Leaf Spectral Measurements Research Articles

Spectral ecophysiology: hyperspectral pressure-volume curves to estimate leaf turgor loss.

Turgor loss point (TLP) is an important proxy for plant drought tolerance, species habitat suitability, and drought-induced plant mortality risk. Thus, TLP serves as a critical tool for evaluating climate change impacts on plants, making it imperative to develop high-throughput and in situ methods to measure TLP. We developed hyperspectral pressure-volume curves (PV curves) to estimate TLP using leaf spectral reflectance. We used partial least square regression models to estimate water potential (Ψ) and relative water content (RWC) for two species, Frangula caroliniana and Magnolia grandiflora. RWC and Ψ's model for each species had R2 ≥ 0.7 and %RMSE = 7-10. We constructed PV curves with model estimates and compared the accuracy of directly measured and spectra-predicted TLP. Our findings indicate that leaf spectral measurements are an alternative method for estimating TLP. F. caroliniana TLP's values were -1.62 ± 0.15 (means ± SD) and -1.62 ± 0.34 MPa for observed and reflectance predicted, respectively (P > 0.05), while M. grandiflora were -1.78 ± 0.34 and -1.66 ± 0.41 MPa (P > 0.05). The estimation of TLP through leaf reflectance-based PV curves opens a broad range of possibilities for future research aimed at understanding and monitoring plant water relations on a large scale with spectral ecophysiology.

Accurate modeling of vertical leaf nitrogen distribution in summer maize using in situ leaf spectroscopy via CWT and PLS-based approaches

The vertical leaf nitrogen (N) distribution in summer maize is believed to be an important adaptive reaction to crop physiology and ecosystem functions. Therefore, it is of great significance to understand the vertical characteristics of leaf N concentration (LNC) across summer maize canopies for crop growth and production. However, the effect of vertical canopy position on LNC and subsequently leaf hyperspectral characteristics of the entire spectrum (350–2500 nm) for summer maize is poorly understood. The purpose of this work was to quantitatively study the effects of the N nutrition on vertical distribution of LNC, identify the sensitive layer and effective wavelength of leaves, and establish a in situ leaf spectrum monitoring model considering vertical distribution of LNC. Six N field trials were conducted for four consecutive years (2017–2020). In addition, the data of 30 farmers' conventional farmland management in 2020 were collected to check the robustness of the constructed optimal estimation model for LNC prediction. Leaf spectral measurements together with LNC were studied at three vertical leaf positions on the crop stem: upper, middle and lower. Spectral reflectance was processed by continuous wavelet transform (CWT); partial least square (PLS) was used to analyze the relationships between LNC of different layers and spectral reflectance. Field sampling indicated that LNC had a vertical distribution pattern and an evident decline from the upper to lower layer. CWT technique can significantly increase the prediction accuracy of LNC at different layers, and the best decomposition scale is CWT-1. The CWT-1-PLS model for predicting vertical LNC distribution had achieved relatively higher accuracy than that based on the full range of the raw hyperspectral reflectance (R), the LNC determination coefficient (R2val) of the validation dataset was 0.832, 0.857 and 0.811 for the upper, middle and lower layer, and the relative percentage deviations (RPDval) were 2.444, 2.432, and 2.181, respectively. Eventually, ten bands were selected as the effective wavelengths for predicting the vertical LNC distribution in the upper, middle and lower layer, respectively. Newly developed CWT-1-PLS model using the effective wavelengths for LNC prediction in different layers also performed well (RPDval>2.0) based on the field experiments validation. Moreover, the validation at the farmers’ fields also showed fine precision for upper (RPDval=2.012), middle (RPDval=2.137) and lower (RPDval=1.881) layer LNC prediction. These results are of great significance for the study of summer maize leaf reflectance modelling, especially for the studies of integrating hyperspectral measurements and leaf traits data.

Hyperspectral Reflectance of Light-Adapted Leaves Can Predict Both Dark- and Light-Adapted Chl Fluorescence Parameters, and the Effects of Chronic Ozone Exposure on Date Palm (Phoenix dactylifera).

High-throughput and large-scale measurements of chlorophyll a fluorescence (ChlF) are of great interest to investigate the photosynthetic performance of plants in the field. Here, we tested the capability to rapidly, precisely, and simultaneously estimate the number of pulse-amplitude-modulation ChlF parameters commonly calculated from both dark- and light-adapted leaves (an operation which usually takes tens of minutes) from the reflectance of hyperspectral data collected on light-adapted leaves of date palm seedlings chronically exposed in a FACE facility to three ozone (O3) concentrations (ambient air, AA; target 1.5 × AA O3, named as moderate O3, MO; target 2 × AA O3, named as elevated O3, EO) for 75 consecutive days. Leaf spectral measurements were paired with reference measurements of ChlF, and predictive spectral models were constructed using partial least squares regression. Most of the ChlF parameters were well predicted by spectroscopic models (average model goodness-of-fit for validation, R2: 0.53–0.82). Furthermore, comparing the full-range spectral profiles (i.e., 400–2400 nm), it was possible to distinguish with high accuracy (81% of success) plants exposed to the different O3 concentrations, especially those exposed to EO from those exposed to MO and AA. This was possible even in the absence of visible foliar injury and using a moderately O3-susceptible species like the date palm. The latter view is confirmed by the few variations of the ChlF parameters, that occurred only under EO. The results of the current study could be applied in several scientific fields, such as precision agriculture and plant phenotyping.

Leaf spectral responses of Poa crymophila to nitrogen deposition and climate change on Qinghai-Tibetan Plateau

Temperature and nitrogen (N) are limited factors on the Qinghai-Tibetan Plateau (QTP). Climate change and increasing N deposition on the QTP have posed threats to the sustainability of alpine ecosystems. To examine plant spectral responses to N deposition and climate change, we measured leaf spectral reflectance of Poa crymophila, a common forage species grown in alpine meadow (AM) and alpine steppe (AS) on the eastern QTP under four N addition treatments (CK, 8, 40, 72 kg N ha −1 yr −1). We used natural climate gradients from colder and drier AS to warmer and wetter AM to simulate the climate change on the QTP. Results showed that both N deposition and climate change had significant effects on entire visible and near-infrared regions (p < 0.05), but the interactive effects were insignificant (p> 0.05). Enhanced visible absorption, increased near-infrared reflection, a red shift for red edge and a blue shift for green peak were observed with increasing N addition levels. The modified red edge simple ratio index and photochemical reflective index increased gradually in response to N addition (p < 0.05). These responses indicated that N deposition may make leaf wider and thicker, increase leaf chlorophyll content and photochemical efficiency, and enhance photosynthetic capacity. Climate warming and wetting had opposite effects from N deposition on leaf reflectance of Poa crymophila. These observations showed the potential to monitor the status of Poa crymophila under future climate change and N deposition with leaf spectral measurements.

In vivo human-like robotic phenotyping of leaf traits in maize and sorghum in greenhouse

In plant phenotyping, leaf-level physiological and chemical trait measurements are needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, enabling automated monitoring with minimal human intervention. In this paper, a plant phenotyping robotic system was developed to realize automated measurement of plant leaf properties. The robotic system comprised of a four Degree of Freedom (DOF) robotic manipulator and a Time-of-Flight (TOF) camera. A robotic gripper was developed to integrate an optical fiber cable (coupled to a portable spectrometer) for leaf spectral reflectance measurement, and a thermistor for leaf temperature measurement. A MATLAB program along with a Graphical User Interface (GUI) was developed to control the robotic system and its components, and for acquiring and recording data obtained from the sensors. The system was tested in a greenhouse using maize and sorghum plants. The results showed that leaf temperature measurements by the phenotyping robot were significantly correlated with those measured manually by a human researcher (R2 = 0.58 for maize and 0.63 for sorghum). The leaf spectral measurements by the phenotyping robot predicted leaf chlorophyll, water content and potassium with moderate success (R2 ranged from 0.52 to 0.61), whereas the prediction for leaf nitrogen and phosphorus were poor. The total execution time to grasp and take measurements from one leaf was 35.5 ± 4.4 s for maize and 38.5 ± 5.7 s for sorghum. Furthermore, the test showed that the grasping success rate was 78% for maize and 48% for sorghum. The phenotyping robot can be useful to complement the traditional image-based high-throughput plant phenotyping in greenhouses by collecting in vivo leaf-level physiological and biochemical trait measurements.

Sentinel‐2 accurately maps green‐attack stage of European spruce bark beetle (Ips typographus, L.) compared with Landsat‐8

AbstractNatural disturbances induced by insect outbreaks have increased in forest ecosystems over the past decades. To minimize economic loss and prevent a mass outbreak, early detection of bark beetle green attack – a period when trees have yet to show visual signs of infestation stress – is therefore crucial to effective and timely forest management. In this study, we evaluated the ability of spectral vegetation indices extracted from Landsat‐8 and Sentinel‐2 imagery to map bark beetle green attack using principal component analysis (PCA) and partial least square discriminate analysis (PLS‐DA). A recent infestation map produced through visual interpretation of high‐resolution aerial photographs validated the final infestation output maps. Leaf spectral measurements alongside total chlorophyll and nitrogen concentration, leaf water content and leaf dry matter content were measured to assess the impact of bark beetle green attack on foliar properties. We observed that the majority of spectral vegetation indices (SVIs) calculated from Sentinel‐2, particularly red‐edge dependent indices (NDRE2 and 3) and water‐related indices (SR‐SWIR,NDWI,DSWIandLWCI), were able to discriminate healthy from infested plots. In contrast, only the water‐related indices (NDWI,DSWIandRDI) from Landsat‐8 were able to discriminate between healthy and infested plots efficiently. The total number of pixels identified as harboring a green attack that matched with ground truth data (aerial photography) was higher for Sentinel‐2 (67%) than for Landsat‐8 (36%)SVIs, indicating the elevated sensitivity of Sentinel‐2 imagery to changes induced by bark beetle green attack. We also determined that foliar chlorophyll and leaf water content were significantly higher (P &lt; 0.05) in healthy trees than in green‐attacked trees. Our study highlights the potential of Sentinel‐2 data for the early detection of bark beetle infestations and the production of reliable infestation maps at the green‐attack stage.

Impact of Vertical Canopy Position on Leaf Spectral Properties and Traits across Multiple Species

Understanding the vertical pattern of leaf traits across plant canopies provide critical information on plant physiology, ecosystem functioning and structure and vegetation response to climate change. However, the impact of vertical canopy position on leaf spectral properties and subsequently leaf traits across the entire spectrum for multiple species is poorly understood. In this study, we examined the ability of leaf optical properties to track variability in leaf traits across the vertical canopy profile using Partial Least Square Discriminatory Analysis (PLS-DA). Leaf spectral measurements together with leaf traits (nitrogen, carbon, chlorophyll, equivalent water thickness and specific leaf area) were studied at three vertical canopy positions along the plant stem: lower, middle and upper. We observed that foliar nitrogen (N), chlorophyll (Cab), carbon (C), and equivalent water thickness (EWT) were higher in the upper canopy leaves compared with lower shaded leaves, while specific leaf area (SLA) increased from upper to lower canopy leaves. We found that leaf spectral reflectance significantly (P ≤ 0.05) shifted to longer wavelengths in the ‘red edge’ spectrum (685–701 nm) in the order of lower &gt; middle &gt; upper for the pooled dataset. We report that spectral bands that are influential in the discrimination of leaf samples into the three groups of canopy position, based on the PLS-DA variable importance projection (VIP) score, match with wavelength regions of foliar traits observed to vary across the canopy vertical profile. This observation demonstrated that both leaf traits and leaf reflectance co-vary across the vertical canopy profile in multiple species. We conclude that canopy vertical position has a significant impact on leaf spectral properties of an individual plant’s traits, and this finding holds for multiple species. These findings have important implications on field sampling protocols, upscaling leaf traits to canopy level, canopy reflectance modelling, and subsequent leaf trait retrieval, especially for studies that aimed to integrate hyperspectral measurements and LiDAR data.

Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat

Detection of yellow rust is of great importance in disease control and reducing the use of fungicide. Spectral analysis is an important method for disease detection in terms of remote sensing. In this study, an emerging spectral analysis method known as continuous wavelet analysis (CWA) was examined and compared with several conventional spectral features for the detection of yellow rust disease at a leaf level. The leaf spectral measurements were made by a spectroradiometer at both Zodaks 37 and 70 stages with a large sample size. The results showed that the wavelet features were able to capture the major spectral signatures of yellow rust, and exhibited considerable potential for disease detection at both growth stages. Both the accuracies of the univariate and multivariate models suggested that wavelet features outperformed conventional spectral features in quantifying disease severity at leaf level. Optimal accuracies returned a coefficient of determination (R2) of 0.81 and a root mean square error (RMSE) of 0.110 for pooled data at both stages. Furthermore, wavelet features showed a stronger response to the yellow rust at Zodaks 70 stage than at Zodaks 37 stage, indicating reliable estimation of disease severity can be made until the Zodaks 70 stage.

Estimation of leaf nitrogen and silicon using hyperspectral remote sensing

The potential to estimate the nutrient status in important agricultural crops such as maize and sugarcane is of significant interest. In South African sugarcane agriculture, just like in global ecosystem, the estimation of Nitrogen (N) and Silicon (Si) is very important. These nutrients are one of the factors influencing the prevalence of the stalk borer Eldana saccharina Walker (Lepidoptera: Pyralidae). Therefore, the researchers aim at estimating leaf N and Si concentration as well as their ratio in sugarcane using hyperspectral remote sensing (spectroradiometry) for monitoring E. saccharina. A hand-held Analytical Spectral Devices (ASD) Field Spec® 3 spectroradiometer was used to take leaf spectral measurements of sugarcane plants from a potted-plant trial taking place under shade house conditions. In this trial, nitrogen and silicon nutrient applications as well as varieties used were known. In addition, watering regimes and artificial infestation of E. saccharina were carefully controlled. The study results indicate that the Red-edge Index (R740/R720) is linearly related to N concentration (R 2 = 0.81, Root Mean Square Error (RMSE) = 0.103) for N37 with the highest correlation coefficient. For Si, the index (R 750 -R 560 )/(R 750 +R 560 ) was linearly related to Si concentration (R 2 = 0.53, RMSE = 0.118) for N25. Finally, the N:Si ratio was linearly correlated to the index (R 1075 -R 730 )/(R 1075 +R 730 ) (R 2 = 0.67, RMSE = 1.508) for N37, hence this index can be used for early detection of E. saccharina damage or for identifying sugarcane that is prone to attack by E. saccharina. It was concluded that hyperspectral remote sensing has potential for use in estimating the N:Si ratio and E. saccharina potential infestations can be monitored rapidly and nondestructively in sugarcane under controlled conditions. It is recommended that an advanced study be conducted in field conditions using airborne and/or spaceborne hyperspectral sensors.

A canopy reflectance model based on an analytical solution to the multiple scattering equation

An approximation to the radiative transfer equation for solar radiation in relatively full, homogeneous plant canopies is presented and solved analytically for solar zenith angles less than 60°. The model predicts reflectance at any depth in the canopy and in any direction and may be inverted with bidirectional reflectance measurements. It may be analytically integrated over the hemisphere at any depth, which makes it useful for energy balance studies, in particular for calculating albedo and intercepted photosynthetically active radiation. The fact that it may be calibrated with directional reflectance measurements and the exact nature of the solution may make it an improvement over the two-stream approximation as applied to canopy scattering problems. No biophysical or single leaf spectral measurements are needed for this inversion, only physically based bounds on the parameters. The model was fit to data at two sun angles and two wavebands (visible and NIR) to within the assumed errors on the reflectance data. The calculated albedos are insensitive to achievable measurement errors. Some of the parameter values themselves found by the inversion agree reasonably well with independent measurements, but the uncertainties introduced by the data noise are rather large. However, the agreement is good enough to demonstrate that the model is physically realistic.

Leaf spectral measurements to screen cotton strains for characters affected by stress

Abstract The potential usefulness of leaf spectral measurements to screen cotton ( Gossypium hirsutum L.) strains for characters affected by stress was investigated. Laboratory spectral measurements (reflectance, transmittance, and absorptance) were made on upper (adaxial) leaf surfaces of irrigated and nonphotoperiodic cotton strains; ‘T25’, drought resistant and ‘T169’, drought susceptible; that were grown in a rainout shelter in Lubbock, Texas in 1982. Leaves of irrigated T25 plants had lower reflectance at 0.55 μm than did T169 leaves of irrigated plants, which appeared to be related to their total chlorophyll concentrations. The T25 leaves from irrigated plants had higher reflectance at 0.85 μm than did T169 leaves of nonirrigated plants. In the water absorptance region, 1.35–2.5 μm waveband, the less succulent leaves fo the T169 strain had higher transmittance values, for example, than did the more succulent leaves of the T25 strain. These results indicate that screening of cotton genotypes for characters such as leaf chlorophyll concentration, succulence, and water content may be feasible by using spectral measurements.