Spectral Diffractive Lenses for Measuring a Modified Red Edge Simple Ratio Index and a Water Band Index

We propose a novel type of spectral diffractive lenses that operate in the ±1-st diffraction orders. Such spectral lenses generate a sharp image of the wavelengths of interest in the +1-st and –1-st diffraction orders. The spectral lenses are convenient to use for obtaining remotely sensed vegetation index images instead of full-fledged hyperspectral images. We discuss the design and fabrication of spectral diffractive lenses for measuring vegetation indices, which include a Modified Red Edge Simple Ratio Index and a Water Band Index. We report synthesizing diffractive lenses with a microrelief thickness of 4 µm using the direct laser writing in a photoresist. The use of the fabricated spectral lenses in a prototype scheme of an imaging sensor for index measurements is discussed. Distributions of the aforesaid spectral indices are obtained by the linear scanning of vegetation specimens. Using a linear scanning of vegetation samples, distributions of the above-said water band index were experimentally measured.

Automated Hyperspectral Vegetation Index Derivation Using A Hyperparameter Optimization Framework for High-Throughput Plant Phenotyping

Hyperspectral vegetation indices (VIs) are widely deployed in agriculture remote sensing and plant phenotyping to estimate plant biophysical and biochemical traits. However, existing VIs consist mainly of simple 2-band indices which limits the net performance and often do not generalize well for other traits than they were originally designed for. We present an automated hyperspectral vegetation index (AutoVI) system for the rapid generation of novel 2- to 6-band trait-specific indices in a streamlined process covering model selection, optimization and evaluation driven by the tree parzen estimator algorithm. Its performance was tested in generating novel indices to estimate chlorophyll and sugar contents in wheat. Results show that AutoVI can rapidly generate complex novel VIs (≥4-band index) which correlated strongly (R2 > 0.8) with measured chlorophyll and sugar contents in wheat. AutoVI-derived indices were used as features in simple and stepwise multiple linear regression for chlorophyll and sugar content estimation, and outperformed results achieved with existing 47 VIs and those provided by partial least squares regression. The AutoVI system can deliver novel trait-specific VIs readily adoptable in high-throughput plant phenotyping platforms and should appeal to plant scientists and breeders. A graphical user interface of AutoVI is herein provided.

Hyperspectral reflectance for non-invasive early detection of black shank disease in flue-cured tobacco

Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed.

Ecophysiological Variability of Alnus viridis (Chaix) DC. Green Alder Leaves in the Bieszczady Mountains (Poland)

Alnus viridis (Chaix) DC., green alder, is a fast-growing shrub that grows expansively in the European mountainside. In Poland, A. viridis naturally occurs only in the Bieszczady Mountains (south-eastern part of the country), above the upper forest border. In this study, we assessed the potential of green alder to expand in post-farming areas in the Bieszczady Mountains. We investigated the effects of topographical, climatic, and edaphic characteristics of four various study sites on the physiological and morphological properties of A. viridis leaves in order to answer the question whether the growth of plants in lower positions improves their physiological condition to such an extent that it increases the species invasiveness. This is the first comprehensive ecophysiological study of this species to be carried out in this part of Europe. The photochemical efficiency of PSII, the chlorophyll content, and leaf 13C and 15N discrimination were analyzed. On the basis of leaf radiation reflection, coefficients such as reflectance indices of anthocyanins, carotenoids, flavonoids (ARI2, CRI1, FRI), photochemical index of reflection (PRI), and the water band index (WBI) were calculated. We observed favorable physiological effects in A. viridis plants growing in locations below the upper forest border compared to plants growing in higher locations. As a result, A. viridis may become an invasive species and disturb the phytocoenotic balance of plant communities of the altitudinal zones in the Polish Western Carpathians.

Differential Law and Influencing Factors of Groundwater Depth in the Key Agricultural and Pastoral Zones Driven by the Minimum Hydrological Response Unit

The water cycle in the key agricultural and pastoral zones (KAPZs) is an important factor for maintaining the stability of the ecosystem. Groundwater collection and lateral seepage are indispensable parts of the water cycle, and it is difficult to monitor the groundwater situation in each area. The strength of the alternate circulation of groundwater is directly related to the utilization value and development prospects of groundwater; therefore, creating an effective method for the detection of groundwater burial depth has become an issue of increasing concern. In this paper, we attempt to create a method for the detection of groundwater burial depth that combines cokriging interpolation, spatial autocorrelation, geographically weighted regression, and other methods to construct a quantitative relationship between different land cover types and groundwater depth. By calculating the band index of the land cover type, the groundwater depthinformation of the unknown area can be obtained more accurately. Through collaborative kriging interpolation, normalized difference vegetation index (NDVI), precipitation, and hydrogeological conditions were used as covariates. The groundwater burial depth of Wengniute Banner in 2005, 2009, 2013, and 2017 was the response variable, and the groundwater burial depth in the study area was calculated. The groundwater burial depth data after the cokriging interpolation was used to transform the raster data into vector data in space using the improved hydrological response unit (HRU) model to make it more suitable for the actual groundwater confluence. Subsequently, 551 minimum response units (MHRUs) were obtained by division, and the spatial autocorrelation analysis was performed accordingly. The groundwater burial depth in the study area is spatially distinct from east to west, and the groundwater level shows a trend of being high in the west and low in the east, gradually increasing due to precipitation and rivers. The average change of groundwater depth in the time series is not significant, but it does gradually show a trend of accumulation. According to the aggregation characteristics of spatial autocorrelation analysis, a geographically weighted regression model of groundwater depth and NDVI, normalized difference drought index (NDDI), and net relatedness index (NRI) was established. The NDVI representing the forest land and the Adjusted R2 of the groundwater depth is 0.67. The NRI representing the cultivated land and the Adjusted R2 of the groundwater depth is 0.8675. The NDDI representing the bare land and the Adjusted R2 of the groundwater depth is 0.7875. It shows that the band index representing the ground type has a good fitting effect with the groundwater burial depth.

Acoustomagnetoelectric Effect in Graphene Nanoribbon in the Presence of External Electric and Magnetic Fields

Acoustomagnetoelectric Effect (AME) in Graphene Nanoribbon (GNR) in the presence of an external electric and magnetic fields was studied using the Boltzmann kinetic equation. On open circuit, the Surface Acoustomagnetoelectric field (ESAME) in GNR was obtained in the region ql >> 1, for energy dispersion "(p) near the Fermi level. The dependence of ESAME on the dimensional factor (ɳ), the sub-band index (pi), and the width (N) of GNR were analyzed numerically. For ESAME versus ɳ, a non-linear graph was obtained. From the graph, at ɳ < 0.62, the obtained graph qualitatively agreed with that experimentally observed in graphite. However at ɳ > 0.62, the ⃗ESAME falls rapidly to a minimum value. We observed that in GNR, the maximum ⃗ESAME was obtained at magnetic field H = 3.2Am−1. The graphs obtainedwere modulated by varying the subband index pi with an inversion observed when pi = 6. The dependence of ESAME on the width N for various pi was also studied where, ⃗ESAME decreases for increase in pi. To enhanced the understanding of ESAME on the N and ɳ, a 3D graph was plotted. This study is relevant for investigating the properties of GNR.