Spectral Discrimination of Two Pigweeds from Cotton with Different Leaf Colors

To implement strategies to control Palmer amaranth (Amaranthus palmeri S. Wats.) and redroot pigweed (Amaranthus retroflexus L.) infestations in cotton (Gossypium hirsutum L.) production systems, managers need effective techniques to identify the weeds. Leaf light reflectance measurements have shown promise as a tool to distinguish crops from weeds. Studies have targeted plants with green leaves. This study focused on using leaf hyperspectral reflectance data to develop spectral profiles of Palmer amaranth, redroot pigweed, and cotton and to determine regions of the light spectrum most sensitive for pigweed and cotton discrimination. The study focused on cotton near-isogenic lines created to have bronze, green, or yellow colored leaves. Reflectance measurements within the 400 to 2500 nm spectral range were obtained from cotton and weed plants grown in a greenhouse in 2015 and 2016. Two scenarios were evaluated for the comparison: (1) Palmer amaranth versus cotton lines and (2) redroot pigweed versus cotton lines. Statistical significance (p ≤ 0.05) was determined with analysis of variance (ANOVA) and Dunnett’s test. Sensitivity measurements were tabulated to determine the optimal region of the light spectrum for weed and cotton line discrimination. Optimal bands for weed and cotton separation were 600 to 700 nm (both weeds versus cotton bronze and cotton yellow), 710 nm (Palmer amaranth versus cotton green), and 1460 nm (redroot pigweed versus cotton green). Spectral bands were identified for separating Palmer amaranth and redroot pigweed from cotton lines with bronze, green, and yellow leaves. Ground-based and airborne sensors can be tuned into the regions of spectrum identified, facilitating using remote sensing technology for Palmer amaranth and redroot pigweed identification in cotton production systems.

Analyzing Field-Derived Arboreal Spectral Signatures

Orbital platforms spectral sensitivity can be a major limitation in ascertaining detailed identification and mapping of arboreal ecosystems. Field-derived spectral signatures using a narrow-band sensor, for example, ASD (Analytical Spectral Devices, Boulder, CO, USA) FieldSpec® Pro cover a spectral FR (Full Range) of 350 - 2500 nm exceeding spectral sensitivities of commonly used orbital platforms. The plausibility of deriving a spectral library of trees or forests within a training set is venerable. On the other hand, diagnostic spectral features between tree species or types are inherently difficult to ascertain from orbital platforms. This is so especially when the spectral library is applied to a demarcated region beyond the extents of training set. Basic suborbital limitations in detailed identification of trees and forests are presented in this study. We draw attention to spectral or temporal deficiencies and offer probable solutions depending on preferred or optimal spectral sensitivities. For example, Hyperion with 220 bands (400 - 2500 nm), one of the three primary instruments on the EO-1 spacecraft, has narrow bandwidths and covers the entire range of the spectral profiles collected for North Dakota tree species. With a 30 m spatial resolution, it is still useful in species identification in moderate stands of forest. Hyperion is a tasking satellite with limited passes over North Dakota (≈7% of total area) limiting its use as a platform of choice for statewide forest resource mapping.

Contrasting altitudinal patterns of leaf UV reflectance and absorbance in four herbaceous species on the Qinghai-Tibetan Plateau

Aims Alpine plants have to cope with intense ultraviolet(UV)radiation and its altitudinal changes.It has been argued that leaf UV reflec-tance and absorbance should play a central role in acclimation and adaptation to changes in UV radiation,but evidence is lim-ited from high altitudinal ecosystems.In this study,we assessed whether leaf UV reflectance and leaf pigments jointly vary with altitude in alpine broadleaved herbaceous species.The primary hypothesis is that leaves with higher UV reflectance should have lower UV absorbance and/or lower contents of photosynthetic pigments.Methods Leaf UV reflectance,leaf UV absorbance and photosynthetic pig-ments(chlorophyll a and b,carotenoids)were examined in four broadleaved herbaceous species in relation to their habitat alti-tudes.The leaf surface reflectance and leaf extract absorbance at wavelengths of 305 and 360 nm were measured to examine the leaf optical and photochemical characteristics in the UV-B and UV-A bands,respectively.The species included Saussurea katochaete Maxim.,Saussurea pulchra Lipsch.,Anaphalis lactea Maxim.and Rheum pumilum Maxim.,which are distributed along the same slope from 3200 to 4200 m in the Qilian Mountains,Qinghai-Tibetan Plateau.Important Findings The leaf UV absorbance was approximately twice as high at 305 nm(UV-B)than at 360 nm(UV-A)for all species except R.pumilum.Among the four species,the leaf UV absorbance was the highest and almost all values were within 2-6 Abs cm^(−2)(absorbance cm^(−2))in S.pulchra,but the lowest(frequently<1 Abs cm^(−2))were observed in R.pumilum.Only R.pumilum showed significantly higher values at higher elevations.Leaf UV reflectance was generally higher at higher elevations for all species except for A.lactea,and exhibited much larger altitudinal variations compared to leaf UV absorbance.Anaphalis lactea showed a very high UV reflectance even at low altitudes.Among the four species,photosynthetic pigments tended to decrease with an increase in leaf UV reflectance but increased with leaf UV absorbance.The study suggests that leaf UV reflec-tance,rather than leaf UV absorbance,plays a more active role in acclimation to altitudinal changes in UV radiation,and a high investment in leaf UV reflectance may limit the accumulation of photosynthetic pigments in alpine plants.

Deep chemometrics for nondestructive photosynthetic pigments prediction using leaf reflectance spectra

The need for the rapid assessment of the photosynthetic pigment contents in plants has encouraged the development of studies to produce nondestructive quantification methods.This need is driven by the fact that data on the photosynthetic pigment contents can provide a variety of important information that is related to plant conditions.Using deep chemometrics,we developed a novel one-dimensional convolutional neural network(CNN)model to predict the photosynthetic pigment contents in a nondestructive and real-time manner.Intact leaf reflectance spectra from spectroscopic measurements were used as the inputs.The prediction was simultaneously carried out for three main photosynthetic pigments,i.e.,chlorophyll,carotenoid and anthocyanin.The experimental results show that the prediction accuracy is very satisfying,with a mean absolute error(MAE)=0.0122±0.0004 for training and 0.0321±0.0022 for validation(data range of 0–1).