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At least 163 records · Page 9

Nearly degenerate two-color impulsive coherent Raman hyperspectral imaging

Impulsive stimulated Raman scattering (ISRS) is a robust technique for studying low frequency (<300 cm –1 ) Raman vibrational modes, but ISRS has faced difficulty in translation to an imaging modality. A primary challenge is the separation of the pump and probe pulses. Here we introduce and demonstrate a simple strategy for ISRS spectroscopy and hyperspectral imaging that uses complementary steep edge spectral filters to separate the probe beam detection from the pump and enables simple ISRS microscopy with a single-color ultrafast laser source. ISRS spectra are obtained that span from the fingerprint region down to <50 cm –1 vibrational modes. Furthermore, hyperspectral imaging and polarization-dependent Raman spectra are also demonstrated.

47 OTHER INSTRUMENTATION↗

Leveraging hyperspectral imaging to identify drought tolerant Populus species and genotypes within species

The aim of this study was to identity variation in drought tolerance across genotypes of Populus deltoides, Populus trichocarpa, and hybrids of the two species. A panel of 102 Populus genotypes, comprising 37 genotypes of P. trichocarpa, 37 of P. deltoides and 28 unique hybrid genotypes (P. trichocarpa x P. deltoides and P. deltoides x P. trichocarpa) were evaluated in the greenhouse under two treatments, well-watered (WW) and drought (DS). Plant physiological data were collected throughout the experiment once the drought treatment began. Throughout the experiment, we tracked soil volumetric water content, pot weight, stomatal conductance, quantum yield of photosystem II, and electron transport rate. In addition to those measurements, upon completion of the experiment, we assessed above and belowground plant biomass, plant height and stem diameter, leaf number, specific leaf area, relative water content, total protein, and total chlorophyll. We obtained hyperspectral signatures of one leaf from each plant at the end of the experiment. Columns BC – LL are hyperspectral averages for one leaf from each plant at each wavelength as described in the column header.

Hyper-spectral imaging, Populus, plant stress tole↗

Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton

Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R2 = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R2 = 0.21 to R2 = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions.

Agriculture↗

Early Detection of Drought Stress in Durum Wheat Using Hyperspectral Imaging and Photosystem Sensing

Wheat, being the third largest U.S. crop and the principal food grain, faces significant risks from climate extremes such as drought. This necessitates identifying and developing methods for early water-stress detection to prevent yield loss and improve water-use efficiency. This study investigates the potential of hyperspectral imaging to detect the early stages of drought stress in wheat. The goal is to utilize this technology as a tool for screening and selecting drought-tolerant wheat genotypes in breeding programs. Additionally, this research aims to systematically evaluate the effectiveness of various existing sensors and methods for detecting early stages of water stress. The experiment was conducted in a durum wheat experimental field trial in Maricopa, Arizona, in the spring of 2019 and included well-watered and water-limited treatments of a panel of 224 replicated durum wheat genotypes. Spectral indices derived from hyperspectral imagery were compared against other plant-level indicators of water stress such as Photosystem II (PSII) and relative water content (RWC) data derived from proximal sensors. Our findings showed a 12% drop in photosynthetic activity in the most affected genotypes when compared to the least affected. The Leaf Water Vegetation Index 1 (LWVI1) highlighted differences between drought-resistant and drought-susceptible genotypes. Drought-resistant genotypes retained 43.36% more water in leaves under well-watered conditions compared to water-limited conditions, while drought-susceptible genotypes retained only 15.69% more. The LWVI1 and LWVI2 indices, aligned with the RWC measurements, revealed a strong inverse correlation in the susceptible genotypes, underscoring their heightened sensitivity to water stress in earlier stages. Several genotypes previously classified based on their drought resistance showed spectral indices deviating from expectations. Results from this research can aid farmers in improving crop yields by informing early management practices. Moreover, this research offers wheat breeders insights into the selection of drought-tolerant genotypes, a requirement that is becoming increasingly important as weather patterns continue to change.

Roy, Bishal (ORCID:0000000199125505)↗

Hyperspectral Infrared Observations of Arctic Snow, Sea Ice, and Non-Frozen Ocean from the RV Polarstern during the MOSAiC Expedition October 2019 to September 2020

This study highlights hyperspectral infrared observations from the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) collected as part of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) deployment on the icebreaker RV Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The ARM M-AERI directly measures the infrared radiance emission spectrum between 520 cm –1 and 3000 cm –1 (19.2–3.3 μm) at 0.5 cm –1 spectral resolution. These ship-based observations provide a valuable set of radiance data for the modeling of snow/ice infrared emission as well as validation data for the assessment of satellite soundings. Remote sensing using hyperspectral infrared observations provides valuable information on sea surface properties (skin temperature and infrared emissivity), near-surface air temperature, and temperature lapse rate in the lowest kilometer. Comparison of the M-AERI observations with those from the DOE ARM meteorological tower and downlooking infrared thermometer are generally in good agreement with some notable differences. Operational satellite soundings from the NOAA-20 satellite were also assessed using ARM radiosondes launched from the RV Polarstern and measurements of the infrared snow surface emission from the M-AERI showing reasonable agreement.

54 ENVIRONMENTAL SCIENCES↗

HAMscope: a snapshot Hyperspectral Autofluorescence Miniscope for real-time molecular imaging

We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel hyperspectral stacks (452-703 nm) or directly infer molecular composition maps from single images. A scalable multi-pass U-Net architecture with transformer-based attention and per pixel uncertainty estimation enables high spatio-spectral fidelity (mean absolute error ∼0.0048) at video rates. While initially demonstrated in plant systems, including lignin, chlorophyll, and suberin imaging in intact poplar and cork tissues, the platform is readily adaptable to other applications such as neural activity mapping, metabolic profiling, and histopathology. We show that the system generalizes to out-of-distribution tissue types and supports direct molecular mapping without the need for spectral unmixing. HAMscope establishes a general framework for compact, uncertainty-aware spectral imaging that combines minimal optics with advanced deep learning, offering broad utility for real-time biochemical imaging across neuroscience, environmental monitoring, and biomedicine.

59 BASIC BIOLOGICAL SCIENCES↗

Support Vector Machines for Hyperspectral Remote Sensing Classification

The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

Gualtieri, J. Anthony↗

Hyperspectral Imagery Data for Remote Sensing

In order for remotely sensed data to be useful in a practical application for agriculture, an information product must be made available to the land management decision maker within 24 to 48 hours of data acquisition. Hyperspectral imagery data is proving useful in differentiation of plant species potentially allowing identification of non-healthy areas and pest infestations within crop fields that may require the farm managers attention. Currently however, extracting the needed site-specific feature information from the vast spectral content of large hyperspectral image files is a labor intensive and time consuming task prohibiting the necessary fast turnaround from raw data to final product. We illustrate the methods, techniques and technologies necessary to produce field-level information products from imagery and other related spatial data that are useful to the farm manager for specific decisions that must be made throughout the growing season. We also propose to demonstrate the cost effectiveness of an integrated system, from acquisition to final product distribution, to utilize imagery for decisions on a working farm in conjunction with a commercial agricultural services company and their crop scouts. The demonstration farm is Chesapeake Farms, a 3000 acre research farm in Chestertown, Maryland on the Eastern Shore and is owned by the DuPont Corporation.

Garegnani, Jerry↗

Supervised Classification Techniques for Hyperspectral Data

The recent development of more sophisticated remote sensing systems enables the measurement of radiation in many mm-e spectral intervals than previous possible. An example of this technology is the AVIRIS system, which collects image data in 220 bands. The increased dimensionality of such hyperspectral data provides a challenge to the current techniques for analyzing such data. Human experience in three dimensional space tends to mislead one's intuition of geometrical and statistical properties in high dimensional space, properties which must guide our choices in the data analysis process. In this paper high dimensional space properties are mentioned with their implication for high dimensional data analysis in order to illuminate the next steps that need to be taken for the next generation of hyperspectral data classifiers.

Jimenez, Luis O.↗

Spectral Band Characterization for Hyperspectral Monitoring of Water Quality

A method for selecting the set of spectral characteristics that provides the smallest increase in prediction error is of interest to those using hyperspectral imaging (HSI) to monitor water quality. The spectral characteristics of interest to these applications are spectral bandwidth and location. Three water quality constituents of interest that are detectable via remote sensing are chlorophyll (CHL), total suspended solids (TSS), and colored dissolved organic matter (CDOM). Hyperspectral data provides a rich source of information regarding the content and composition of these materials, but often provides more data than an analyst can manage. This study addresses the spectral characteristics need for water quality monitoring for two reasons. First, determination of the greatest contribution of these spectral characteristics would greatly improve computational ease and efficiency. Second, understanding the spectral capabilities of different spectral resolutions and specific regions is an essential part of future system development and characterization. As new systems are developed and tested, water quality managers will be asked to determine sensor specifications that provide the most accurate and efficient water quality measurements. We address these issues using data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and a set of models to predict constituent concentrations.

Vermillion, Stephanie C.↗

Clues to Coral Reef Health: Integrating Radiative Transfer Modeling and Hyperspectral Data

An important contribution to coral reef research is to improve spectral distinction between various health states of coral species in areas subject to harmful anthropogenic activity and climate change. New insights into radiative transfer properties of corals under healthy and stressed conditions can advance understandings of ecological processes on reefs and allow better assessments of the impacts of large-scale bleaching and disease events, Our objective was to examine the spectral and spatial properties of hyperspectral sensors that may be used to remotely sense changes in reef community health. We compare in situ reef environment spectra (healthy coral, stressed coral, dead coral, algae, and sand) with airborne hyperspectral data to identify important spectral characteristics and indices. Additionally, spectral measurements over a range of water depths, relief, and bottom types are compared to help quantify bottom-water column influences. In situ spectra were collected in July and August 2002 at the Long Rock site in the Andros Island, Bahamas coastal zone coral reef. Our primary emphasis was on Acropora palmata (or elkhorn coral), a major reef building coral, which is prevalent in the study area, but is suffering from white band disease. A. palmata is currently being, proposed as an endangered species because its populations have severely declined in many areas of the Caribbean. In addition to the A. palmata biotope, we have collected spectra of at least seven other coral biotopes that exist within the study area, each with different coral community composition, density of corals, relief, and size of corals. Coral spectral reflectance was then input into a radiative transfer model, CORALMOD (CM1), which is based on a leaf radiative transfer model. In CM1, input coral reflectance measurements produce modeled reflectance through an inversion at each visible wavelength to provide the absorption spectrum. Initially, we imposed a scattering baseline that is the same regardless of the coral condition and that coral is optically thick and no light is transmitted through coral. Here we will focus on methodology, experimental design, and initial findings of the in situ spectral measurements and preliminary output from the radiative transfer model.

Guild, Liane↗

First Use of an Airborne Thermal Infrared Hyperspectral Scanner for Compositional Mapping

In May 1999, the airborne thermal infrared hyperspectral imaging system, Spatially Enhanced Broadband Array Spectrograph System (SEBASS), was flown over Mon-non Mesa, NV, to provide the first test of such a system for geological mapping. Several types of carbonate deposits were identified using the 11.25 microns band. However, massive calcrete outcrops exhibited weak spectral contrast, which was confirmed by field and laboratory measurements. Because the weathered calcrete surface appeared relatively smooth in hand specimen, this weak spectral contrast was unexpected. Here we show that microscopic roughness not readily apparent to the eye has introduced both a cavity effect and volume scattering to reduce spectral contrast. The macroroughness of crevices and cobbles may also have a significant cavity effect. The diminished spectral contrast is important because it places higher signal-to-noise ratio (SNR) requirements for spectroscopic detection and identification. This effect should be factored into instrumentation planning and interpretations, especially interpretations without benefit of ground truth. SEBASS had the required high SNR and spectral resolution to allow us to demonstrate for the first time the ability of an airborne hyperspectral thermal infrared scanner to detect and identify spectrally subtle materials.

Kirkland, Laurel↗

Mapping Lithologic Units Exposed on the Summit of Mauna Kea Using AVIRIS Hyperspectral Reflectance Data

The Mauna Kea summit region is largely comprised of cinder cones and lava flows that form the cap of the Mauna Kea Volcano. The cones and flows at the summit are part of the Laupahoehoe Volcanic series. The Laupa hoehoe volcanism occurred both during and after the late Pleistocene Makanaka glacial episode at the summit. In addition, a few Laupahoehoe cones have been glacially eroded as evidenced by oversteepened slopes, which suggests that they predate the Makanaka glacial period. Two notable examples of possible preglacial cones are Puu Waiau and Puu Poliahu. These two cones are also significantly altered, most likely by hydrothermal activity that has weakly cemented the materials on the cones. Well-crystalline sulfates (alunite and jarosite), phyllosilicates, and zeolites, have been found in samples collected from altered cones at the summit. In addition, palagonitic tephra, which have nanophase ferric oxide, allophane, and other poorly crystalline forms of weakly altered basaltic glass (i.e., no phyllosilicates), have been described at several locations on Mauna Kea. While several studies have discussed the occurrence of alteration products on Mauna Kea, the distribution of these materials exposed at the summit has not been extensively mapped. Hyperspectral imaging can provide information for identifying and mapping lithologic units containing alteration minerals, such as are found on Mauna Kea. The Airborne Visible Infrared Imaging Spectrometer (AVIRIS) is a hyperspectral imaging instrument that covers the wavelength range from about 0.4 to 2.5 micron in 224 bands, with a band spacing of 10 nm and average band width of 10 nm

Guinness, E. A.↗

Mapping Ungulate Habitats in Yellowstone National Park with Airborne Hyperspectral Data

Mapping vegetation habitats of ungulates (e.g., bison, elk, and deer) is critical to the development of efficient wildlife management and monitoring practices in Yellowstone National Park. Image endmembers were chosen using the ENVI minimum noise fraction, pixel purity index, N-dimensional visualizer approach. The spectral angle mapper algorithm was used to classify the image. This process was applied to low altitude AVIRIS and Probe-1 hyperspectral imagery of the Lamar River/Soda Butte Creek confluence to map several ungulate habitats (e.g., grasses, sedge, sage, aspen, willow, and cottonwood. The results are being compared to field measurements and large-scale color infrared aerial photography to assess mapping accuracy. The use of AVIRIS and Probe-1 data enabled the examination of hyperspectral data collected at different spatial and spectral resolutions.

Terrie, Gregory↗

Hyperspectral Imaging on the International Space Station: An Innovative Approach to Commercial Development of Space

NASA s Space Partnership Division (SPD) was established to promote the commercial development of space by providing access to space ai opportunity to perform commercial research in the microgravity environment. NASA, through SPD, has established Research Partnership Centers (RPC s) that bring the government, universities at private industry together to perform research in space for commercial applica!.!lons. The SPD Office has fostered a re!ationship between an RPC and an aerospace company to perform hyperspectral imaging on the Window Observational Research Facility (WORF) on board the International Space Station (ISS). As a result of this relationship and M the capabilities of the WORF, the ISS will serve the private sector with platform to conduct hyperspectral imaging for commercial research.

Source record↗

Software for Simulation of Hyperspectral Images

A package of software generates simulated hyperspectral images for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport as well as surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, 'ground truth' is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces and the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for and a supplement to field validation data.

Richtsmeier, Steven C.↗

Derivative Analysis of AVIRIS Hyperspectral Data for the Detection of Plant Stress

A remote sensing campaign was conducted over a U.S. Department of Agriculture test site at Shelton, Nebraska. The test field was set off in blocks that were differentially treated with nitrogen. Four replicates of 0-kg/ha to 200-kg/ha, in 50-kg/ha increments, were present. Low-altitude AVIRIS hyperspectral data were collected over the site in 224 spectral bands. Simultaneously, ground data were collected to support the airborne imagery. In an effort to evaluate published, derivative-based algorithms for the detection of plant stress, different derivative-based approaches were applied to the collected AVIRIS image cube. The results indicate that, given good quality hyperspectral imagery, derivative techniques compare favorably with simple, well known band ratio algorithms for detection of plant stress.

Estep, Lee↗

A GEO Hyperspectral Mission For Continental-Scale Carbon Cycle Observations

For both terrestrial and ocean carbon cycle science objectives, a hyperspectral geostationary sensor should enable the development of new remote sensing measurements for important but as yet unobservable variables, and with the overall goal of linking both terrestrial and ocean carbon cycle processes to climate variability. For terrestrial research, accurate estimates of carbon, water and energy (CWE) exchange between the terrestrial biosphere and atmosphere are needed to identify the geographical locations of carbon sources/sinks and to improve regional climate models and global climate change assessments. It is an enormous challenge to estimate CWE exchange from the infrequent temporal coverage provided by most polar-orbiting satellites, and without benefit of spectral indices that capture vegetation responses to stress conditions that down-regulate photosynthesis. Physiological status can be better assessed with spectral indices based on continuous, narrow (5 nm) bands, as can seasonal and annual terrestrial productivity. For coastal and ocean constituents, narrow-band observations in the ultraviolet and visible are essential to investigate the variability, dynamics and biogeochemical cycles of the world s coastal and open ocean regions, which will in turn help in measuring ocean productivity and predicting the variability of Ocean carbon uptake and its role in climate change scenarios. The GSFC Carbon Team has been pursuing a geostationary hyperspectral instrument, which would revolutionize our knowledge of biological processes on land, in the ocean, and along the coast by providing multiple, diurnal coverage. Preliminary studies in Goddard's Instrument Synthesis and Analysis Laboratory (ISAL) indicate that we can meet many of our science requirements: full spectral coverage (360-1000 nm); narrow bandwidths (5-10 nm); adequate ground resolution (100-200 m); and continental-scale coverage 4-6 times per day; all the while achieving a signal to noise ratio of between 500 and 1000 to 1. However, an innovative and bold focal plane design and a large mirror (1.8 meter diameter) would be required. The development of our science requirements and the results of the initial design study will be presented as well as our most recent technological developments.

Gervin, Janette C.↗