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NASA NTRS1994

Earth observational research using multistage EOS-like data

This grant was funded as a part of a program in which both research and educational impact were intended. Research work under this grant is directed at the understanding and use of future hyperspectral data such as that from imaging spectrometers. Specifically, the objectives of the work were: to prepare suitable means for analyzing data from sensors which have large numbers of spectral bands, to advance the fundamental understanding of the manner in which soils and vegetative materials reflect high spectral resolution optical wavelength radiation, and to maximize the impact of the results on the educational community. Over the life of the grant, the work has thus involved basic earth science research and information system technique understanding and development in a mutually supportive way. However, during the later years it became necessary to focus the work primarily on the first and last areas, due to a steadily decreasing level of effort.

Johannsen, C. J.↗

NASA NTRS2022

Mapping invasive alien species in grassland ecosystems using airborne imaging spectroscopy and remotely observable vegetation functional traits

Lespedeza cuneata (sericea lespedeza; hereafter “sericea”) is an invasive species brought to the U.S. from East Asia in the 1890s to be used as forage. However, it has now become a growing ecological and economic threat in grasslands of several states in the U.S. southern Great Plains including Oklahoma, Kansas, Missouri, and Nebraska. Here, we demonstrate the capability of airborne imaging spectroscopy to map sericea in a large natural grassland within the Tallgrass Prairie Preserve, the largest protected tallgrass prairie in the world, located in northeastern Oklahoma. Through this research, we investigated which remotely observable vegetation functional traits (referring to biochemical, physiological, and structural traits) contribute to distinguishing sericea from cooccurring native species and whether we can detect sericea remotely through quantifying these functional traits using imaging spectroscopic data (also known as hyperspectral data). To achieve these objectives, full-range airborne hyperspectral data with spatial resolution of 1 m were collected from the study area in August 2020. In addition, a total of 12 vegetation functional traits were measured through field sampling for model development. We first identified functional traits that contributed to separating sericea from other species, and then used them in a classification model to detect sericea in our study site. We found total carotenoids (sum of neoxanthin, violaxanthin, antheraxanthin, zeaxanthin, and lutein), chlorophyll a + b (sum of chlorophyll a and chlorophyll b), total nitrogen, canopy height, potassium, and magnesium as the main functional traits contributing to the detection of sericea; an overall classification accuracy of approximately 94% was reported. However, the proposed approach overestimated sericea cover in species-rich plant communities. Overall, our findings demonstrated an essential role for airborne remote sensing in 1) direct mapping of invasive plants and 2) quantifying functional traits associated with success strategies of invasive species. Eventually, experiments like ours can aid in developing large-scale and science-driven management practices to both identify the current extent, and to control the spread of invasive species in grasslands and similar short-stature environments. This will not only improve management practices but will have major societal and economic benefits.

Hamed Gholizadeh↗

NASA NTRS2024

Integrating Manned Aircraft and UAVs for the Prediction, Tracking, and Eradication of Desert Locust Swarms

Desert locusts (Schistocerca gregaria) present an acute threat to the agriculture of certain regions, capable of annihilating vast expanses of farmland and destabilizing food security. In this paper, an aviation-based framework concept is introduced that integrates both manned aircraft and Unmanned Aerial Vehicles (UAVs) to preemptively forecast, localize, and eliminate locust swarms efficiently. The framework begins by leveraging predictive modeling methods to refine search areas and pinpoint high-value Points of Interest (POIs) where locust swarms are most likely to materialize. From there, the proposed two-step model uses optical, infrared, and hyperspectral sensors onboard manned aircraft to conduct long-range surveying of probable swarm locations. Upon detection of a swarm, UAVs equipped with sophisticated sensors, including pheromone detectors, thermal and optical imaging systems, are deployed directly from the aircraft to carry out targeted biopesticide applications. This approach, which capitalizes on the combined strengths of both vehicles, provides the rapid response and large-scale intervention necessary to control locust outbreaks. By targeting locust swarms during their most destructive phase, this paper aims to disrupt the locust life cycle and mitigate their severe agricultural impact, offering a scalable and sustainable solution for food security in afflicted regions.

locust monitoring systems↗

NASA NTRS2003

Early Results from the Odyssey THEMIS Investigation

The Thermal Emission Imaging System (THEMIS) began studying the surface and atmosphere of Mars in February, 2002 using thermal infrared (IR) multi-spectral imaging between 6.5 and 15 m, and visible/near-IR images from 450 to 850 nm. The infrared observations continue a long series of spacecraft observations of Mars, including the Mariner 6/7 Infrared Spectrometer, the Mariner 9 Infrared Interferometer Spectrometer (IRIS), the Viking Infrared Thermal Mapper (IRTM) investigations, the Phobos Termoscan, and the Mars Global Surveyor Thermal Emission Spectrometer (MGS TES). The THEMIS investigation's specific objectives are to: (1) determine the mineralogy of localized deposits associated with hydrothermal or sub-aqueous environments, and to identify future landing sites likely to represent these environments; (2) search for thermal anomalies associated with active sub-surface hydrothermal systems; (3) study small-scale geologic processes and landing site characteristics using morphologic and thermophysical properties; (4) investigate polar cap processes at all seasons; and (5) provide a high spatial resolution link to the global hyperspectral mineral mapping from the TES investigation. THEMIS provides substantially higher spatial resolution IR multi-spectral images to complement TES hyperspectral (143-band) global mapping, and regional visible imaging at scales intermediate between the Viking and MGS cameras.

Christensen, Philip R.↗

DOE OSTI2025

Maps of growing season gross primary production and net ecosystem exchange for Council Road Mile Marker 71, Seward Peninsula, Alaska, [2017-2023]

This data archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025a). Murphy et al. (2025a) evaluated whether incorporating observed Arctic vegetation heterogeneity into ELM, the land model of the Department of Energy’s Energy Exascale Earth System Model (E3SM), improved simulations of tundra carbon cycling. The associated model archive can be found at Murphy et al. (2025b). The study focused on the spatial patterns and net landscape-level growing season productivity and carbon uptake. As part of this evaluation, observationally derived maps of average growing season (June–August) net ecosystem exchange (NEE) and gross primary production (GPP) were developed for the same domain. These maps, which form the dataset described here, integrate eddy covariance flux tower, remote sensing, and vegetation community data to provide spatially explicit benchmarks for model evaluation. The maps provide spatially explicit estimates of average growing season NEE and GPP across 13 tundra vegetation communities within the study domain. By combining flux tower observations with Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral imagery and drone-based normalized difference vegetation index (NDVI), these maps capture the heterogeneity of carbon fluxes associated with different Arctic vegetation types. While they represent average seasonal conditions rather than interannual variability, the maps provide a unique dataset for evaluating model performance, comparing vegetation community contributions to landscape-scale carbon cycling, and supporting regional analyses of Arctic carbon dynamics. This data archive contains 5 m resolution maps of vegetation communities, vegetation community average growing season GPP, and vegetation community average growing season NEE (three *.tif files), a User’s Guide (*pdf file), and Table 1 of the User’s Guide displaying vegetation community coverage and average growing season NEE and GPP values (*.csv file).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗

DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗

NASA NTRS1999

Hyperspectral Remote Sensing and Ecological Modeling Research and Education at Mid America Remote Sensing Center (MARC): Field and Laboratory Enhancement

The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and vegetation in the Land-Between-the Lakes (LBL) using Landsat-TM data. A Landsat-TM scene of the same day was obtained to relate ground measurements to the satellite data. A spectral library has been created for overstory species in LBL. Some of the methods, such as NPDF and IDFD techniques for spectral unmixing and reduction of effects of shadows in classifications- comparison of hyperspectral classification techniques, and spectral nonlinear and linear unmixing techniques, are being tested using the laboratory.

Cetin, Haluk↗

NASA NTRSDate not supplied

COSMIR-H: an Airborne Hyperspectral Microwave Sounder for Thermodynamic Sensing of the Planetary Boundary Layer

The NASA Planetary Boundary Layer (PBL) Incubation Study Team Report lists hyperspectral microwave (HMW) sensors as one of the “essential components” of a future global PBL observing system. HMW sensors will provide profiles of the PBL thermodynamic (temperature and water vapor) structure in synergy with passive infrared sounders and to complement active measurements (e.g., lidar and radar). While many simulation studies have been performed showing the benefit of HMW observations, measurements are needed to demonstrate the efficacy of hyperspectral retrievals. The Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) is an airborne sensor that is currently being modified with digital spectrometers to create a HMW sounder with 4-MHz spectral resolution at 50.0-58.0 GHz and 175-191 GHz. The new CoSMIR-Hyperspectral (CoSMIRH) will collect measurements in July 2024 on the highaltitude NASA ER-2 aircraft. These measurements will advise on current knowledge gaps in HMW sounding and inform decisions on future spaceborne HMW sensors

radiometry↗

NASA NTRS2008

Matching Observations to Model Resolution for Future Weather and Climate Applications

High spatial resolution sounding observations will improve initialization and assimilation into the next generation forecast models and validation of the next generation of climate models. One such advanced sounder concept for low earth orbit is the Advanced Remote-sensing Imaging Emission Spectrometer (ARIES) which proposes to provide highvspatial hyperspectral resolution observations in the mid to longwave infrared. This paper explores the effects of spatial resolution on the errors expected from the combined use of models and observations for representing scene information. We calculate the frequency response of the instrument and model and determine the error at any given spatial frequency. The results show that it is vital to have observations match the spatial resolution of models to minimize the uncertainty in the representation of the scene contents.

spatial resolution↗

NASA NTRSDate not supplied

Pre-Launch Analysis and Test of the Ocean Color Instrument Modulation Transfer Function

Launched in February 2024, the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. The Ocean Color Instrument (OCI) is the primary instrument supporting PACE by collecting accurate radiometric data of the Earth by observing the top of atmosphere reflectance and is a combination hyperspectral (ultra-violet to near infrared) / multiband imager (shortwave infrared). An important aspect of the radiometry is the ability of OCI to resolve contrast from scene to scene to achieve data accuracy requirements. This paper reviews the analysis that was performed to verify the modulation transfer function requirement levied against the instrument and presents the supportive test data collected during pre-launch instrument testing to supplement the analysis. The requirements levied against OCI drove to a 1.2 km2 ground resolution leading to a Nyquist period of 2.4 km (0.417 cycles / km). The analysis is comprehensive with incorporation of as many contributors as practical to provide the clearest possible picture of the margin against requirements. The analysis provided input for additional design trade studies and insight into the aspects of the design that required the most attention during the implementation phase. We describe four separate MTF contributor networks based on the instrument design and mutual exclusivity of contributing factors. The various networks represent the hyperspectral and multiband detection systems separately as well as the along track and cross track imaging dimensions. Ground-test was performed against the MTF primary contributors and the results were used to validate the analysis as will be presented here.

OCI↗

NASA NTRSDate not supplied

Pre-Launch Analysis and Test of the Ocean Color Instrument Modulation Transfer Function

OCI↗

DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

PlantCV v4: Image analysis software for high‐throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.

Schuhl, Haley [Donald Danforth Plant Science Cente↗

DOE OSTI2020

2017 Puerto Rico G-LiHT Metadata

This data package contains metadata for data acquired for sample transects across Puerto Rico using NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager in March 2017. Flights specifically targeted sites with coincident field data collection for NGEE-Tropics, existing forest inventory plots from the USDA-Forest Service Forest Inventory & Analysis (FIA) program and university collaborators, and other research installations (e.g., NEON).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2007

Morphological Feature Extraction for Automatic Registration of Multispectral Images

The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

Plaza, Antonio↗

NASA NTRS2007

Physically-Retrieving Cloud and Thermodynamic Parameters from Ultraspectral IR Measurements

A physical inversion scheme has been developed, dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders, to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1-d) variational multi-variable inversion solution is used to improve an iterative background state defined by an eigenvector-regression-retrieval. The solution is iterated in order to account for non-linearity in the 1-d variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with relatively high accuracy (i.e., error < 1 km). NPOESS Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the Atlantic-THORPEX Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing Cloud Physics Lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and the Hyperspectral Environmental Suite (HES). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on Polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project and the following NPOESS series of satellites.

Zhou, Daniel K.↗

NASA NTRS2014

Pu'u Poli'ahu, Mauna Kea: A Possible Analog for the Hematite Bearing Layer Located in Gale Crater, Mars.

Hyperspectral data detected by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on board Mars Reconnaissance Orbiter (MRO) indicated the presence of a hematite bearing ridge on Mount Sharp situated in the Gale Crater, Mars. [Fraeman]. The presence of this mineral in high concentrations is indicative of possible aqueous origins. [Fraeman] In 2012, Curiosity Rover landed in Gale Crater on Mars. Curiosity's mission is to determine Mars' habitability and is equipped with an advanced suite of scientific instruments that are capable of conducting analyses on rocks and soil. The hematite bearing ridge on Mount Sharp is thought to be a good candidate of study for Curiosity. To better understand this type of terrain, the study of analog sites similar in geologic setting is of great importance. One site thought to be a comparable analog is a cinder cone called Pu'u Poli'ahu located on the summit of Mauna Kea, Hawaiʻi. Poli'ahu is unique among the tephra cones of Mauna Kea because it is thought to have formed in subaqueous conditions approximately 170,000 to 175,000 years ago. [Porter] Consequently located on the inner flanks of Poli'ahu is a rock outcrop that contains hematite. Samples were collected from the outcrop and characterized using the following instruments: Digital Microscope, Panalytical X‐ray diffraction (XRD), and scanning electron microscope (SEM). The initial preparation of the rocks involved documenting each sample by creating powdered samples, thick sections, and photo documentation.

Adams, M. E.↗