Leaf Reflectance Research Articles

Using hyperspectral leaf reflectance to estimate photosynthetic capacity and nitrogen content across eastern cottonwood and hybrid poplar taxa.

Eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performing Populus genotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (Vcmax), electron transport-limited carboxylation rate (Jmax), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (Narea), and leaf reflectance of seven taxa and 62 genotypes of Populus from two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except for Narea in which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimating Vcmax (R2 = 0.51 and RMSPE = 31%) and Jmax (R2 = 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R2 = 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimating Narea (R2 = 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimating Vcmax and Jmax. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes because of its capacity to estimate these physicochemical parameters.

Rapid quantification of alkaloids, sugar and yield of tobacco (Nicotiana tabacum L.) varieties by using Vis–NIR–SWIR spectroradiometry

Tobacco genetic improvement programs, as well as the tobacco industry, require techniques that allow the estimation of its attributes in a fast and cheap way. The use of remote sensing through visible, near infrared and short-wave spectroscopy (Vis-NIR-SWIR) has been studied aiming to meet such demand. Thus, the aim of this work was to evaluate the use of Vis-NIR-SWIR spectroradiometer as a rapid tool to estimate alkaloids, sugars and yield of tobacco varieties. For that purpose, a study was carried out in a greenhouse with plants grown in pots (18 dm−3) containing nutrient solutions. The experimental design was completely randomized, with 30 treatments (tobacco varieties) and 10 repetitions. Tobacco leaf reflectance was collected at 13, 34 and 68 days after transplantation (DAT) with a plant-probe device connected to the spectroradiometer by an optical fiber. Subsequently, leaf analysis of alkaloids, sugars and yield were performed, and such attributes were estimated by using the Partial Least Squares Regression (PLSR), combined with the following pre-processing (PP) techniques: multiplicative scatter correction (MSC), Savitzky-Golay (SG) and standard normal variate (SNV). The results showed presence of typical inflections of chemical and structural components of the plants, which allowed obtaining PLSR models with R2p and RPDp superior to 0.71 and 2.27, respectively, for all PP techniques and attributes evaluated. The most important wavelengths were well distributed within the three operating ranges of the spectroradiometer (Vis-NIR-SWIR). Thus, the methodology proposed by this research was able to simultaneously determine all the three attributes (alkaloids, sugars and yield) with excellent predictive capacity. This is a promising result for genetic improvement and processing of tobacco (as well as other crops), since it is necessary to evaluate a large number of samples within a short period and at a low cost.

A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance

Forest leaf chlorophyll (Cab) and carotenoid (Cxc) are key functional indicators for the state of the forest ecosystem. Current machine learning models based on hyperspectral reflectance are widely applied to estimate leaf Cab and Cxc contents at leaf scale. However, these models have certain accuracy for non-independent datasets but have poor generalization for independent datasets when they are used to estimate leaf Cab and Cxc contents. This fact limits that hyperspectral remote sensing completely replaces destructive measurements for leaf Cab and Cxc contents. Thus, the development of an estimation model with high accuracy and satisfactory generalization is necessary. Convolutional neural networks (CNNs) have certain accuracy and generalization in many domains, and have the potential to solve above-mentioned problem. Therefore, this study developed a CNN using one-dimensional hyperspectral reflectance, which aimed to improve the model's accuracy and generalization in leaf Cab and Cxc content estimation at leaf scale. The proposed CNN was developed by three steps. First, in consideration of the correlation between leaf Cab and Cxc contents in natural leaves, 2500 physical data with leaf reflectance and corresponding Cab and Cxc contents were generated by leaf radiative transfer model and multivariable gaussian distribution function. Then, the proposed CNN was built by five strategies based on the architecture of the AlexNet. Finally, five-fold cross validation was performed with 70% of the physical data to determine the best strategy to develop the proposed CNN. These were executed to ensure the proposed CNN with the maximum accuracy and generalization. In addition, the accuracy and generalization of the proposed CNN were tested using a non-independent dataset and an independent dataset, respectively. The proposed CNN was also compared with back propagation neural network (BPNN), support vector regression (SVR) and gaussian process regression (GPR). Results showed that the best CNN could be developed with one input, five convolutional, three max-pooling and three fully-connected layers. Comprehensively considering the model's accuracy and generalization, the proposed CNN was the best model for leaf Cab and Cxc content estimation compared with BPNN, SVR and GPR. This study provides a development strategy of CNN estimation model using one-dimensional hyperspectral reflectance at leaf scale. The proposed CNN could further promote the practical application of hyperspectral remote sensing in leaf Cab and Cxc content estimation.

Variations in accuracy of leaf functional trait prediction due to spectral mixing

Associations between leaf chemicals and reflectance values using spectroscopy enables the modelling, and prediction of, individual functional traits. Prediction accuracy is generally high when reflectance values are acquired from a single individual or species, however when the spectroscopy field of view is extended to include multiple species, the effects on predictive accuracy are less clear.Leaf spectroscopy of 23 plant species and the subsequent chemical derivation of 14 leaf traits enabled the creation of a species-inclusive partial least squares regression model for each trait (Validation R-2 = 0.28 – 0.85). Model effects were applied to mixed spectra containing various combinations of a target plant species and one, or multiple, other species to determine if, and at what amount of target species presence, spectral mixing significantly altered the trait prediction of the target species.Trait prediction accuracy does change due to spectral mixing and certain traits were more affected by mixing than others. For example, percent nitrogen (%N) and equivalent water thickness (EWT) were more affected by spectral mixing than chlorophyll a (chl a) concentration and leaf mass per area (LMA). These results suggest that species-specific relationships between spectra and traits like %N and EWT are more important, whereas a general site model that holds across species is more achievable for chl a or LMA.Mixes containing various percentages of forbs, shrubs, graminoids and trees generate different prediction errors in some traits, including %N. LMA was relatively unaffected, except when Q. garryana, a tree, contributed to the mixed spectra.In mixed grassland-woodland ecosystems the prediction of LMA was relatively unaffected by spectral mixing, while %N highlights graminoid presence and isolates a key invader.Spectral mixing can complicate the accurate estimation of remotely sensed leaf functional traits, compromising the effectiveness of this technique.

Statistical Analysis of Crop Water Stress in Rainfed Rice (Oryza sativa L.) Using Spectral and Non-spectral Indices

The use of vegetation indices derived from wavelengths known to be sensitive to plant water status is a fast, reliable, and non-destructive method of identifying the spatial and temporal distribution of crop water requirements. A pot experiment was conducted in a screenhouse to assess the potential of vegetation indices in detecting water stress in rainfed rice during the reproductive growth phase. Rainfed lowland (PSB Rc14) and upland (UPL Ri7) rice varieties were subjected to non-water stress [2.0 irrigation water (IW) per cumulative pan evaporation (CPE)], mild water stress (1.5 IW/CPE), and severe water stress (1.0 IW/CPE) treatments during the reproductive growth stage. Leaf reflectance measurements were taken using a portable field spectrometer (Jazz Spectral Sensing Suite, Ocean Optics, Inc.) with a spectral range of 650–1050 nm and 0.36 nm bandwidth. Vegetation indices – namely, water index (WI), normalized water index-1 (NWI-1), normalized water index-2 (NWI-2), normalized water index-3 (NWI-3), and normalized water index-4 (NWI-4) – were then calculated from the leaf spectral reflectance measurements and correlated with leaf relative water content (RWC), crop water stress index, and grain yield. The leaf reflectance in the NIR region (700–1050 nm) of both rice varieties under severe water stress conditions (1.0 IW/CPE) was lower compared to those under mild water stress (1.5 IW/CPE) and non-water stress treatments (2.0 IW/CPE). Significant differences in the vegetation indices were detected at the flowering stage when the onset of water stress was also identified by the crop water stress index (CWSI). NWI-2 had the strongest correlation with grain yield of PSB Rc14 (r = –0.7815), whereas WI and NWI-1 correlated well with grain yield for UPL Ri7 (r = –0.876). These results suggest the potential of using hyperspectral vegetation indices as indicators of water stress during the flowering stage of rice.

Assessment of the Impact of Soil Contamination with Cadmium and Mercury on Leaf Nitrogen Content and Miscanthus Yield Applying Proximal Spectroscopy

The research objective was to use proximal spectroscopy in visible and near infrared (VNIR) spectra to determine the total leaf nitrogen (TN) content and the above-ground biomass of Miscanthus × giganteus (MxG) grown in the open-roof greenhouse experiment on soil contaminated with cadmium and mercury (100 mg Cd/kg soil; 20 mg Hg/kg soil), in dependence of different soil amendments in four treatments (I-soil without amendment; II-sludge; III-mycorrhizae; IV-MxG ash). Leaf reflectance was acquired using a field spectroradiometer (350–1050 nm) at the end of the vegetation period between 2018 and 2019 (n = 24). TN content was determined using the dry combustion method, while biomass was weighted immediately after the harvest. In terms of the treatment effect, sludge showed the greatest contribution in TN content. Regarding the biomass quantity, MxG ash revealed the best results as soil amendment. Applying the partial least squares regression, complete correlation and low root mean squared error (RMSE) were obtained between predicted and measured values for the validation dataset of TN content (R2 = 0.87, RMSE = 0.139%), while a strong correlation was calculated for biomass (R2 = 0.53, RMSE = 0.833 t/ha). As an additional tool with analytical methods, proximal spectroscopy is suitable to integrate the optical and physiological properties of MxG, and to assess nutrient stress in crop grown on contaminated soils.

Measurements of Anthocyanin Content of Prunus Leaves Using Proximal Sensing Spectroscopy and Statistical Machine Learning

A novel measurement framework for absolute total anthocyanin content (AAC) in leaves is proposed, where proximally-sensed leaf spectroscopic measurements are coupled with machine learning techniques. Use of both leaf reflectance and transflection spectral measurements is investigated by sounding the entire spectral range, from the visible to the short-wave infrared, and accounting for the whole shape of the leaf spectra. Results show low prediction errors (not higher, on average, than 0.34 mg/g) for AAC retrieval even when only a quarter of available data are used for training, with improved performance for higher fractions of training data. The employment of transflectance is shown to benefit to the retrieval process, thus providing generally lower prediction errors. During the AAC retrieval process, the machine learning technique automatically performs wavelength selection. By providing as output the subset of most relevant wavelengths for AAC retrieval, this framework represents a first step toward the development of a low-cost measurement system to be easily operated on the field for quick, reliable anthocyanin content measurements.

Ratio of Intensities of Blue and Red Light at Cultivation Influences Photosynthetic Light Reactions, Respiration, Growth, and Reflectance Indices in Lettuce.

Simple SummaryIllumination is an important factor for plant life because light is the basis of photosynthesis and productivity, the regulator of physiological processes, and a potential cause of damage. The development of LED technology has contributed to increasing the efficiency of illumination during plant cultivation through the use of light sources with narrow spectral bands. However, the characteristics of influence of light sources with different spectra on specific species of agricultural plants require further investigation. In the present work, we analyzed the influence of two variants of LED illumination (with increased intensity of red or blue light) on physiological processes in lettuce. These variants were selected because they corresponded to two maximums of photosynthetic light absorption. It was shown that, under the increased intensity of the blue light, both respiration and cyclic electron flow were stimulated; theseprocesses are known to be related to stress changes in plants. In contrast, under the increased intensity of the red light, linear electron flow was stimulated, a process that is related to plant productivity, and the biomass during cultivation was increased. The reflectance of leaves was also dependent on the variant of illumination. In the future, our results can be used to increase the efficiency of lettuce cultivation.LED illumination can have a narrow spectral band; its intensity and time regime are regulated within a wide range. These characteristics are the potential basis for the use of a combination of LEDs for plant cultivation because light is the energy source that is used by plants as well as the regulator of photosynthesis, and the regulator of other physiological processes (e.g., plant development), and can cause plant damage under certain stress conditions. As a result, analyzing the influence of light spectra on physiological and growth characteristics during cultivation of different plant species is an important problem. In the present work, we investigated the influence of two variants of LED illumination (red light at an increased intensity, the “red” variant, and blue light at an increased intensity, the “blue” variant) on the parameters of photosynthetic dark and light reactions, respiration rate, leaf reflectance indices, and biomass, among other factors in lettuce (Lactuca sativa L.). The same light intensity (about 180 µmol m−2s−1) was used in both variants. It was shown that the blue illumination variant increased the dark respiration rate (35–130%) and cyclic electron flow around photosystem I (18–26% at the maximal intensity of the actinic light) in comparison to the red variant; the effects were dependent on the duration of cultivation. In contrast, the blue variant decreased the rate of the photosynthetic linear electron flow (13–26%) and various plant growth parameters, such as final biomass (about 40%). Some reflectance indices (e.g., the Zarco-Tejada and Miller Index, an index that is related to the core sizes and light-harvesting complex of photosystem I), were also strongly dependent on the illumination variant. Thus, our results show that the red illumination variant contributes a great deal to lettuce growth; in contrast, the blue variant contributes to stress changes, including the activation of cyclic electron flow around photosystem I.

Detection of Larch Forest Stress from Jas’s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing

Detection of forest pest outbreaks can help in controlling outbreaks and provide accurate information for forest management decision-making. Although some needle injuries occur at the beginning of the attack, the appearance of the trees does not change significantly from the condition before the attack. These subtle changes cannot be observed with the naked eye, but usually manifest as small changes in leaf reflectance. Therefore, hyperspectral remote sensing can be used to detect the different stages of pest infection as it offers high-resolution reflectance. Accordingly, this study investigated the response of a larch forest to Jas’s Larch Inchworm (Erannis jacobsoni Djak) and performed the different infection stages detection and identification using ground hyperspectral data and data on the forest biochemical components (chlorophyll content, fresh weight moisture content and dry weight moisture content). A total of 80 sample trees were selected from the test area, covering the following three stages: before attack, early-stage infection and middle- to late-stage infection. Combined with the Findpeaks-SPA function, the response relationship between biochemical components and spectral continuous wavelet coefficients was analyzed. The support vector machine classification algorithm was used for detection infection. The results showed that there was no significant difference in the biochemical composition between healthy and early-stage samples, but the spectral continuous wavelet coefficients could reflect these subtle changes with varying degrees of sensitivity. The continuous wavelet coefficients corresponding to these stresses may have high potential for infection detection. Meanwhile, the highest overall accuracy of the model based on chlorophyll content, fresh weight moisture content and dry weight moisture content were 90.48%, 85.71% and 90.48% respectively, and the Kappa coefficients were 0.85, 0.79 and 0.86 respectively.

Successional and Intraspecific Variations in Leaf Traits, Spectral Reflectance Indices and Herbivory in a Brazilian Tropical Dry Forest

Leaf traits are good indicators of ecosystem functioning and can affect herbivory and leaf reflectance patterns, allowing a better understanding of changes in environmental conditions, such those observed during forest natural regeneration. The aim of this study was to evaluate the intraspecific variation in leaf traits and their influence on the pattern of herbivory and leaf reflectance in three species distributed along a successional gradient (early, intermediate and late stages) in a tropical dry forest (TDF) in northern Minas Gerais, Brazil. We sampled individuals of the following abundant tree species that occurred in multiple successional stages: Cenostigma pluviosum, Handroanthus ochraceus, and Tabebuia reticulata. We collected 10 leaves from each tree to determine the contents of chlorophyll a, b, and total, carotenoids and water, as well as the percentage of leaf area removed by herbivores and leaf specific mass (LSM). We also measured five spectral reflectance indices (Normalized Difference Vegetation Index-NDVI, Simple Ratio-SR, modified Normalized Difference-nND, modified SR-mSR and Water Index-WI) using a portable spectrometer. Our results showed intraspecific differences in most leaf traits along the successional gradient, suggesting that local adaptation may play an important role in plant community assembly. However, herbivory only differed for H. ochraceus in early and intermediate stages, but it was not affected by the leaf traits considered here. Spectral reflectance indices also differed among successional stage for all species together and for each species separately, except for T. reticulata in intermediate and late stages. Thus, leaf spectral signatures may be an important tool to the remote detection of different successional stages in TDFs, with implications for forest management.

Impact of Structural, Photochemical and Instrumental Effects on Leaf and Canopy Reflectance Variability in the 500–600 nm Range

Current rapid technological improvement in optical radiometric instrumentation provides an opportunity to develop innovative measurements protocols where the remote quantification of the plant physiological status can be determined with higher accuracy. In this study, the leaf and canopy reflectance variability in the PRI spectral region (i.e., 500–600 nm) is quantified using different laboratory protocols that consider both instrumental and experimental set-up aspects, as well as canopy structural effects and vegetation photoprotection dynamics. First, we studied how an incorrect characterization of the at-target incoming radiance translated into an erroneous vegetation reflectance spectrum and consequently in an incorrect quantification of reflectance indices such as PRI. The erroneous characterization of the at-target incoming radiance translated into a 2% overestimation and a 31% underestimation of estimated chlorophyll content and PRI-related vegetation indexes, respectively. Second, we investigated the dynamic xanthophyll pool and intrinsic Chl vs. Car long-term pool changes affecting the entire 500–600 nm spectral region. Consistent spectral behaviors were observed for leaf and canopy experiments. Sun-adapted plants showed a larger optical change in the PRI range and a higher capacity for photoprotection during the light transient time when compared to shade-adapted plants. Outcomes of this work highlight the importance of well-established spectroscopy sampling protocols to detect the subtle photochemical features which need to be disentangled from the structural and biological effects.

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.

Estimating Leaf Area Index with Dynamic Leaf Optical Properties

Leaf area index (LAI) plays an important role in models of climate, hydrology, and ecosystem productivity. The physical model-based inversion method is a practical approach for large-scale LAI inversion. However, the ill-posed inversion problem, due to the limited constraint of inaccurate input parameters, is the dominant source of inversion errors. For instance, variables related to leaf optical properties are always set as constants or have large ranges, instead of the actual leaf reflectance of pixel vegetation in the current model-based inversions. This paper proposes to estimate LAI with the actual leaf optical property of pixels, calculated from the leaf chlorophyll content (Chlleaf) product, using a three-dimensional stochastic radiative transfer model (3D-RTM)-based, look-up table method. The parameter characterizing leaf optical properties in the 3D-RTM-based LAI inversion algorithm, single scattering albedo (SSA), is calculated with the Chlleaf product, instead of setting fixed values across a growing season. An algorithm to invert LAI with the dynamic SSA of the red band (SSAred) is proposed. The retrieval index (RI) increases from less than 42% to 100%, and the RMSE decreases to less than 0.28 in the simulations. The validation results show that the RMSE of the dynamic SSA decreases from 1.338 to 0.511, compared with the existing 3D-RTM-based LUT algorithm. The overestimation problem under high LAI conditions is reduced.

Mapping canopy nitrogen-scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed-species Eucalyptus forests.

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high‐resolution multispectral imagery, collected by a lightweight and low‐cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand‐scale from leaf‐scale chemistry measurements across a gradient of mixed‐species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50–1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross‐validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.

Upscaling from leaf to canopy: Improved spectral indices for leaf biochemical traits estimation by minimizing the difference between leaf adaxial and abaxial surfaces

The knowledge of leaf biochemical traits is significant to understand the plant growth and physiological status. Spectral indices have been widely used to assess leaf biochemical traits, while the estimation accuracies at canopy level are frequently lower compared to those at leaf level in part due to the complexity of canopy structures and the variations in optical properties between leaf adaxial and abaxial surfaces. This study thus improved spectral indices with minimizing the effect of the difference between leaf adaxial and abaxial surfaces to assess leaf biochemical traits from leaf to canopy level. The datasets including leaf reflectance and canopy reflectance with corresponding leaf chlorophyll content ( C ab ), water content ( C w ), and dry matter content ( C m ) from a wide range of plant species were used. Results showed that there existed a significant difference between leaf abaxial and abaxial reflectance, causing the variation in the relationship between leaf biochemical traits and spectral indices. The published spectral indices exhibited the strong relationships with C ab , C w , and C m for either leaf adaxial or abaxial data, while the relationships of spectral indices with C ab , C w , and C m from leaf adaxial reflectance were inconsistent with those from leaf abaxial reflectance. The proposed adjusted ratio of difference spectral index (ARDSI) minimized the difference between leaf adaxial and abaxial reflectance for assessing C ab , C w , and C m at leaf level with the root mean square error of 9.32 μg cm -2 , 0.0050 g cm -2 , and 0.0053 g cm -2 , respectively. The application of the proposed ARDSI to the canopy level alleviated the effect of the variations of leaf adaxial and abaxial reflectance difference and canopy structures on the estimation of leaf biochemical traits, which thus improved the assessment of C ab , C w , and C m at canopy level in the simulated and measured datasets. The proposed approach would advance the applicability of spectral indices to monitor the physiological and functional traits of field crops at different scales. • Both leaf sides showed varied surface characteristics and optical properties. • Differences between both leaf sides affected the simulation of canopy reflectance. • Universal spectral indices were developed for leaf adaxial and abaxial surfaces. • The spectral indices well assessed C ab , C w , and C m at leaf and canopy levels.

Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets

Accurate estimation of leaf nitrogen concentration (LNC) is critical to characterize ecosystem and plant physiological processes for example in carbon fixation. Remote sensing can capture LNC, while interrelated traits and spectral diversity across plant species prevent development of transferable LNC assessment models based on leaf reflectance. Here, we developed a new transfer learning method by coupling transfer component analysis with the support vector regression, namely TCA-SVR, to transfer LNC assessment models across different plant species. We benchmarked the performance of TCA-SVR against a well-established partial least squares regression (PLSR) model with five remote sensing datasets on 60 plant species measured from three spectroradiometers with varied spectral resolutions and illumination and viewing angles. The result showed that leaf reflectance presented the high spectral diversity in different spectral regions, plant species, and growth stages. The combination of visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) reflectance (e.g. 550–2300 nm) achieved the optimal LNC assessment across all datasets. Results on the testing datasets showed that the transferability of the PLSR models highly depended on the LNC distribution and spectral features, which were associated with the differences in plant species, spectral measurements, and growth conditions between datasets. These differences led to the large variations in LNC and leaf reflectance, which thus produced the overestimations and underestimations of LNC. Compared to the PLSR model, TCA-SVR greatly improved the transferability of the LNC assessment model by reducing the average root mean square error by 36.76%. Further, the implementation of modeling updating can help TCA-SVR learn the features related to the difference in plant species and LNC ranges by transferring samples from the target dataset to the source dataset. Our model updating approach improved the performance of TCA-SVR and only needed 5% of the off-site samples to supplement the source dataset to achieve an effective assessment of LNC. Refining the proposed method with new remote sensing datasets will aid rapid monitoring of plant nitrogen status and may improve carbon‑nitrogen interactions in existing ecosystem models.

Leaf reflectance and functional traits as environmental indicators of urban dust deposition

BackgroundHow to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment. Here, we focus on changes of plant reflectance and plant functional traits due to dust deposition, and develop a prediction model of dust deposition based on these traits.ResultsThe results showed that (1) The average dust deposition per unit area of Ligustrum quihoui leaves was significantly different among urban environments (street (18.1001 g/m2), community (14.5597 g/m2) and park (9.7661 g/m2)). Among different urban environments, leaf reflectance curves tends to be consistent, but there were significant differences in leaf reflectance values (park (0.052–0.585) > community (0.028–0.477) > street (0.025–0.203)). (2) There were five major reflection peaks and five major absorption valleys. (3) The spectral reflectances before and after dust removal were significantly different (clean leaves > dust-stagnant leaves). 695 ~ 1400 nm was the sensitive range of spectral response. (4) Dust deposition has significant influence on slope and position of red edge. Red edge slope was park > community > street. After dust deposition, the red edge position has obviously “blue shift”. The moving distance of the red edge position increases with the increase of dust deposition. The forecast model of dust deposition amount established by simple ratio index (y = 2.517x + 0.381, R2 = 0.787, RMSE (root-mean-square error) = 0.187. In the model, y refers to dust retention, x refers to simple ratio index.) has an average accuracy of 99.98%. (5) With the increase of dust deposition, the specific leaf area and chlorophyll content index decreased gradually. The leaf dry matter content, leaf tissue density and leaf thickness increased gradually.ConclusionIn the dust-polluted environment, L. quihoui generally presents a combination of characters with lower specific leaf area, chlorophyll content index, and higher leaf dry matter content, leaf tissue density and leaf thickness. Leaf reflectance spectroscopy and functional traits have been proved to be effective in evaluating the changes of urban dust deposition.

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

BackgroundMacrophytes 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.ResultsOur 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.ConclusionsOur 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.

Plant priming changes physiological properties and lignin content in Miscanthus x giganteus

During the last couple of years, the traditionally mild climate of central Europe changed to very hot and dry (including the spring season). Local plants and crops need to deal with this stressful change causing a yield decrease. Primary aims were to explore simple ways of "hardening" Miscanthus x giganteus J.M.Greef, Deuter ex Hodk., Renvoize energy grass to deal with climate changes more efficiently and improve its productivity as a biomass crop at the same time. Plant priming has been discussed as a cheap and simple tool for the improvement of plant qualities. Specific doses of vitamins, metals as priming compounds, and different initial cultivation conditions (cold, hot, dark) were applied to study changes in plant physiology using non-invasive methods of measurement of leaf fluorescence and reflectance. After a short priming period, all plants studied were cultivated in a greenhouse under conditions mimicking the latest climate trend. Copper application and cold treatment (10 °C) increased the synthesis of lignin in treated plants. Nitrogen supplementation had the opposite effect; lignin production decreased. All treatments had a significant influence on photosynthetic processes; bud did not negatively influence the production of photosynthetic pigments. Obtained results show that applying particular physical and chemical primers to young Miscanthus x giganteus can result in substantial physiological changes, mainly concerning the effectivity of photosynthesis and change in cell wall composition.

Testing stomatal models at the stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1)

Abstract. At the leaf level, stomata control the exchange of water and carbon across the air–leaf interface. Stomatal conductance is typically modeled empirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predicting carbon and water fluxes at both the leaf and tree levels. However, how well the optimization models perform at larger scales has not been extensively evaluated. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such as big-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves nor the entire canopy. As a result, they are unable to directly link canopy optical properties with light distribution within the canopy to remote sensing data observed from afar. Here, we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the land component of a new Earth system model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and water fluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIF retrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model, in particular, showed increased skill in predicting the water flux given the lower error (ca. 14.2 % and 21.8 % improvement for the gymnosperm and angiosperm forests, respectively) and better 1:1 comparison (slope increases from ca. 0.34 to 0.91 for the gymnosperm forest and from ca. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for both forests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth system modeling in general.