METAPHOR: Metabolic evaluation through phasor-based hyperspectral imaging and organelle recognition for mouse blastocysts and oocytes.

Only 30% of embryos from in vitro fertilized oocytes successfully implant and develop to term, leading to repeated transfer cycles. To reduce time-to-pregnancy and stress for patients, there is a need for a diagnostic tool to better select embryos and oocytes based on their physiology. The current standard employs brightfield imaging, which provides limited physiological information. Here, we introduce METAPHOR: Metabolic Evaluation through Phasor-based Hyperspectral Imaging and Organelle Recognition. This non-invasive, label-free imaging method combines two-photon illumination and AI to deliver the metabolic profile of embryos and oocytes based on intrinsic autofluorescence signals. We used it to classify i) mouse blastocysts cultured under standard conditions or with depletion of selected metabolites (glucose, pyruvate, lactate); and ii) oocytes from young and old mouse females, or in vitro-aged oocytes. The imaging process was safe for blastocysts and oocytes. The METAPHOR classification of control vs. metabolites-depleted embryos reached an area under the ROC curve (AUC) of 93.7%, compared to 51% achieved for human grading using brightfield imaging. The binary classification of young vs. old/in vitro-aged oocytes and their blastulation prediction using METAPHOR reached an AUC of 96.2% and 82.2%, respectively. Finally, organelle recognition and segmentation based on the flavin adenine dinucleotide signal revealed that quantification of mitochondria size and distribution can be used as a biomarker to classify oocytes and embryos. The performance and safety of the method highlight the accuracy of noninvasive metabolic imaging as a complementary approach to evaluate oocytes and embryos based on their physiology.

Capturing patterns of evolutionary relatedness with reflectance spectra to model and monitor biodiversity.

Biogeographic history can set initial conditions for vegetation community assemblages that determine their climate responses at broad extents that land surface models attempt to forecast. Numerous studies have indicated that evolutionarily conserved biochemical, structural, and other functional attributes of plant species are captured in visible-to-short wavelength infrared, 400 to 2,500 nm, reflectance properties of vegetation. Here, we present a remotely sensed phylogenetic clustering and an evolutionary framework to accommodate spectra, distributions, and traits. Spectral properties evolutionarily conserved in plants provide the opportunity to spatially aggregate species into lineages (interpreted as "lineage functional types" or LFT) with improved classification accuracy. In this study, we use Airborne Visible/Infrared Imaging Spectrometer data from the 2013 Hyperspectral Infrared Imager campaign over the southern Sierra Nevada, California flight box, to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from 1372 surveys and a phylogeny representing 1,572 species. Despite temporal and spatial differences in our training data, we classified plant lineages with moderate reliability (Kappa = 0.76) and overall classification accuracy of 80.9%. We present an assessment of classification error and detail study limitations to facilitate future LFT development. This work demonstrates that lineage-based methods may be a promising way to leverage the new-generation high-resolution and high return-interval hyperspectral data planned for the forthcoming satellite missions with sparsely sampled existing ground-based ecological data.

Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines.

Leaf rust (Puccinia triticina Eriks) is a wheat disease causing substantial yield losses in wheat production globally. The identification of genetic resources with permanently effective resistance genes and the generation of mutant lines showing increased levels of resistance allow the efficient incorporation of these target genes into germplasm pools by marker-assisted breeding. In this study, new mutant (M3 generation) lines generated from the rust-resistant variety Kazakhstanskaya-19 were developed using gamma-induced mutagenesis through 300-, 350-, and 400-Gy doses. In field trials after leaf rust inoculation, 75 mutant lines showed adult plant resistance. These lines were evaluated for resistance at the seedling stage via microscopy in greenhouse experiments. Most of these lines (89.33%) were characterized as resistant at both developmental stages. Hyperspectral imaging analysis indicated that infected leaves of wheat genotypes showed increased relative reflectance in visible and near-infrared light compared to the non-infected genotypes, with peak means at 462 and 644 nm, and 1936 and 2392 nm, respectively. Five spectral indexes, including red edge normalized difference vegetation index (RNDVI), structure-insensitive pigment index (SIPI), ratio vegetation index (RVSI), water index (WI), and normalized difference water index (NDWI), demonstrated significant potential for determining disease severity at the seedling stage. The most significant differences in reflectance between susceptible and resistant mutant lines appeared at 694.57 and 987.51 nm. The mutant lines developed were also used for the development and validation of KASP markers for leaf rust resistance genes Lr1, Lr2a, Lr3, Lr9, Lr10, and Lr17. The mutant lines had high frequencies of "a" resistance alleles (0.88) in all six Lr genes, which were significantly associated with seedling resistance and suggest the potential of favorable haplotype introgression through functional markers. Nine mutant lines characterized by the presence of "b" alleles in Lr9 and Lr10-except for one line with allele "a" in Lr9 and three mutant lines with allele "a" in Lr10-showed the progressive development of fungal haustorial mother cells 72 h after inoculation. One line from 300-Gy-dosed mutant germplasm with "b" alleles in Lr1, Lr2a, Lr10, and Lr17 and "a" alleles in Lr3 and Lr9 was characterized as resistant based on the low number of haustorial mother cells, suggesting the contribution of the "a" alleles of Lr3 and Lr9.

Large-Scale Analysis of Defects in Atomically Thin Semiconductors using Hyperspectral Line Imaging.

Point defects play a crucial role in determining the properties of atomically thin semiconductors. This work demonstrates the controlled formation of different types of defects and their comprehensive optical characterization using hyperspectral line imaging (HSLI). Distinct optical responses are observed in monolayer semiconductors grown under different stoichiometries using metal-organic chemical vapor deposition. HSLI enables the simultaneous measurement of 400 spectra, allowing for statistical analysis of optical signatures at close to a centimeter scale. The study discovers that chalcogen-rich samples exhibit remarkable optical uniformity due to reduced precursor accumulation compared to the metal-rich case. The utilization of HSLI as a facile and reliable characterization tool pushes the boundaries of potential applications for atomically thin semiconductors in future devices.

Computational Hyperspectral Microflow Cytometry.

Miniaturized flow cytometry has significant potential for portable applications, such as cell-based diagnostics and the monitoring of therapeutic cell manufacturing, however, the performance of current techniques is often limited by the inability to resolve spectrally-overlapping fluorescence labels. Here, the study presents a computational hyperspectral microflow cytometer (CHC) that enables accurate discrimination of spectrally-overlapping fluorophores labeling single cells. CHC employs a dispersive optical element and an optimization algorithm to detect the full fluorescence emission spectrum from flowing cells, with a high spectral resolution of ≈3 nm in the range from 450 to 650 nm. CHC also includes a dedicated microfluidic device that ensures in-focus imaging through viscoelastic sheathless focusing, thereby enhancing the accuracy and reliability of microflow cytometry analysis. The potential of CHC for analyzing T lymphocyte subpopulations and monitoring changes in cell composition during T cell expansion is demonstrated. Overall, CHC represents a major breakthrough in microflow cytometry and can facilitate its use for immune cell monitoring.

Stable Isotope Probing-nanoFTIR for Quantitation of Cellular Metabolism and Observation of Growth-Dependent Spectral Features.

This study utilizes nanoscale Fourier transform infrared spectroscopy (nanoFTIR) to perform stable isotope probing (SIP) on individual bacteria cells cultured in the presence of 13C-labelled glucose. SIP-nanoFTIR simultaneously quantifies single-cell metabolism through infrared spectroscopy and acquires cellular morphological information via atomic force microscopy. The redshift of the amide I peak corresponds to the isotopic enrichment of newly synthesized proteins. These observations of single-cell translational activity are comparable to those of conventional methods, examining bulk cell numbers. Observing cells cultured under conditions of limited carbon, SIP- nanoFTIR is used to identify environmentally-induced changes in metabolic heterogeneity and cellular morphology. Individuals outcompeting their neighboring cells will likely play a disproportionately large role in shaping population dynamics during adverse conditions or environmental fluctuations. Additionally, SIP-nanoFTIR enables the spectroscopic differentiation of specific cellular growth phases. During cellular replication, subcellular isotope distribution becomes more homogenous, which is reflected in the spectroscopic features dependent on the extent of 13C-13C mode coupling or to specific isotopic symmetries within protein secondary structures. As SIP-nanoFTIR captures single-cell metabolism, environmentally-induced cellular processes, and subcellular isotope localization, this technique offers widespread applications across a variety of disciplines including microbial ecology, biophysics, biopharmaceuticals, medicinal science, and cancer research.

Viewing early life without labels: optical approaches for imaging the early embryo†.

Embryo quality is an important determinant of successful implantation and a resultant live birth. Current clinical approaches for evaluating embryo quality rely on subjective morphology assessments or an invasive biopsy for genetic testing. However, both approaches can be inherently inaccurate and crucially, fail to improve the live birth rate following the transfer of in vitro produced embryos. Optical imaging offers a potential non-invasive and accurate avenue for assessing embryo viability. Recent advances in various label-free optical imaging approaches have garnered increased interest in the field of reproductive biology due to their ability to rapidly capture images at high resolution, delivering both morphological and molecular information. This burgeoning field holds immense potential for further development, with profound implications for clinical translation. Here, our review aims to: (1) describe the principles of various imaging systems, distinguishing between approaches that capture morphological and molecular information, (2) highlight the recent application of these technologies in the field of reproductive biology, and (3) assess their respective merits and limitations concerning the capacity to evaluate embryo quality. Additionally, the review summarizes challenges in the translation of optical imaging systems into routine clinical practice, providing recommendations for their future development. Finally, we identify suitable imaging approaches for interrogating the mechanisms underpinning successful embryo development.

An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual Histology.

Conventional histopathology involves expensive and labor-intensive processes that often consume tissue samples, rendering them unavailable for other analyses. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning. First, we developed a custom hyperspectral microscope to nondestructively image the autofluorescence of unstained tissue sections. We then trained a deep learning model to use autofluorescence to generate virtual histologic stains, which avoids the cost and variability of chemical staining procedures and conserves tissue samples. We showed that the virtual images reproduce the histologic features present in the real-stained images using a randomized nonalcoholic steatohepatitis (NASH) scoring comparison study, where both real and virtual stains are scored by pathologists (D.T., A.D.B., R.K.P.). The test showed moderate-to-good concordance between pathologists' scoring on corresponding real and virtual stains. Finally, we developed deep learning-based models for automated NASH Clinical Research Network score prediction. We showed that the end-to-end automated pathology platform is comparable with an independent panel of pathologists for NASH Clinical Research Network scoring when evaluated against the expert pathologist consensus scores. This study provides proof of concept for this virtual staining strategy, which could improve cost, efficiency, and reliability in pathology and enable novel approaches to spatial biology research.

Hyperspectral signals in the soil: Plant-soil hydraulic connection and disequilibrium as mechanisms of drought tolerance and rapid recovery.

Predicting soil water status remotely is appealing due to its low cost and large-scale application. During drought, plants can disconnect from the soil, causing disequilibrium between soil and plant water potentials at pre-dawn. The impact of this disequilibrium on plant drought response and recovery is not well understood, potentially complicating soil water status predictions from plant spectral reflectance. This study aimed to quantify drought-induced disequilibrium, evaluate plant responses and recovery, and determine the potential for predicting soil water status from plant spectral reflectance. Two species were tested: sweet corn (Zea mays), which disconnected from the soil during intense drought, and peanut (Arachis hypogaea), which did not. Sweet corn's hydraulic disconnection led to an extended 'hydrated' phase, but its recovery was slower than peanut's, which remained connected to the soil even at lower water potentials (-5 MPa). Leaf hyperspectral reflectance successfully predicted the soil water status of peanut consistently, but only until disequilibrium occurred in sweet corn. Our results reveal different hydraulic strategies for plants coping with extreme drought and provide the first example of using spectral reflectance to quantify rhizosphere water status, emphasizing the need for species-specific considerations in soil water status predictions from canopy reflectance.

Inside-out: Synergising leaf biochemical traits with stomatal-regulated water fluxes to enhance transpiration modelling during abiotic stress.

As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant-environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r2 = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal-based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing-based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields.

Hyperspectral imaging reveals small-scale water gradients in apple leaves due to minimal cuticle perforation by Venturia inaequalis conidiophores.

Effects of Venturia inaequalis on water relations of apple leaves were studied under controlled conditions without limitation of water supply to elucidate their impact on the non-haustorial biotrophy of this pathogen. Leaf water relations, namely leaf water content and transpiration, were spatially resolved by hyperspectral imaging and thermography; non-imaging techniques-gravimetry, a pressure chamber, and porometry-were used for calibration and validation. Reduced stomatal transpiration 3-4 d after inoculation coincided with a transient increase of water potential. Perforation of the plant cuticle by protruding conidiophores subsequently increased cuticular transpiration even before visible symptoms occurred. With sufficient water supply, cuticular transpiration remained at elevated levels for several weeks. Infections did not affect the leaf water content before scab lesions became visible. Only hyperspectral imaging was suitable to demonstrate that a decreased leaf water content was strictly limited to sites of emerging conidiophores and that cuticle porosity increased with sporulation. Microscopy confirmed marginal cuticle injury; although perforated, it tightly surrounded the base of conidiophores throughout sporulation and restricted water loss. The role of sustained redirection of water flow to the pathogen's hyphae in the subcuticular space above epidermal cells, to facilitate the acquisition and uptake of nutrients by V. inaequalis, is discussed.

Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets.

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400-700 nm. However, ultra-violet (UV) radiation within 280-400 nm and far-red (FR) radiation within 700-750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. We explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year-1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

Automated hyperspectral imaging for non-invasive characterization of human eye vasculature: A potential tool for ocular vascular evaluation.

The vascular supply to the human eye plays a vital role in maintaining ocular health, making its non-invasive evaluation essential for diagnosing and managing various ocular disorders. This paper presents a novel approach utilizing hyperspectral imaging (HSI) to non-invasively characterize human eye vasculature. The proposed system aims to specifically identify the blood atrium and veins of the human eye at 470 nm and 750 nm, respectively, using quantitative phase analysis and k-means clustering. The study involved capturing diffused reflection spectra and hyperspectral images of the human eye at different wavelengths to reveal distinctive vascular features. The results of ten volunteers demonstrate promising capabilities in automated differentiation of atrium and veins, as well as the potential for mapping varicose veins in the lower limb. This non-invasive and non-contact imaging technique shows great promise in facilitating accurate and detailed evaluation of ocular blood flow, providing valuable information for clinical diagnosis and treatment in ophthalmology and vascular medicine fields.

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal.

PREMISE: Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data. METHODS: Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model. RESULTS: Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species. CONCLUSIONS: Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

Novel melatonin-producing Bacillus safensis EH143 mitigates salt and cadmium stress in soybean.

Recently, microorganism and exogenous melatonin application has been recognized as an efficient biological tool for enhancing salt tolerance and heavy metal detoxification in agriculture crops. Thus, the goal of this study was to isolate and evaluate a novel melatonin-producing plant growth promoting bacterium. With high-throughput whole genome sequencing, phytohormone measurements, expression profiling, and biochemical analysis, we can identify a novel PGPB that produces melatonin and unravel how it promotes soybean growth and development and protects against salt and Cd stress. We identify the melatonin synthesis pathway (tryptophan→tryptamine→serotonin melatonin) of the halotolerant (NaCl > 800 mM) and heavy metal-resistant (Cd >3 mM) rhizobacterium Bacillus safensis EH143 and use it to treat soybean plants subjected to Cd and NaCl stresses. Results show that EH143 will highly bioaccumulate heavy metals and significantly improve P and Ca2+ uptake and the K+/Na+ (93%↑under salt stress) ratio while reducing Cd uptake (49% under Cd stress) in shoots. This activity was supported by the expression of the ion regulator HKT1, MYPB67, and the calcium sensors CDPK5 and CaMK1 which ultimately led to increased plant growth. EH143 significantly decreased ABA content in shoots by 13%, 20%, and 34% and increased SA biosynthesis in shoots by 14.8%, 31%, and 48.2% in control, salt, and Cd-treated plants, upregulating CYP707A1 and CYP707A2 and PAL1 and ICS, respectively. The melatonin content significantly decreased along with a reduced expression of ASMT3 following treatment with EH143; moreover, reduced expression of peroxidase (POD) and superoxide dismutase (SOD) by 134.5% and 39% under salt+Cd stress, respectively and increased level of total amino acids were observed. Whole-genome sequencing and annotation of EH143 revealed the presence of the melatonin precursor tryptophan synthase (trpA, trpB, trpS), metal and other ion regulators (Cd: cadA, potassium: KtrA and KtrB, phosphate: glpT, calcium: yloB, the sodium/glucose cotransporter: sgIT, and the magnesium transporter: mgtE), and enzyme activators (including the siderophore transport proteins yfiZ and yfhA, the SOD sodA, the catalase katA1, and the glutathione regulator KefG) that may be involved in programming the plant metabolic system. As a consequence, EH143 treatment significantly reduced the contents of lipid peroxidation (O2-, MDA, and H2O2) up to 69%, 46%, and 29% in plants under salt+Cd stress, respectively. These findings suggest that EH143 could be a potent biofertilizer to alleviate NaCl and Cd toxicity in crops and serve as an alternative substitute for exogenous melatonin application.

Diverse projected climate change scenarios affect the physiology of broccoli plants to different extents.

Climate change caused by global warming involves crucial plant growth factors such as atmospheric CO2 concentration, ambient temperature or water availability. These stressors usually co-occur, causing intricate alterations in plant physiology and development. This work focuses on how elevated atmospheric CO2 levels, together with the concomitant high temperature, would affect the physiology of a relevant crop, such as broccoli. Particular attention has been paid to those defence mechanisms that contribute to plant fitness under abiotic stress. Results show that both photosynthesis and leaf transpiration were reduced in plants grown under climate change environments compared to those grown under current climate conditions. Furthermore, an induction of carbohydrate catabolism pointed to a redistribution from primary to secondary metabolism. This result could be related to a reinforcement of cell walls, as well as to an increase in the pool of antioxidants in the leaves. Broccoli plants, a C3 crop, grown under an intermediate condition showed activation of those adaptive mechanisms, which would contribute to coping with abiotic stress, as confirmed by reduced levels of lipid peroxidation relative to current climate conditions. On the contrary, the most severe climate change scenario exceeded the adaptive capacity of broccoli plants, as shown by the inhibition of growth and reduced vigour of plants. In conclusion, only a moderate increase in atmospheric CO2 concentration and temperature would not have a negative impact on broccoli crop yields.

Simultaneous mapping of cathodoluminescence spectra and backscatter diffraction patterns in a scanning electron microscope.

Electron backscatter diffraction and cathodoluminescence are complementary scanning electron microscopy modes widely used in the characterisation of semiconductor films, respectively revealing the strain state of a crystalline material and the effect of this strain on the light emission from the sample. Conflicting beam, sample and detector geometries have meant it is not generally possible to acquire the two signals together during the same scan. Here, we present a method of achieving this simultaneous acquisition, by collecting the light emission through a transparent sample substrate. We apply this combination of techniques to investigate the strain field and resultant emission wavelength variation in a deep-ultraviolet micro-LED. For such compatible samples, this approach has the benefits of avoiding image alignment issues and minimising beam damage effects.

Measurements of Methane Emissions from a Biofertilizer Storage Tank Using Ground-Based Hyperspectral Imaging and Flux Chambers.

Open storages of organic material represent potentially large sources of the greenhouse gas methane (CH4), an emissions source that will likely become more common as a part of societal efforts toward sustainability. Hence, monitoring and minimizing CH4 emissions from such facilities are key, but effective assessment of emissions without disturbing the flux is challenging. We demonstrate the capacity of using a novel high-resolution hyperspectral camera to perform sensitive CH4 flux assessments at such facilities, using as a test case a biofertilizer storage tank for residual material from a biogas plant. The camera and simultaneous conventional flux chamber measurements showed emissions of 6.0 ± 1.3 and 13 ± 5.7 kg of CH4 h-1, respectively. The camera measurements covered the whole tank surface of 1104 m2, and the chamber results were extrapolated from measurements over 5 m2. This corresponds to 0.7-1.4% of the total CH4 production at the biogas plant (1330 N m3 h-1 corresponding to 950 kg h-1). The camera could assess the entire tank emission in minutes without disturbing normal operations at the plant and revealed additional unknown emissions from the inlet to the tank (17 g of CH4 h-1) and during the loading of the biofertilizer into trucks (3.1 kg of CH4 h-1 during loading events). This study illustrates the importance of adequate measurement capacity to map methane fluxes and to verify that methane emission mitigation efforts are effective. Given the high methane emissions observed, it is important to reduce methane emissions from open storage of organic material, for example by improved digestion in the biogas reactor, precooling of sludge before storage, or building gastight storage tanks with sealed covers. We conclude that hyperspectral, ground-based remote sensing is a promising approach for greenhouse gas monitoring and mitigation.

Early Detection of Pipeline Natural Gas Leakage from Hyperspectral Imaging by Vegetation Indicators and Deep Neural Networks.

The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt detection. This study explores the utility of Vegetation Indicators (VIs) to reflect vegetation health deterioration, thereby representing leak-induced stress. Despite the acknowledged potential of VIs, their sensitivity and separability remain understudied. In this study, we employed ground vegetation as biosensors for detecting methane emissions from underground pipelines. Hyperspectral imaging from vegetation was collected weekly at both plant and leaf scales over two months to facilitate stress detection using VIs and Deep Neural Networks (DNNs). Our findings revealed that plant pigment-related VIs, modified chlorophyll absorption reflectance index (MCARI), exhibit commendable sensitivity but limited separability in discerning stressed grasses. A NG-specialized VI, the optimized soil-adjusted vegetation index (OSAVI), demonstrates higher sensitivity and separability in early detection of methane leaks. Notably, the OSAVI proved capable of discriminating vegetation stress 21 days after methane exposure initiation. DNNs identified the methane leaks following a 3-week methane treatment with an accuracy of 98.2%. DNN results indicated an increase in visible (VIS) and a decrease in near-infrared (NIR) in spectra due to methane exposure.

Research progress of inland river water quality monitoring technology based on unmanned aerial vehicle hyperspectral imaging technology.

In recent years, increasing demand for inland river water quality precision management has heightened the necessity for real-time, rapid, and continuous monitoring of water conditions. By analyzing the optical properties of water bodies remotely, unmanned aerial vehicle (UAV) hyperspectral imaging technology can assess water quality without direct contact, presenting a novel method for monitoring river conditions. However, there are currently some challenges to this technology that limit the promotion application of this technology, such as underdeveloped sensor calibration, atmospheric correction algorithms, and limitations in modeling non-water color parameters. This article evaluates the advantages and disadvantages of traditional sensor calibration methods and considers factors like sensor aging and adverse weather conditions that impact calibration accuracy. It suggests that future improvements should target hardware enhancements, refining models, and mitigating external interferences to ensure precise spectral data acquisition. Furthermore, the article summarizes the limitations of various traditional atmospheric correction methods, such as complex computational requirements and the need for multiple atmospheric parameters. It discusses the evolving trends in this technology and proposes streamlining atmospheric correction processes by simplifying input parameters and establishing adaptable correction algorithms. Simplifying these processes could significantly enhance the accuracy and feasibility of atmospheric correction. To address issues with the transferability of water quality inversion models regarding non-water color parameters and varying hydrological conditions, the article recommends exploring the physical relationships between spectral irradiance, solar zenith angle, and interactions with water constituents. By understanding these relationships, more accurate and transferable inversion models can be developed, improving the overall effectiveness of water quality assessment. By leveraging the sensitivity and versatility of hyperspectral sensors and integrating interdisciplinary approaches, a comprehensive database for water quality assessment can be established. This database enables rapid, real-time monitoring of non-water color parameters which offers valuable insights for the precision management of inland river water quality.