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NASA NTRS2010

A History of NASA Remote Sensing Contributions to Archaeology

During its long history of developing and deploying remote sensing instruments, NASA has provided a scientific data that have benefitted a variety of scientific applications among them archaeology. Multispectral and hyperspectral instrument mounted on orbiting and suborbital platforms have provided new and important information for the discovery, delineation and analysis of archaeological sites worldwide. Since the early 1970s, several of the ten NASA centers have collaborated with archaeologists to refine and validate the use of active and passive remote sensing for archeological use. The Stennis Space Center (SSC), located in Mississippi USA has been the NASA leader in archeological research. Together with colleagues from Goddard Space Flight Center (GSFC), Marshall Space Flight Center (MSFC), and the Jet Propulsion Laboratory (JPL), SSC scientists have provided the archaeological community with useful images and sophisticated processing that have pushed the technological frontiers of archaeological research and applications. Successful projects include identifying prehistoric roads in Chaco canyon, identifying sites from the Lewis and Clark Corps of Discovery exploration and assessing prehistoric settlement patterns in southeast Louisiana. The Scientific Data Purchase (SDP) stimulated commercial companies to collect archaeological data. At present, NASA formally solicits "space archaeology" proposals through its Earth Science Directorate and continues to assist archaeologists and cultural resource managers in doing their work more efficiently and effectively. This paper focuses on passive remote sensing and does not consider the significant contributions made by NASA active sensors. Hyperspectral data offers new opportunities for future archeological discoveries.

Giardino, Marco J.↗
NASA NTRS2019

A New Discrete Wavelength BUV Algorithm for Consistent Volcanic SO2 Retrievals from Multiple Satellite Missions

This paper describes a new discrete wavelength algorithm developed for retrieving volcanic sulfur dioxide (SO2) vertical column density (VCD) from UV observing satellites. The Multi-Satellite SO2 algorithm (MS_SO2) simultaneously retrieves column densities of sulfur dioxide, ozone, and Lambertian effective reflectivity (LER) and its spectral dependence. It is used operationally to process measurements from the heritage Total Ozone Mapping Spectrometer (TOMS) onboard NASA's Nimbus-7 satellite (N7/TOMS: 1978-1993) and from the current Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR: 2015-) from the Earth-Sun Lagrange (L1) orbit. Results from MS_SO2 algorithm for several volcanic cases were assessed using the more sensitive principal component analysis (PCA) algorithm. The PCA is an operational algorithm used by NASA to retrieve SO2 from hyperspectral UV spectrometers, such as the Ozone Monitoring Instrument (OMI) onboard NASA's Earth Observing System Aura satellite and Ozone Mapping and Profiling Suite (OMPS) onboard NASA-NOAA Suomi National Polar Partnership (SNPP) satellite. For this comparative study, the PCA algorithm was modified to use the discrete wavelengths of the Nimbus-7/TOMS instrument, described in Sect. S1 of the Supplement. Our results demonstrate good agreement between the two retrievals for the largest volcanic eruptions of the satellite era, such as the 1991 Pinatubo eruption. To estimate SO2 retrieval systematic uncertainties, we use radiative transfer simulations explicitly accounting for volcanic sulfate and ash aerosols. Our results suggest that the discrete-wavelength MS_SO2 algorithm, although less sensitive than hyperspectral PCA algorithm, can be adapted to retrieve volcanic SO2 VCDs from contemporary hyperspectral UV instruments, such as OMI and OMPS, to create consistent, multi-satellite, long-term volcanic SO2 climate data records.

Bradford L Fisher↗
DOE OSTI2021

Imaging CdCl 2 defect passivation and formation in polycrystalline CdTe films by cathodoluminescence

Polycrystalline thin-film solar cells are attractive for low-cost photovoltaics, but their efficiencies are hindered by material quality issues. State-of-the-art CdTe solar cells use CdCl 2 annealing treatments whose effects are still being discovered at a fundamental level. Here, a series of CdTe samples with different annealing temperatures is investigated with high-resolution hyperspectral cathodoluminescence mapping measured at both room temperature and low temperature on the same microscopic areas. A statistical analysis over a large number of grains is combined with a local analysis at grain boundaries. The results elucidate the dynamic interplay between grain boundary and intragrain defect passivation and formation, in the midst of grain growth. The CdCl 2 annealing initially contributes to an increase of the grain size and the passivation of both grain boundaries and grain interiors, increasing the overall luminescence and diffusion length. For higher annealing temperatures, a further increase of grain size is counterbalanced by the rise of bulk defects. The results illustrate the tradeoffs that lead to an optimal annealing temperature, as well as new methods for understanding defect passivation and creation in thin film solar cells.

14 SOLAR ENERGY↗
NASA NTRS2019

Surface Biology & Geology Pathfinder Data Analysis Pipeline

NASA's future global orbital mission, currently in development as the Surface Biology and Geology (SBG) Designated Observable study, will acquire relatively high resolution solar-reflected spectroscopy and thermal infrared observations. Innovative processes must be utilized for handling the high volume of data anticipated to be collected, which is anticipated to exceed 100 terabytes/day, greater than NASA's total extant airborne hyperspectral data collection. Collecting, processing/re-processing, disseminating, and exploiting this volume of data presents new challenges. To begin addressing them, NASA is drawing upon the expertise developed from its astrophysics programs to address Earth science and applications. Specifically, NASA is adapting the science processing operations technology developed for the Kepler and TESS planet-hunting missions for imaging spectroscopy data processing. This technology development has been the foundation for the remarkable scientific successes of Kepler and TESS. The Kepler/TESS data processing technology provides a scalable architecture for robust, repeatable, and replicable science and application products while enabling the Earth science community to develop, test, and implement new algorithms. Our effort to leverage this existing capability has begun by ingesting data and applying workflows from the EO-1/Hyperion 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. This pathfinding data processing system will help define the solutions to processing SBG data volumes and will enable the scientific community to interact with the data and processing pipeline to create new science products.

Jenkins, Jon↗
NASA NTRS2012

The HYSPIRI Decadal Survey Mission: Update on the Mission Concept and Science Objectives for Global Imaging Spectroscopy and Multi-Spectral Thermal Measurements

The NASA HyspIRI mission is planned to provide global solar reflected energy spectroscopic measurement of the terrestrial and shallow water regions of the Earth every 19 days will all measurements downlinked. In addition, HyspIRI will provide multi-spectral thermal measurements with a single band in the 4 micron region and seven bands in the 8 to 12 micron region with 5 day day/night coverage. A direct broadcast capability for measurement subsets is also planned. This HyspIRI mission is one of those designated in the 2007 National Research Council (NRC) Decadal Survey: Earth Science and Applications from Space. In the Decadal Survey, HyspIRI was recognized as relevant to a range of Earth science and science applications, including climate: "A hyperspectral sensor (e.g., FLORA) combined with a multispectral thermal sensor (e.g., SAVII) in low Earth orbit (LEO) is part of an integrated mission concept [described in Parts I and II] that is relevant to several panels, especially the climate variability panel." The HyspIRI science study group was formed in 2008 to evaluate and refine the mission concept. This group has developed a series of HyspIRI science objectives: (1) Climate: Ecosystem biochemistry, condition & feedback; spectral albedo; carbon/dust on snow/ice; biomass burning; evapotranspiration (2) Ecosystems: Global plant functional types, physiological condition, and biochemistry including agricultural lands (3) Fires: Fuel status, fire frequency, severity, emissions, and patterns of recovery globally (4) Coral reef and coastal habitats: Global composition and status (5) Volcanoes: Eruptions, emissions, regional and global impact (6) Geology and resources: Global distributions of surface mineral resources and improved understanding of geology and related hazards These objectives are achieved with the following measurement capabilities. The HyspIRI imaging spectrometer provides: full spectral coverage from 380 to 2500 at 10 nm sampling; 60 m spatial sampling with a 150 km swath; and fully downlinked coverage of the Earth's terrestrial and shallow water regions every 19 days to provide seasonal cloud-free coverage of the terrestrial surface. The HyspIRI Multi-Spectral Thermal instrument provides: 8 spectral bands from 4 to 12 microns; 60 m spatial sampling with a 600 km swath; and fully downlinked coverage of the Earth's terrestrial shallow water regions every 5 days (day/night) to provide nominally cloud-free monthly coverage. The HyspIRI mission also includes an on-board processing and direct broadcast capability, referred to as the Intelligent Payload Module (IPM), which will allow users with the appropriate antenna to download a subset of the HyspIRI data stream to a local ground station. These science and science application objectives are critical today and uniquely addressed by the combined imaging spectroscopy, thermal infrared measurements, and IPM direct broadcast capability of HyspIRI. Two key objectives are: (1) The global HyspIRI spectroscopic measurements of the terrestrial biosphere including vegetation composition and function to constrain and reduce the uncertainty in climate-carbon interactions and terrestrial biosphere feedback. (2) The global 8 band thermal measurements to provide improved constraint of fire related emissions. In this paper the current HyspIRI mission concept that has been reviewed and refined to its current level of maturity with a Data Products Symposium, Science Workshop and NASA HWorkshop is presented including traceability between the measurements and the science and science application objectives.

Green, Robert O.↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
NASA NTRS2011

Hyperspectral Biofilm Classification Analysis for Carrying Capacity of Migratory Birds in the South Bay Salt Ponds

Tidal marshes are highly productive ecosystems that support migratory birds as roosting and over-wintering habitats on the Pacific Flyway. Microphytobenthos, or more commonly 'biofilms' contribute significantly to the primary productivity of wetland ecosystems, and provide a substantial food source for macroinvertebrates and avian communities. In this study, biofilms were characterized based on taxonomic classification, density differences, and spectral signatures. These techniques were then applied to remotely sensed images to map biofilm densities and distributions in the South Bay Salt Ponds and predict the carrying capacity of these newly restored ponds for migratory birds. The GER-1500 spectroradiometer was used to obtain in situ spectral signatures for each density-class of biofilm. The spectral variation and taxonomic classification between high, medium, and low density biofilm cover types was mapped using in-situ spectral measurements and classification of EO-1 Hyperion and Landsat TM 5 images. Biofilm samples were also collected in the field to perform laboratory analyses including chlorophyll-a, taxonomic classification, and energy content. Comparison of the spectral signatures between the three density groups shows distinct variations useful for classification. Also, analysis of chlorophyll-a concentrations show statistically significant differences between each density group, using the Tukey-Kramer test at an alpha level of 0.05. The potential carrying capacity in South Bay Salt Ponds is estimated to be 250,000 birds.

Hsu, Wei-Chen↗
NASA NTRS2023

The Mars Atmosphere Water Ice Aerosol Climatology by MRO/CRISM: 5 Mars Years of Observations

We use near-infrared spectra returned from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter (MRO) to provide retrievals of the column-integrated optical depth of water ice aerosols for more than 5 Martian years between Mars Year (MY) 28 at Ls = 112° (27 September 2006) and MY 33 at Ls = 213° (30 August 2016). We have developed a radiative transfer model for this purpose that retrieves aerosol opacity from the water ice aerosol feature near 3.3 μm using CRISM hyperspectral observations for the first time. The resulting retrievals well depict the main features in the water ice aerosol climatology with repeatable patterns every Martian year. The aphelion cloud belt (ACB) is observed between 10°S and 30°N, with peak optical depth around Ls = 90°. Apart from the global dust storm during MY 28, modest interannual variability in the water ice aerosol optical depth is observed in the ACB. The north polar hood is observed at latitudes poleward of 45°N throughout northern spring and summer seasons and reappears in northern winter. The south polar hood is observed in early to mid-southern autumn, reaching latitudes around 60°S, and re-emerging during southern winter between 45°S and 60°S. A lack of water ice aerosols is repeatedly seen in the southern hemisphere throughout southern spring and summer. High optical depth in the ACB is observed over the volcanoes of Olympus Mons, Elysium, and the Tharsis bulge, as well as over Hellas Basin.

Mars↗
NASA NTRS2004

Lunar and Planetary Science XXXV: Image Processing and Earth Observations

The titles in this section include: 1) Expansion in Geographic Information Services for PIGWAD; 2) Modernization of the Integrated Software for Imagers and Spectrometers; 3) Science-based Region-of-Interest Image Compression; 4) Topographic Analysis with a Stereo Matching Tool Kit; 5) Central Avra Valley Storage and Recovery Project (CAVSARP) Site, Tucson, Arizona: Floodwater and Soil Moisture Investigations with Extraterrestrial Applications; 6) ASE Floodwater Classifier Development for EO-1 HYPERION Imagery; 7) Autonomous Sciencecraft Experiment (ASE) Operations on EO-1 in 2004; 8) Autonomous Vegetation Cover Scene Classification of EO-1 Hyperion Hyperspectral Data; 9) Long-Term Continental Areal Reduction Produced by Tectonic Processes.

Source record↗
NASA NTRS2011

Filling-In of Broad Far-Red Solar Lines by Terrestrial Fluorescence and Atmospheric Raman Scattering as Detected by SCIAMACHY Satellite Measurements

Global mapping of terrestrial vegetation fluorescence from space has recently been accomplished with high spectral resolution measurements from the Japanese Greenhouse gases Observing SATellite (GOSAT). These data are of interest because they can potentially provide global information on the functional status of vegetation including light use efficiency and global primary productivity that can be used for global carbon cycle modeling. Quantifying the impact of fluorescence on the O2-A band is important as this band is used for cloud- and aerosol-characterization for other trace-gas retrievals including CO2. Here, we demonstrate that fluorescence information can be derived from space using potentially lower-cost hyperspectral instrumentation, i.e., more than an order of magnitude less spectral resolution than GOSAT, with a relatively simple algorithm. As a demonstration, we use the filling-in of one of the few wide and deep solar Fraunhofer lines in the red and far-red chlorophylla fluorescence bands, the calcium II line near 866 nm, to retrieve fluorescence with the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) satellite instrument. Although the signal from vegetation fluorescence is extremely weak at 866 nm, our results suggest that useful information may be obtained after adjustments are made to the observed spectra to correct for instrumental artifacts. We compare fluorescence from SCIAMACHY with that retrieved at 758 and 770 nm from similarly-corrected GOSAT data as well with the Enhanced Vegetation Index (EVI) from the MODerate-resolution Imaging Spectroradiometer (MODIS). We also show that filling-in occurs at 866 nm over barren areas, possibly originating from luminescent minerals in rock and soil.

Joiner, J.↗
NASA NTRS2023

A Cloud Detection Neural Network for Above-Aircraft Clouds Using Airborne Cameras

We introduce a method convolutional neural networks to detect the presence of clouds in airborne camera images. We quantify the performance of this Cloud Detection Neural Network (CDNN) using human-labeled validation data where we report a 96% accuracy in detecting clouds in testing datasets for both Zenith- viewing and Forward-viewing models. We assess our performance by comparing the flight-averaged cloud fraction of zenith and forward CDNN retrievals, with that of the prototype hyperspectral total-diffuse Sunshine Pyranometer (SPN-S) instrument’s cloud optical depth data. Comparison of the CDNN with the SPN-S on time specific intervals resulted in 93% accuracy for the zenith- viewing CDNN and 84% for the forward- viewing CDNN. The comparison of the CDNNs with the SPN-S on flight-averaged cloud fraction resulted in an agreement of 0.15 for the Forward CDNN and 0.07 for the Zenith CDNN. We then quantify the ability of the CDNN to identify the presence of clouds above the aircraft using a forward- looking camera mounted inside the aircraft cockpit compared to the use of an All- Sky upward-looking camera that is mounted outside the fuselage on top of the aircraft. We present results from the CDNN based on airborne imagery from the NASA Aerosol Cloud Meteorology Interactions Over the Western Atlantic Experiment (ACTIVATE) and the Clouds, Aerosol and Monsoon Processes- Philippines Experiment (CAMP2Ex). For CAMP2Ex 53% of flight dates had above- aircraft cloud fraction above 50%, while for ACTIVATE 52% and 54% of flight dates observed above-aircraft cloud fraction above 50% for 2020 and 2021, respectively.

Joseph D Nied↗
DOE OSTI2024

Local A-Site Phase Segregation Leads to Cs-Rich Regions Showing Accelerated Photodegradation in Mixed-Cation Perovskite Semiconductor Films

We use hyperspectral photoluminescence microscopy to study compositional heterogeneity and its influence on the stability of mixed-cation (formamindinium (FA) and cesium) FA 1-x Cs x Pb(I 0.9 Br 0.1 ) 3 lead halide perovskites with different Cs contents. We observe substantial microscale heterogeneity in the photoluminescence. By correlating photoluminescence maps with time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging, we show that the redder-photoluminescence regions of the perovskite film are associated with Cs-rich compositions. X-ray diffraction measurement and confocal Raman spectroscopy provide evidence for the presence of d-phase CsPbI x Br 3-x in these regions. Photo-aging tests show that these Cs-rich clusters undergo faster photoluminescence decay than the rest of the film. These observations highlight the importance of local heterogeneities and their influence on the stability of halide perovskite semiconductors being studied for optoelectronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

A Modeling and Simulation Study for the GeoXO Atmospheric Composition Instrument (ACX): System Level SO2 and O3 Retrieval Performance as a Function of SNR

NOAA’s Geostationary Extended Observations (GeoXO) program is planning to include a hyperspectral UV/visible atmospheric composition instrument in geostationary orbit slated for operations by the early 2030s. Trace gas retrievals provide critical information about our Earth’s system as it relates to both natural and anthropogenic activity. This work focused on the impacts of the signal-to-noise ratio (SNR) on the retrieval uncertainty of SO2 and O3. An end-to-end physics-based imaging system and scene modeling and simulation framework was developed to assess instrument performance considerations in support of the planned GeoXO atmospheric composition instrument (ACX). The framework is composed of three primary components: 1) simulated at-sensor radiance via radiative transfer models of a known atmospheric composition, 2) a noise model driven by previous and planned sensor specifications, and 3) a physical retrieval algorithm. In this analysis, the tropospheric concentrations of SO2 and O3 in the atmospheric column were varied in the boundary layer (0-3 km) and at an elevation of 5 km with a thickness of 1-2 km; the uncertainty in retrieval was studied for various SNR levels for multiple viewing geometries and solar zenith angles. This effort supports the development of the GeoXO ACX instrument by providing a simulated ACX performance assessment of the system-level retrieval performance as a function of SNR, particularly with respect to the wavelength range of interest (approximately 305 – 340 nm) for SO2 and O3 trace gas retrievals.

Monica Cook↗
DOE OSTI2020

Features of correlation measurements of the parameters of pulsed hyperspectral optical fields using an asymmetric interferometer

Differences in correlation measurements of the parameters of pulsed hyperspectral optical fields using symmetric and asymmetric interferometers are considered. It is shown analytically that the resulting cross-correlation function is sensitive to phase perturbations in the original wave field. The considered setup, which contains a telescopic reflective 4f system of parabolic mirrors in one arm, demonstrates that in the case of an asymmetric interferometer, the presence of aberrations leads to degradation of the reconstructed image, whereas in the case of symmetric interferometers these aberrations do not affect the result. (paper)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2014

Release 2 data products from the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler

The OMPS Limb Profiler (LP) was launched on board the NASA Suomi National Polar-orbiting Partnership (SNPP) satellite in October 2011. OMPS-LP is a limb-scattering hyperspectral sensor that provides ozone profiling capability at 1.5 km vertical resolution from cloud top to 60 km altitude. The use of three parallel slits allows global coverage in approximately four days. We have recently completed a full reprocessing of all LP data products, designated as Release 2, that improves the accuracy and quality of these products. Level 1 gridded radiance (L1G) changes include intra-orbit and seasonal correction of variations in wavelength registration, revised static and intra-orbit tangent height adjustments, and simplified pixel selection from multiple images. Ozone profile retrieval changes include removal of the explicit aerosol correction, exclusion of channels contaminated by stratospheric OH emission, a revised instrument noise characterization, improved synthetic solar spectrum, improved pressure and temperature ancillary data, and a revised ozone climatology. Release 2 data products also include aerosol extinction coefficient profiles derived with the prelaunch retrieval algorithm. Our evaluation of OMPS LP Release 2 data quality is good. Zonal average ozone profile comparisons with Aura MLS data typically show good agreement, within 5-10% over the altitude range 20-50 km between 60 deg S and 60 deg N. The aerosol profiles agree well with concurrent satellite measurements such as CALIPSO and OSIRIS, and clearly detect exceptional events such as volcanic eruptions and the Chelyabinsk bolide in February 2013.

Limb scatter↗
DOE OSTI2023

A review on recent machine learning applications for imaging mass spectrometry studies

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.

47 OTHER INSTRUMENTATION↗
NASA NTRS2022

U.S. Plans for Geostationary Hyperspectral Infrared Sounders

NOAA’s Geostationary Extended Observations (GeoXO) satellite system will provide advanced hyperspectral resolution infrared observations. The first GeoXO Sounder (GXS) will fly in the mid-2030s and provide an unprecedented level of information. The plans are for the GXS to be located at a longitude over the center of the U.S. The GXS will be as much of an improvement over the legacy Geostationary Operational Environmental Satellite (GOES)broad-spectral resolution sounder as the Advanced Baseline Imager (ABI) was to the legacy imager. The GXS will be the U.S. contribution to the global ring of geostationary advanced infrared sounders. The GXS sensor will provide unique information about the vertical structure of moisture, winds, and temperature to support both Numerical Weather Prediction (NWP)and nowcasting applications.

Timothy J Schmit↗