2 > 0.9 and Q2 > 0.8. The coefficient factors illustrated that yields of byproducts such as seed and total biomass negatively influenced DE values, while they were positively correlated with crude fiber and xylose contents of the peels. Overall, it is interesting to highlight that, regardless of the differences in fruit varieties, the amount of biomass and peel proximate properties can be proficiently applied to establish classification of desirable properties of the industrial MPP.

Nanotechnology is currently applied in food processing and packaging in the food industry. Nano encapsulation techniques could improve sensory perception and nutrient absorption. The purpose of this study was to identify the sensory characteristics and consumer acceptability of three types of commercial and two types of laboratory-developed soy milk. A total of 20 sensory attributes of the five different soy milk samples, including appearance, smell (odor), taste, flavor, and mouthfeel (texture), were developed. The soy milk samples were evaluated by 100 consumers based on their overall acceptance, appearance, color, smell (odor), taste, flavor, mouthfeel (texture), goso flavor (nuttiness), sweetness, repeated use, and recommendation. One-way analysis of variance (ANOVA), principal component analysis (PCA), and partial least square regression (PLSR) were used to perform the statistical analyses. The SM_D sample generally showed the highest scores for overall liking, flavor, taste, mouthfeel, sweetness, repeated consumption, and recommendation among all the consumer samples tested. Consumers preferred sweet, goso (nuttiness), roasted soybean, and cooked soybean (nuttiness) attributes but not grayness, raw soybean flavor, or mouthfeel. Sweetness was closely related to goso (nuttiness) odor and roasted soybean odor and flavor based on partial least square regression (PLSR) analysis. Determination of the sensory attributes and consumer acceptance of soymilk provides insight into consumer needs and desires along with basic data to facilitate the expansion of the consumer market.

The mapping of soil nutrients is a key issue for numerous applications and research fields ranging from global changes to environmental degradation, from sustainable soil management to the precision agriculture concept. The characterization, modeling and mapping of soil properties at diverse spatial and temporal scales are key factors required for different environments. This paper is focused on the use and comparison of soil chemical analyses, Visible near infrared and shortwave infrared VNIR-SWIR spectroscopy, partial least-squares regression (PLSR), Ordinary Kriging (OK), and Landsat-8 operational land imager (OLI) images, to inexpensively analyze and predict the content of different soil nutrients (nitrogen (N), phosphorus (P), and potassium (K)), pH, and soil organic matter (SOM) in arid conditions. To achieve this aim, 100 surface samples of soil were gathered to a depth of 25 cm in the Wadi El-Garawla area (the northwest coast of Egypt) using chemical analyses and reflectance spectroscopy in the wavelength range from 350 to 2500 nm. PLSR was used firstly to model the relationship between the averaged values from the ASD spectroradiometer and the available N, P, and K, pH and SOM contents in soils in order to map the predicted value using Ordinary Kriging (OK) and secondly to retrieve N, P, K, pH, and SOM values from OLI images. Thirty soil samples were selected to verify the validity of the results. The randomly selected samples included the spatial diversity and characteristics of the study area. The prediction of available of N, P, K pH and SOM in soils using VNIR-SWIR spectroscopy showed high performance (where R2 was 0.89, 0.72, 0.91, 0.65, and 0.75, respectively) and quite satisfactory results from Landsat-8 OLI images (correlation R2 values 0.71, 0.68, 0.55, 0.62 and 0.7, respectively). The results showed that about 84% of the soils of Wadi El-Garawla are characterized by low-to-moderate fertility, while about 16% of the area is characterized by high soil fertility.