Leaf Reflectance Research Articles

SCOPE 2.0: a model to simulate vegetated land surface fluxes and satellite signals

Abstract. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model aims at linking satellite observations in the visible, infrared, and thermal domains with land surface processes in a physically based manner, and quantifying the microclimate in vegetation canopies. It simulates radiative transfer in the soil, leaves, and vegetation canopies, as well as photosynthesis and non-radiative heat dissipation through convection and mechanical turbulence. Since the first publication 12 years ago, SCOPE has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary production (GPP), and directional thermal signals. Here, we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements: (1) it enables the definition of layers consisting of leaves with different properties, thus enabling the simulation of vegetation with an understorey or with a vertical gradient in leaf chlorophyll concentration; (2) it enables the simulation of soil reflectance; (3) it includes the simulation of leaf and canopy reflectance changes induced by the xanthophyll cycle; and (4) the computation speed has been reduced by 90 % compared to earlier versions due to a fundamental optimization of the model. These new features improve the capability of the model to represent complex canopies and to explore the response of remote sensing signals to vegetation physiology. The improvements in computational efficiency make it possible to use SCOPE 2.0 routinely for the simulation of satellite data and land surface fluxes. It also strengthens the operability for the numerical retrieval of land surface products from satellite or airborne data.

An insight into spectral composition of light available for photosynthesis via remotely assessed absorption coefficient at leaf and canopy levels.

Non-invasive comparative analysis of the spectral composition of energy absorbed by crop species at leaf and plant levels was carried out using the absorption coefficient retrieved from leaf and plant reflectance as an informative metric. In leaves of three species with contrasting leaf structures and photosynthetic pathways (maize, soybean, and rice), the blue, green, and red fractions of leaf absorption coefficients were 48, 20, and 32%, respectively. The fraction of green light in the total budget of light absorbed at the plant level was higher than at the leaf level approaching the size of the red fraction (24% green vs. 25.5% red) and surpassing it inside the canopy. The plant absorption coefficient in the far-red region (700-750nm) was significant reaching 7-10% of the absorption coefficient in green or red regions. The spectral composition of the absorbed light in the three species was virtually the same. Fractions of light in absorbed PAR remained almost invariant during growing season over a wide range of plant chlorophyll content. Fractions of absorption coefficient in the green, red, and far-red were in accord with published results of quantum yield for CO2 fixation on an absorbed light basis. The role of green and far-red light in photosynthesis was demonstrated in simple experiments in natural conditions. The results show the potential for using leaf and plant absorption coefficients retrieved from reflectance to quantify photosynthesis in each spectral range.

Early infestations by arthropod pests induce unique changes in plant compositional traits and leaf reflectance.

BACKGROUNDWith steadily growing interest in the use of remote‐sensing technologies to detect and diagnose pest infestations in crops, it is important to investigate and characterize possible associations between crop leaf reflectance and unique pest‐induced changes in plant compositional traits. Accordingly, we compiled plant compositional traits from chrysanthemum and gerbera plants in four treatments: non‐infested, or infested with mites, thrips or whiteflies, and we acquired hyperspectral leaf reflectance data from the same plants over time (0–14 days).RESULTSPlant compositional traits changed significantly in response to arthropod infestations, and individual chrysanthemum and gerbera plants were classified with 78% and 80% accuracy, respectively. Based on leaf reflectance, individual plants from the four treatments were classified with moderate accuracy levels of 76% (gerbera) and 73% (chrysanthemum) but with a clear distinction between non‐infested and infested plants. Accurate and consistent diagnosis of biotic stressors was not achieved.CONCLUSIONTo our knowledge, this is the first study in which infestations by multiple economically important arthropod pests are directly compared and associated with leaf reflectance responses and changes in plant compositional traits. It is important to highlight that imposed stress levels were low, period of infestation was short, and hyperspectral remote‐sensing data were acquired at four time points with analyses based on large data sets (3826 leaf reflectance profiles for chrysanthemum and 4041 for gerbera). This study provides novel insight into crop responses to different biotic stressors and into possible associations between plant compositional traits and hyperspectral leaf reflectance data acquired from crop leaves.

A novel approach for long-term spectral monitoring of desert shrubs affected by an oil spill

Crude oil pollution is a global environmental concern since it persists in the environment longer than most conventional carbon sources. In December 2014, the hyper-arid Evrona Nature Reserve, Israel, experienced large-scale contamination when crude oil spilled. The overarching goal of the study was to investigate the possible changes, caused by an accidental crude oil spill, in the leaf reflectance and biochemical composition of four natural habitat desert shrubs. The specific objectives were (1) to monitor the biochemical properties of dominant shrub species in the polluted and control areas; (2) to study the long-term consequences of the contamination; (3) to provide information that will assist in planning rehabilitation actions; and (4) to explore the feasibility of vegetation indices (VIs), along with the machine learning (ML) technique, for detecting stressed shrubs based on the full spectral range. Four measurement campaigns were conducted in 2018 and 2019. Along with the various stress indicators, field spectral measurements were performed in the range of 350–2500 nm. A regression analysis to examine the relation of leaf reflectance to biochemical contents was carried out, to reveal the relevant wavelengths in which polluted and control plants differ. Vegetation indices applied in previous studies were found to be less sensitive for indirect detection of long-term oil contamination. A novel spectral index, based on indicative spectral bands, named the “normalized blue-green stress index” (NBGSI), was established. The NBGSI distinguished significantly between shrubs located in the polluted and in the control areas. The NBGSI showed a strong linear correlation with pheophytin a. Machine learning classification algorithms obtained high overall prediction accuracy in distinguishing between shrubs located in the oil-polluted and the control sites, indicating internal component differences. The findings of this study demonstrate the efficacy of indirect and non-destructive spectral tools for detecting and monitoring oil pollution stress in shrubs.

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 (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.

Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana

Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 > 90%) and agree on the features with the highest discriminative power (reflectance at ~670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 ± 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines.

High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat

BackgroundEpicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density.ResultsCuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm−2) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm−2 utilizing ground and aerial canopy reflectance.ConclusionsOverall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.

Corrected photochemical reflectance index (PRI) is an effective tool for detecting environmental stresses in agricultural crops under light conditions.

High-throughput detection of plant environmental stresses is required for minimizing the reduction in crop yield. Environmental stresses in plants have primarily been validated by the measurements of photosynthesis with gas exchange and chlorophyll fluorescence, which involve complicated procedures. Remote sensing technologies that monitor leaf reflectance in intact plants enable real-time visualization of plant responses to environmental fluctuations. The photochemical reflectance index (PRI), one of the vegetation indices of spectral leaf reflectance, is related to changes in xanthophyll pigment composition. Xanthophyll dynamics are strongly correlated with plant stress because they contribute to the thermal dissipation of excess energy. However, an accurate assessment of plant stress based on PRI requires correction by baseline PRI (PRIo) in the dark, which is difficult to obtain in the field. In this study, we propose a method to correct the PRI using NPQT, which can be measured under light. By this method, we evaluated responses of excess light energy stress under drought in wild watermelon (Citrullus lanatus L.), a xerophyte. Demonstration on the farm, the stress behaviors were observed in maize (Zea mays L.). Furthermore, the stress status of plants and their recovery following re-watering were captured as visual information. These results suggest that the PRI is an excellent indicator of environmental stress and recovery in plants and could be used as a high-throughput stress detection tool in agriculture.

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.

Evaluating the Performance of Hyperspectral Leaf Reflectance to Detect Water Stress and Estimation of Photosynthetic Capacities

Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards.

Effects of Incorporating Measured Leaf Optical Properties in Land Surface Models

Leaf optical properties (LOPs, i.e., leaf reflectance and transmittance), as a fundamental property of vegetation, are a key parameter in the canopy radiative transfer process. LOPs have a direct impact on the surface solar radiation partition and further affect surface flux exchanges. Recent works have provided reliable LOP data and mentioned that notable differences exist between the prescribed LOP values in current land surface models and measured LOP values, especially in the near-infrared (NIR) band. To evaluate the effects of different LOP values in land surface modeling, we ran two land surface models (the Community Land Model and the Common Land Model) with their default prescribed and measured values to examine the differences in simulated surface radiation partitions and fluxes. Our analyses show that differences in LOP values can lead to a large discrepancy in albedo, radiation partition, sensible heat flux and net radiation simulations. By using the measured LOP values, in the boreal forest zone, Southeast China, and the eastern United States, both models have a significantly increased surface albedo in the NIR band, with the difference exceeding 10% during JJA. Thus, the measured LOP values can improve the negative albedo bias in the boreal forest zone during summertime. Moreover, both models simulate less net radiation with a maximum reduction of 11 W/m2 when incorporating the measured LOP values. Therefore, the total sensible heat flux can be reduced by as much as 11 W/m2. The results of this study emphasize that different LOP values can have a considerable effect on the surface radiation budget and sensible heat flux simulations which need attention in land surface model development. However, in current offline simulations, the measured LOP values cause slight changes in land surface temperatures and gross primary productivity (GPP).

Factors Involved on Tiger-Stripe Foliar Symptom Expression of Esca of Grapevine.

Esca of grapevine causes yield losses correlated with incidence and severity symptom expression. Factors associated with leaf symptom mechanisms are yet to be fully clarified. Therefore, in 2019 and 2020, macro and microelement analyses and leaf reflectance measurements were carried out on leaves at different growth stages in a vineyard located in Abruzzo, central Italy. Surveys were carried out on leaves of both never leaf-symptomatic vines and different categories of diseased vine shoots. Never leaf-symptomatic and diseased vines were also treated with a fertilizer mixture that proved to be able to limit the symptom expression. Results showed that untreated asymptomatic diseased vines had high calcium contents for most of the vegetative season. On the contrary, treated asymptomatic diseased vines showed higher contents of calcium, magnesium, and sodium, at berries pea-sized, before the onset of symptoms. These vines had better physiological efficiency showing higher water index (WI), normalized difference vegetation index (NDVI), and green normalized difference vegetation index (GNDVI) values, compared to untreated asymptomatic vines, at fruit set. Results confirmed the strong response of the plant to symptom expression development and the possibility of limiting this response with calcium and magnesium applications carried out before the symptom onset.

Plant spectral diversity as a surrogate for species, functional and phylogenetic diversity across a hyper‐diverse biogeographic region

AbstractAimWith plant biodiversity under global threat, there is an urgent need to monitor the spatial distribution of multiple axes of biodiversity. Remote sensing is a critical tool in this endeavour. One remote sensing approach for detecting biodiversity is based on the hypothesis that the spectral diversity of plant communities is a surrogate of multiple dimensions of biodiversity. We investigated the generality of this ‘surrogacy’ for spectral, species, functional and phylogenetic diversity across 1,267 plots in the Greater Cape Floristic Region (GCFR), a hyper‐diverse region comprising several biomes and two adjacent global biodiversity hotspots.LocationThe GCFR centred in south‐western and western South Africa.Time periodAll data were collected between 1978–2014.Major taxa studiedVascular plants within the GCFR.MethodsSpectral diversity was calculated using leaf reflectance spectra (450–950 nm) and was related to other dimensions of biodiversity via linear models. The accuracy of different spectral diversity metrics was compared using 10‐fold cross‐validation.ResultsWe found that a distance‐based spectral diversity metric was a robust predictor of species, functional and phylogenetic biodiversity. This result serves as a proof‐of‐concept that spectral diversity is a potential surrogate of biodiversity across a hyper‐diverse biogeographic region. While our results support the generality of spectral diversity as a biodiversity surrogate, we also find that relationships vary between different geographic subregions and biomes, suggesting that differences in broad‐scale community composition can affect these relationships.Main conclusionsSpectral diversity was shown to be a robust surrogate of multiple dimensions of biodiversity across biomes and a widely varying biogeographic region. We also extend these surrogacy relationships to ecological redundancy to demonstrate the potential for additional insights into community structure based on spectral reflectance.

A New Linear Relation for Estimating Surface Broadband Emissivity in Arid Regions Based on FTIR and MODIS Products

Broadband emissivity is a crucial parameter for calculating the radiation budget, still, it adopts a constant value in land surface models due to a lack of adequate observations. Arid regions have complex underlying surfaces and estimations of the broadband emissivity in such areas suffer from high spatial variation and uncertainty. Here, we propose a novel method for estimating broadband emissivity in the 8–14 µm range based on Fourier-transform infrared spectroscopy (FTIR) observations, moderate resolution imaging spectrometer (MODIS) emissivity, the leaf area index (LAI) and reflectance products. The proposed method exploits FTIR observations, MODIS single-channel emissivity, reflectance and the LAI to fit a linear regression of the broadband emissivity, so the optimal equation includes emissivity, reflectance and the LAI, with an R2 and root-mean-squared error of 0.942 and 0.08. Then we used the proposed method to generate a broadband emissivity map of Northwest of China, the broadband emissivity estimated by the method showed higher variations and finer distribution in arid areas and sparsely vegetated regions compared to data from the global land surface satellite and land model. An analysis of the relationship between the broadband emissivity, land-use type and soil moisture found an existing but not linear relationship, which indicated that the relationship was complicated under the inhomogeneous surface of wetness and vegetation. In conclusion, our results suggest that the proposed method can accurately estimate the broadband emissivity in arid regions. In future research, we will test the data in a land model.

Estimation model and its trade-off strategy of Mangifera persiciforma Colletotrichum gloeosporioides degree based on leaf reflection spectrum.

Colletotrichum gloeosporioides is one of the most common and serious fungal diseases of the tree Mangifera persiciforma. Yet we lack an effective method to evaluate this ecological interaction accurately. Here, we measured the functional traits and leaf reflectance spectrum of the host plants under different disease degrees. The findings provide a fast and efficient method for large-scale and high-precision monitoring of C. gloeosporioides in M. persiciforma stands. Using the collected leaf reflection data, we set up a prediction model of the optimal disease degree. Firstly, we found that leaf functional traits of M. persiciforma generally consisted of low leaf thickness, low relative chlorophyll content, small specific leaf area, high leaf tissue density, high dry matter content, low stomatal density, and large stomatal area. Secondly, leaf reflectivity increases with damage of C. gloeosporioides, which corresponds to five main reflection peaks and five absorption valleys in the spectral reflectance curve of leaves at the same positions (350-1800 nm). Thirdly, with the increase of infection degree, red edge slope and yellow edge slope decrease, while green peak reflectance, red valley reflectance, and blue edge slope all increase. Blue shift was detected in the red edge, green peak, and red valley, while red shift appeared at the blue edge and yellow edge. Finally, the best predictive model was that based on green peak reflectance (y=3.6396-0.0693x, R2=0.5149, RMSE [root-mean-square error] =0.2735), with an R2=0.92 and RMSE=0.0042 between its predicted vs. observed values. Because of its high inversion accuracy, the model can be used to predict the invasion conditions of M. persiciforma by C. gloeosporioides. Our study demonstrated that when plants are infected by C. gloeosporioides, there was a strong trade-off relationship between leaf functional traits. On the global leaf economics spectrum, the leaves tended toward the "slow investment-return" end when infected by C. gloeosporioides.

Leaf Age Matters in Remote Sensing: Taking Ground Truth for Spectroscopic Studies in Hemiboreal Deciduous Trees with Continuous Leaf Formation

We examined the seasonal changes in biophysical, anatomical, and optical traits of young leaves, formed throughout the vegetative season due to sylleptic growth, and mature leaves formed by proleptic growth in spring. Leaf developmental categories contribute to the top-of-canopy reflectance and should be considered when taking ground truth for remote sensing studies (RS). Deciduous tree species, Betula pendula, Populus tremula, and Alnus incana, were sampled from May to October 2018 in an Estonian hemiboreal forest. Chlorophyll and carotenoid content were detected biochemically; leaf anatomical traits (leaf, palisade, and spongy mesophyll thickness) were measured on leaf cross-sections; leaf reflectance was measured by a spectroradiometer with an integrating sphere (350–2500 nm). Biophysical and anatomical leaf traits were related to 64 vegetation indices (VIs). Linear models based on VIs for all tested leaf traits were more robust if both juvenile and mature leaves were included. This study provides information on which VIs are interchangeable or independent. Pigment and leaf thickness sensitive indices formed PC1; water and structural trait related VIs formed an independent group associated with PC3. Type of growth and leaf age could affect the validation of biophysical and anatomical leaf trait retrieval from the optical signal. It is, therefore, necessary to sample both leaf developmental categories—young and mature—in RS, especially if sampling is only once within the vegetation season.

Water Saving Strategies in Mediterranean Areas

Mediterranean climate is associated with summer water scarcity and, consequently, with a general susceptibility to erosion, degradation, and desertification. Thin soils, with low water storage capacity, develop in slopes where all main factors for erosion can at its worst. The need for wise irrigation is crucial for human activities. Plant adaptation includes stomatal closure as a strategy to reduce water use improving plant survival, while reducing photosynthesis and control of leaf temperature. Other strategies at leaf level include reducing radiation interception and increasing sensible heat diffusion, for instance by developing small leaf size, high leaf reflectance and adapted positions towards sunlight. Another order of survival strategies to cope with summer water scarcity concerns the increase of water uptake at root level and hydraulic lift or water redistribution. Finally, irrigation efficiency and evapotranspiration efficiency also should be optimised. Several experiments were performed on olive orchards under the harsh summer conditions of South East Portugal. The results were consistent in several aspects related to water uptake: colonization of the inter-row space, qualitative and quantitative importance of hydraulic redistribution and water balance estimates from soil water content versus plant water use measured by a micrometeorological technique. In orchards under deficit irrigation, analysed in same study, water use was also quantified and parameters of stress coefficients functions were critically analysed.

Understanding limits of species identification using simulated imaging spectroscopy

Imaging spectroscopy is a powerful tool for mapping and monitoring the spatial distribution of species compositions. Most spectroscopy studies rely on extensive field campaigns to assess classification accuracies against ground-truth samples. Their findings are correlative and not based on first principles and by design they often do not provide for an end-to-end traceability of error budgets. In this study, we present a simulation approach for modeling canopy-level reflectance using three-dimensional plant models arranged on a synthetic landscape. This type of simulation offers an important avenue for understanding the interplay between instrument parameters and landscape characteristics for different ecosystems. The simulation approach was applied using species-level leaf reflectance data from the biodiversity hotspot in the Greater Cape Floristic Region (GCFR) of South Africa. We simulated 140 random realizations, each of 20x20m in size, encompassing 10 to 70 species to understand how changing the diversity and image resolution (pixel size) affected spectral mixing and the ability to discern species within the plot. Overall, across all resolutions and species mixtures, the mean classification accuracy was 0.690±0.184 (mean F1-score±standard deviation across realizations), however, this varied widely from 0.812±0.057 at a pixel size of 60 cm for landscapes with 30 species to 0.196±0.106 for landscapes with 70 species and a pixel size of 240 cm. As expected, classification accuracy decreased with increasing spatial grain from 0.763±0.064 at 15 cm resolution to only 0.414±0.214 at 240 cm. Classification accuracies across different levels of species richness remained fairly constant except in the coarsest grain size (240 cm). This suggests an increasing challenge in species identification as grain sizes become coarser than the constituent organisms. In summary, this approach can facilitate detailed simulation of reflectance while exploring various ecological scenarios such as changing species composition or the impacts of environmental variability such as drought.

Prediction of leaf traits of lianas and trees via the integration of wavelet spectra in the visible-near infrared and thermal infrared domains

Predicting leaf traits using models based on spectroscopic data can provide essential information to advance ecological research and future Earth system models. Most current models are based on Partial Least Squares Regression (PLSR) algorithms that attempt to predict a set of leaf traits of several plant groups using leaf spectra. However, PLSR models tend to be inconsistent in describing the importance of absorption features when used to predict leaf traits. Likewise, the effect of contrasting absorption features of different plant groups on the prediction and evaluation of PLSR models is not well understood. Hence, this study focuses on using wavelet spectra to overcome current PLSR's limitation and improve leaf trait predictions. Specifically, we explored the use of visible–near-infrared (0.45–1.0 μm) and mid- long-wave infrared spectra (2.55–11 μm) to predict three-leaf traits of lianas and trees: Leaf Mass Area (LMA), Water Content (WC), and Equivalent Water Thickness (EWT). We also compare the effect of life forms on the prediction of traits by using sun leaves collected from 14 liana species and 21 tree species (n = 700) from a Neotropical Dry Forest. On each leaf, reflectance measurements were performed for both selected spectral regions; then, leaf traits were estimated from a leaf segment. Leaf reflectance was first resampled and then processed using continuous wavelet transformation (CWT) to derive the wavelet spectra. PLSR models linking the leaf traits and the reflectance or wavelet spectra were compared. Our results reveal that PLSR models based on wavelet spectra require fewer components to predict traits (13–16) than those based on reflectance (25–29). In addition, PLSR models' performance (e.g., R2) of testing datasets tend to be higher for models based on wavelet spectra (LMA = 0.83; WC = 0.77; EWT = 0.68) than reflectance (LMA = 0.78; WC = 0.76; EWT = 0.49). Wavelet spectra models also seem to better characterize absorption features that drive the variability of leaf traits than models based on reflectance. However, life forms play an essential role in model performance, where the prediction of lianas' traits presenting lower R2 (R2 = 0.61 ± 0.25) than trees' traits (R2 = 0.69 ± 0.15) regardless of the type of spectra or leaf trait. Our findings highlight the use of wavelet spectra to overcome limitations of the PLSR models for predicting leaf traits and the need to explore potential bias associated with plant groups on the model evaluations.

Determination of Plant Developmental Stability in Plant Lighting with Hyperspectral Imaging

The authors showed that a convenient, accurate and fast way of assessing the degree of influence of environmental factors on plants was needed to optimize photoculture. They emphasized the importance of non-destructive monitoring of crops physiological state of, for which they used phenomics technologies, for example, remote sensing using hyperspectral cameras.(Research purpose) To reveal the possibility of using hyperspectral imaging to determine the plant developmental stability.(Materials and methods) As a measure of the favorable impact of environmental factors on the growth and development of plants, their developmental stability was taken, numerically characterized by the fluctuating asymmetry value. The authors proposed to use vegetation indices determined from the leaf reflection spectra as a bilateral feature. The object of experimental research was juvenile cucumber plants. The studies were carried out in laboratory conditions. The spectral characteristics of cucumber leaves grown under different light quality of radiation were determined using a Specim IQ hyperspectral camera. Information on the spectral reflectances was extracted from the resulting data hypercube. As an example calculations were performed for Normalized Difference Vegetation Index.(Results and discussion) The authors revealed differences in the productivity indicators of plants grown under different light quality. They revealed a significant frequency of occurrence of Normalized Difference Vegetation Index asymmetry in two halves of the cucumber leaf surface. The fluctuating nature of this asymmetry was confirmed. They found that with a light quality providing a higher productivity of plants, lower values of fluctuating asymmetry were observed, which indicate greater stability of plant development.(Conclusions) The authors proposed a method for determining the plant developmental stability using a hyperspectral camera. The method was based on the assessment of the fluctuating asymmetry of vegetation indices calculated for points on the leaf surface, characterized by the same location conditions relative to the border of its left and right halves. A preliminary assessment of the possibility of determining the developmental stability by the results of phenotyping using the example of cucumber plants showed the feasibility of the method and its practical applicability.