Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of proposed new observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. Extensive OSSEs have been conducted at NASA/ GSFC and NOAA/AOML over the last three decades. These OSSEs determined correctly the quantitative potential for several proposed satellite observing systems to improve weather analysis and prediction prior to their launch, evaluated trade-offs in orbits, coverage and accuracy for space-based wind lidars, and were used in the development of the methodology that led to the first beneficial impacts of satellite surface winds on numerical weather prediction. In this paper, we summarize early applications of global OSSEs to hurricane track forecasting and new experiments using both global and regional models. These experiments are aimed at determining (1) the potential impact of unmanned aerial systems, (2) the relative impact of alternative concepts for space-based lidar winds, and (3) the relative impact of alternative concepts for polar and geostationary hyperspectral sounders.

A new set of Observing System Simulation Experiments (OSSEs) are underway to assess the impact of higher spatial and temporal resolution sounding on hurricane forecast accuracy. To support these studies, we have developed an OSSE retrieval simulation system. The system uses a simulated satellite orbit track to provide sample locations and footprint area of the infrared sounder configuration to be simulated over the region of interest. The data to be sampled are an OSSE nature run developed by the NOAA Atlantic Oceanographic Meteorological Laboratory (AOML) and the University of Miami (UM). The nature run is sampled at the sounder locations and integrated over the sounder footprint area. The resulting averaged profiles are smoothed vertically with simulated averaging kernels for the Atmospheric Infrared Sounder (AIRS) using a linear retrieval simulation to produce calculated temperature and water vapor profiles. With reasonable fidelity, the sampled and smoothed profiles simulate the retrievals we can expect from a sounder like AIRS for the orbit and sampling configurations under test. Three instruments were simulated corresponding to the AIRS 45×45km footprint in LEO, a hypothetical sounder at 2×2km footprint in LEO, and a hypothetical GEO sounder at 5×5km regional and 10km × 10km full disk footprint sizes. RMS error relative to the nature run is calculated to demonstrate the error characteristics of the simulation system. The simulated retrievals as a result of this effort are currently being assessed by NOAA AOML in an OSSE study to determine the impact of advanced hyperspectral infrared sounders on hurricane forecast improvement.

Detection and identification of bacteria are important for health and safety. Hyperspectral imaging offers the potential to capture unique spectral patterns and spatial information from bacteria which can then be used to detect and differentiate bacterial species. Here, hyperspectral imaging has been used to characterize different bacterial colonies and investigate their growth over time. Six bacterial species (Pseudomonas fluorescens, Escherichia coli, Serratia marcescens, Salmonella enterica, Staphylococcus aureus, Enterobacter aerogenes) were grown on tryptic soy agar plates. Hyperspectral data were acquired immediately after, 24 hours after, and 96 hours after incubation. Spectral signatures from bacterial colonies demonstrated repeatable measurements for five out of six species. Spatial variations as well as changes in spectral signatures were observed across temporal measurements within and among species at multiple wavelengths due to strengthening or weakening reflectance signals from growing bacterial colonies based on their pigmentation. Between-class differences and within-class similarities were the most prominent in hyperspectral data collected 96 hours after incubation.

OSIRIS-REx is the third spacecraft in the NASA New Frontiers Program and is planned for launch in 2016. OSIRIS-REx will orbit the near-Earth asteroid (101955) Bennu, characterize it, and return a sample of the asteroid’s regolith back to Earth. The Regolith X-ray Imaging Spectrometer (REXIS) is an instrument on OSIRIS-REx designed and built by students at MIT and Harvard. The purpose of REXIS is to collect and image sun-induced fluorescent X-rays emitted by Bennu, thereby providing spectroscopic information related to the elemental makeup of the asteroid regolith and the distribution of features over its surface. Telescopic reflectance spectra suggest a CI or CM chondrite analog meteorite class for Bennu, where this primitive nature strongly motivates its study. A number of factors, however, will influence the generation, measurement, and interpretation of the X-ray spectra measured by REXIS. These include: the compositional nature and heterogeneity of Bennu, the time-variable solar state, X-ray detector characteristics, and geometric parameters for the observations. In this paper, we will explore how these variables influence the precision to which REXIS can measure Bennu’s surface composition. By modeling the aforementioned factors, we place bounds on the expected performance of REXIS and its ability to ultimately place Bennu in an analog meteorite class.

The Prototype Hyspiri-TIR (PHyTIR) instrument was developed under NASA’s instrument incubator program and is now operational in the laboratory. The scan head uses state-of-the-art focal plane technology to rapidly acquire data from an eight inch telescope focused at infinite, reflective relay and continuously rotating scan mirror. A series of narrowband interference filters are placed in close proximity to the focal plane. Arrays of 256×16 Mercury Cadmium detectors are under each filter. The detectors have their long wave cutoff at 13.2μm. The filters serve to block out unwanted radiation from other spectral channels, hence forming a high performance multi-band imager with the use of the scanning mirror.

The NASA ESTO funded Multislit Optimized Spectrometer (MOS) Instrument Incubator Program advances a spatial multiplexing spectrometer for coastal ocean remote sensing from laboratory demonstration to flight-like environment testing. The multiple slit design reduces the required telescope aperture leading to mass and volume reductions over conventional spectrometers when applied to the GEO-CAPE oceans mission. This paper discusses the performance and characterization of the MOS instrument from laboratory and thermal vacuum testing. It also presents the current technology readiness level and possible future applications. Results of an ocean color data product simulation study using flight-like performance data from MOS are also covered. The MOS instrument implementation for GEO-CAPE provides system benefits that may lead to measurable cost savings and reductions in risks while meeting its science objectives.

Spectroscopy was first used in 1814 by Joseph von Fraunhofer as a scientific method for discovery, and to develop and test scientific hypotheses. From this beginning, spectroscopy evolved to a broadly used analytical tool for both science and applications. In the 1970’s, technology began to enable a class of instruments that measure spectra for every point in an image. The first airborne imaging spectrometer developed at the Jet Propulsion Laboratory flew in 1982. Subsequently, a wide range of imaging spectrometers have been developed, many at the Jet Propulsion Laboratory, for airborne and space platforms and they have participated in NASA mission throughout the solar system. A key lesson over this time period has been the broad applicability of imaging spectrometers to pursue a range of science and application objectives wherever there is relevant signal in the spectral range from the ultra violet to the thermal infrared. As with all optical imaging instruments, imaging spectrometers have spectral, radiometric and spatial characteristics and related requirements. Of these, uniformity, radiometric precision, and calibration have been identified as critically important for the science and application utility of imaging spectrometer instruments. These key requirements are enabling for the most advanced imaging spectrometer algorithms that retrieve parameters with units and quantifiable uncertainties. The current trend in imaging spectrometer instrumentation is for broader spectral coverage and wider swath while improving uniformity, precision, and calibration. A companion emphasis is for lower mass, power and volume, with instruments taking advantage of the latest detector, optical, electronics and computational technologies. The number of imaging spectrometers in use is increasing every year and this trend is on track to continue.

The Compact Wide Swath Imaging Spectrometer (CWIS) is a pushbroom imaging spectrometer for the solar reflected spectrum (380-2500 nm) with wide swath (1600 elements), fast optical speed (F/1.8), and high uniformity (≥95%). The CWIS compact Dyson demonstrates a reduction in volume and mass over the equivalent Offner-type instrument. CWIS is currently under development at the Jet Propulsion Laboratory and is intended to address the need for high signal to noise ratio compact imaging spectrometer systems for the visible short wave infrared wavelength range. Optical design, stray light modeling, and current status of the instrument are discussed.

We describe a fast, uniform, low-polarization imaging spectrometer and telescope system that can be integrated in a 6U CubeSat. The spectral range is 350-1700 nm, with 5.7 nm sampling. The telescope and spectrometer operate at F/1.8. At 100 mm focal length, the telescope is the highest resolution form that can fit in the CubeSat frame without deployable mirrors. The field of view is 10° with 600 cross-track pixels. The spectrometer is designed for the new Teledyne CHROMA detector array with 30μm pixel size for maximizing throughput. The primary intended applications are coastal ocean and snow cover monitoring.

We report on the design, tolerancing, and laboratory breadboard of an imaging spectrometer for the Earth Science Decadal Survey Hyperspectral and Infrared Imager (HyspIRI) mission. The spectrometer is of the Offner type but with a much longer slit than typical designs, with 1600 resolvable spatial elements along the slit for a length of 48 mm. Two such spectrometers cover more than the required swath while maintaining high throughput and signal-to-noise thanks to the large pixel size (30 μm), relatively high speed (F/2.8) and small number of reflections. We also demonstrate a method for measuring smile using a linear array, and use the method to prove the achievement of negligible smile of less than 2% of a pixel over the entire 48 mm slit. Thus we show that this high-heritage, all-spherical mirror design can serve the requirements of the HyspIRI mission.

This paper discusses the calculation, interpretation, and implications of various radiometric sensitivity metrics for Earth-observing hyperspectral imaging (HSI) sensors. The most commonly used sensor performance metric is signal-to-noise ratio (SNR), from which additional noise equivalent quantities can be computed, including: noise equivalent spectral radiance (NESR), noise equivalent delta reflectance (NEΔρ), noise equivalent delta emittance (NEΔƐ), and noise equivalent delta temperature (NEΔT). For hyperspectral sensors, these metrics are typically calculated from an at-aperture radiance (typically generated by MODTRAN) that includes both target radiance and non-target (atmosphere and background) radiance. Unfortunately, these calculations treat the entire at-aperture radiance as the desired signal, even when the target radiance is only a fraction of the total (such as when sensing through a long or optically dense atmospheric path). To overcome this limitation, an augmented set of metrics based on contrast signal-to-noise ratio (CNSR) is developed, including their noise equivalent counterparts (CNESR, CNEΔρ, CNEΔƐ, and CNEΔT). These contrast metrics better quantify sensor performance in an operational environment that includes remote sensing through the atmosphere.

In the LWIR, often one assumes a scene does not contain solar reflection. To test this assumption, a simple scene model is analyzed for two BRDFs: Lambertian and measured data. For certain geometries, non-negligible solar reflection is observed at 8, 10, and 12 microns when using the BRDF data. Additionally, the wavelength variation of the pupil plane radiance data differs from the Lambertian case when using a BRDF, not just the magnitude. These results suggest that even in the LWIR, BRDFs should be incorporated to account for solar radiance effects.

Spectral mixing can occur in a number of different ways, which may be linear or non-linear. Perhaps the pixel size of a sensor is just too large so many pixels contain patches of different materials within them resulting in linear mixing of the materials. However, there are more complex situations, such as scattering that occurs in mixtures of vegetation and soil, or intimate mixing of granular materials like soils. Such multiple scattering and microscopic mixtures within pixels have varying degrees of non-linearity. Often enough, scenes may contain cases of both linear and non-linear mixing on a pixel-by-pixel basis. This study compares two approaches for use as generalized methods for un-mixing pixels in a scene that may be linear or non-linear. The first is a kernel-based fully-constrained method for spectral unmixing, which uses a kernel that seeks to capture the linear behavior of albedo in non-linear mixtures of materials. The second method directly converts reflectance to single-scattering albedo (SSA) according to Hapke theory assuming bidirectional scattering at nadir look angles and uses a constrained linear model on the computed albedo values. Multiple scenes of hyperspectral imagery calibrated to reflectance are used to validate the methods. We test the approaches using a HyMAP scene collected over the Waimanalo Bay region in Oahu, Hawaii, as well as an AVIRIS scene collected over the oil spill region in the Gulf of Mexico during the Deepwater Horizon oil incident.

A spatial-spectral metric learning (SSML) framework for hyperspectral image (HSI) classification is proposed. SSML learns a metric by considering both the spectral characteristics and spatial features represented as the mean of neighboring pixels. It first performs the local pixel neighborhood preserving embedding (LPNPE) to reduce the dimensionality of HSI and meanwhile to preserve the spatial local similarity structure. Then, it learns a spectral and spatial distance metric, separately. Finally, the combination of the spectral and spatial metrics yields a joint spatial-spectral metric. It is followed by a nearest neighbor (NN) classifier for HSI classification. SSML shows good performance over the spectral and spatial NN and SVM on the benchmark hyperspectral data set of Indian Pines.

The Limb-imaging Ionospheric and Thermospheric Extreme-ultraviolet Spectrograph (LITES) and GPS Radio Occultation and Ultraviolet Photometry-Colocated (GROUP-C) experiments are being considered for flight aboard the Space Test Program Houston 5 (STP-H5) experiment pallet to the International Space Station (ISS). LITES is a compact imaging spectrograph that makes one-dimensional images of atmospheric and ionospheric ultraviolet (60-140 nm) airglow above the limb of the Earth. The LITES optical design is advantageous in that it uses a toroidal grating as its lone optical surface to create these high-sensitivity images without the need for any moving parts. GROUP-C consists of two instruments: a nadir-viewing ultraviolet photometer that measures nighttime ionospheric airglow at 135.6 nm with unprecedented sensitivity, and a GPS receiver that measures ionospheric electron content and scintillation with the assistance of a novel antenna array designed for multipath mitigation. By flying together, these two experiments form an ionospheric observatory aboard the ISS that will provide new capability to study low- and mid-latitude ionospheric structures on a global scale. This paper presents the design and implementation of the LITES and GROUP-C experiments on the STP-H5 payload that will combine for the first time high-sensitivity in-track photometry with vertical spectrographic imagery of ionospheric airglow to create high-fidelity images of ionospheric structures. The addition of the GPS radio occultation measurement provides the unique opportunity to constrain, as well as cross-validate, the merged airglow measurements.