The Airborne Visible/Infrared Imaging Spectrometer-4 (AVIRIS-4) represents the next generation in the series of airborne imaging spectrometers built by NASA JPL. Commissioned by the Swiss ARES research consortium, AVIRIS-4 is geared towards delivering cutting-edge imaging spectroscopy data for scientific and practical applications as a replacement for its predecessor APEX. AVIRIS-4 is based on a Dyson-type imaging spectrometer design, also employed by NASA-operated AVIRIS-3 and EMIT, and integrates a scaled two-mirror telescope housed in a compact vacuum vessel. This enables airborne measurements in unpressurized aircraft at altitudes ranging from 500 m to 7620 m, achieving image resolutions between 0.3 and 4.5 m with a field of view of 40.2° in 1241 spatial pixels. AVIRIS-4 surpasses previous state-of-the-art sensor heads in signal-to-noise ratio performance and features a spectral range of 375 to 2504 nm and 7.4 nm spectral sampling. The operation, data capture and mission control hardware as well as the calibration and data processing software is developed by UZH, EPFL, and ZHAW. This paper outlines the design and calibration strategies implemented in AVIRIS-4’s development and highlights its performance during its first year of operation in 2024.

The Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument is the newest member of NASA’s AVIRIS airborne imaging spectrometer family. A Dyson pushbroom spectrometer similar to the satellite-based Earth Mineral Dust Source Investigation (EMIT) instrument, AVIRIS-3 offers higher throughput, a higher signal-to-noise ratio, and a more compact form factor than previous AVIRIS generations. AVIRIS-3 relies upon in-flight data to create updates to the wavelength, flatfield, and radiometric calibration using features from Earth’s surface and atmosphere. This technique of applying calibration updates derived from in-flight, solar-illuminated Earth scenes will be used in NASA’s upcoming Surface Biology and Geology (SBG) mission. We discuss the calibration method and first results from the first year of flights from AVIRIS-3.

Imaging spectrometers like EMIT and AVIRIS-3 have similar instrument parameters and methane and CO2 mapping capability that enables direct attribution of observed plumes to the oil and gas, waste, and agriculture sectors. Onboard the International Space Station, EMIT can constrain methane and CO2 emissions over a significant portion of the Earth’s surface. With improved spatial resolution, the airborne AVIRIS-3 instrument enables quantification of smaller emissions sources that compliment EMIT observations from space. These instruments offer the potential to improve understanding of greenhouse gas budgets, inform mitigation strategies, and in some cases lead to voluntary mitigation.

Recent advances in remote imaging spectroscopy have increased its utility for detecting and quantifying greenhouse gas emissions. In fact, multiple airborne and space-based instruments are actively used to estimate methane emissions. Many of these measurements are made using matched-filter-based detection and estimation algorithms. In this work, we present new methods for quantifying and improving the accuracy and uncertainty of these algorithms. Two new metrics are proposed that capture the biases and uncertainties in gas quantity measurements stemming from local surface and atmospheric variation, observation and solar geometries, and sensor noise. We show that one of these, termed the “sensitivity,” can be used to correct the bias in the gas concentration length estimates due to variable atmospheres and backgrounds, reducing the estimator’s root mean squared (rms) error in spectra that deviate from the mean spectrum. The second, termed the “uncertainty,” represents the bias-removed statistical uncertainty in the corrected estimator. Expressions for the rms error both with and without the correction are provided along with interpretation to help quantify the various noise sources. The utility of the metrics is demonstrated using data from the Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer currently collecting Earth observations onboard the International Space Station (ISS). The EMIT data also demonstrates the potential accuracy increase afforded by the sensitivity correction over variable surface types. These metrics and their concomitant estimator accuracy increases could prove valuable for future work in quantifying gas source emission rates and their uncertainties, instrument design, and machine learning-based detection methods.

Radiative forcing by small dust particles deposited on snow plays a key role in climate change. Detection and quantification of these particles is essential for predicting melt rates, and assessing the associated impacts on Earth’s climate. NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) aims to improve our understanding of the Earth’s dust source and sink regions. The latter include snow surfaces in topographically challenging mountainous terrain. We present estimated snow reflectance, dust concentration, and radiative forcing from a new retrieval framework, and highlight their sensitivity to topographic characteristics. These findings will be essential for updating snow melt and climate models, but also for the conception of retrieval algorithms for upcoming global spaceborne imaging spectroscopy missions, including NASA’s Surface Biology and Geology (SBG).

We present the preliminary results of a study conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia’s national science agency) and NASA’s Jet Propulsion Laboratory (JPL) to demonstrate the utility of imaging spectroscopy from space to provide actionable information on water quality and aquatic ecosystems for Australia and Western USA. Mission requirements are derived from three key application objectives: potentially harmful algal blooms and nutrient pollution, invasive aquatic vegetation, and coral reef habitat benthic cover. The proposed AquaSat-1 instrument is a state-of-the-art visible to near-infrared (VNIR) Dyson imaging spectrometer, which builds on over 30 years of imaging spectroscopy development at JPL.

Pursuant to recommendations by the National Academies of Science, Engineering and Medicine's Earth Science Decadal Survey [1], the National Aeronautics and Space Administration (NASA) has announced the development of an Earth System Observatory (ESO), a series of missions designed to observe processes across the Earth's interior, surface and atmosphere. A key component of this system is the Surface Biology and Geology (SBG) investigation. SBG will measure the composition and properties of Earth's land, inland waters, and coastal oceans. The notional architecture consists of multiple spacecraft slated for launch in the 2027–2028 timeframe (Figure 1). Target science questions and geophysical variables span diverse disciplines including terrestrial and aquatic ecology, geology, vulcanology, hydrology and cryospheric sciences (Figure 2). Beyond simply measuring geophysical variables for each discipline, SBG will provide information about the links between the different domains, enabling a more comprehensive understanding of the Earth as a connected system. SBG measurements will also benefit a wide range of societal applications including agriculture, terrestrial and aquatic biodiversity, natural hazards, public health, and management of water and other natural resources [2]. SBG will also coordinate measurements, data products, and analyses with other ESO elements to deliver an integrated Earth System perspective of Earth and its changing climate.

The NASA Earth Venture Instrument mission, Earth Surface Mineral Dust Source Investigation (EMIT), is planned for launch to the International Space Station on a SpaceX rocket in May of 2022. EMIT's science objectives are to reduce uncertainty in the direct radiative forcing effect of mineral dust in the Earth system today and assess future changes in the effect under a range of climate scenarios. The development of the EMIT imaging spectrometer instrumentation and other systems has proceeded successfully despite the severe impacts of the COVID pandemic. The status and plans for the imaging spectrometer, calibration, ground system, in-orbit checkout, and prime science measurement observation phase are reported.

On Jan. 5, 2018, at the request of NASA, the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS), the Committee on the Decadal Survey for Earth Science and Applications from Space (ESAS) of the National Academies of Sciences, Engineering and Medicine (NASEM) Space Studies Board, Division on Engineering and Physical Sciences released the 2017 Decadal Survey, “Thriving on Our Changing Planet: A Decadal Strategy for Earth Observations from Space” [1]. The 700-page document is the second such Earth sciences survey produced by NASEM. The first, “Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond,” was released in 2007. The 2018 study designated a global “Surface Biology and Geology” (SBG) investigation that would include both imaging spectroscopy and thermal infrared observations [1]. This suite of measurements would address a wide range of global science questions. Its themes include: flows of energy, carbon, water, and nutrients sustaining terrestrial and marine ecosystems; the variability of the land surface and the fluxes of water and energy; inventory of the world's volcanoes, and the composition and temperature of volcanic products immediately following eruptions; other natural hazards including wildfires; snow accumulation and melt; water balance from the headwaters to the continent; land and water use effects on evapotranspiration; functional traits and diversity of terrestrial and aquatic ecosystems and vegetation; and more. Figure 1 shows example spectra from these surfaces, illustrating the enormous diversity of scene content that would be observed. Tables 1 and 2 show examples of the core and higher-level products that the SBG mission would produce.

The NASA Earth Surface Mineral Dust Source Investigation (EMIT) will use imaging spectroscopy to measure the mineral composition of the Earth's arid land regions from the International Space Station. The new directly observed surface composition products will be validated and used to initialize state-of-the-art Earth System Models to improve constraint of the sign and magnitude of dust-related radiative forcing at regional and global scales as well as predict the increase or decrease of available dust sources under future climate scenarios. EMIT is a NASA Earth Venture Instrument (EVI) Mission planned to launch in 2022. The instrument is a state-of-the-art optically fast Dyson imaging spectrometer that covers the spectral range from the visible to the short wavelength infrared (VSWIR). EMIT measurements and products will be openly available to the full science and applications communities for the range of additional investigations they enable.

Earth and planetary sciences often rely upon the detailed examination of spectroscopic data for rock and mineral identification. This typically requires the collection of high resolution spectroscopic measurements. However, they tend to be scarce, as compared to low resolution remote spectra. This work addresses the problem of inferring high-resolution mineral spectroscopic measurements from low resolution observations using probability models. We present the Deep Gaussian Conditional Model, a neural network that performs probabilistic super resolution via maximum likelihood estimation. It also provides insight into learned correlations between measurements and spectroscopic features, allowing for the tractability and interpretability that scientists often require for mineral identification. Experiments using remote spectroscopic data demonstrate that our method compares favorably to other analogous probabilistic methods. Finally, we show and discuss how our method provides human-interpretable results, making it a compelling analysis tool for scientists.

The Earth Surface Mineral Dust Source Investigation (EMIT) has been selected as an Earth Venture mission by NASA in 2018. EMIT will measure surface mineralogy in the arid land dust source regions of the Earth using visible to short wavelength infrared (VSWIR) imaging spectroscopy. These new measurements will be used to initialize advanced Earth System Models (ESM) to reduce uncertainty in the understanding of the impact of mineral dust aerosols on radiative forcing. The EMIT imaging spectrometer is planned to operate on the International Space Station (ISS) beginning in 2021.

The last five years have seen dramatic growth in the use of Visible Shortwave Infrared (VSWIR) and Thermal Infrared (TIR) imaging spectrometers to detect and characterize greenhouse methane sources. Targets include: dairy and animal husbandry emissions; landfills; fossil fuel extraction, storage, and transport infrastructure; geologic sources; natural emissions associated with sensitive arctic ecosystems; and more. These campaigns have resulted in significant new discoveries and advances in our understanding of the North American CH4 budget. Recent algorithm improvements have been critical for these campaigns, enabling robust statistical CH4measurement, fully-automated image-space source identification, and quantification of flux. Here we survey recent campaigns by NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) and NASA's Hyperspectral Thermal Emission Spectrometer (HyTES). We describe their algorithmic advances and major findings.

The National Academies Decadal Survey for Earth Science recommended that NASA pursue global imaging spectroscopy and thermal infrared measurements in the coming decade [1]. Both measurements would offer repeat coverage on approximately five-day to biweekly cadence, with comprehensive coverage of the globe's coastal and terrestrial area. This would be an unprecedented volume of data with the potential to transform remote sensing practice. To address this recommendation, NASA has sponsored a concept study by NASA research centers and associated university partners (https://sbg.jpl.nasa.gov). This study is determining a family of architecture options - including launch vehicle, spacecraft, instrument, and suborbital components - that could address the Decadal Survey objectives. The architecture study is driven by science needs and builds on input of the research community. As of this writing, the study is entering a phase in which a large field of system possibilities is pared down to a representative handful for an ultimate decision by NASA.

On the 5thof January 2018, an unedited prepublication version of the 2017 Decadal Survey was released titled, “Thriving on Our Changing Planet A Decadal Strategy for Earth Observation from Space.” This 700 page document that was developed over more than two years with broad input from the Earth science and applications communities. Five targeted observables are indicated as observing system priorities in this early release version. One of these is surface biology and geology. In this category, a set of science and applications targets are identified including: ground/water temperature, snow reflectivity, active geologic processes, vegetation traits and algal biomass. The candidate measurement approach is described as, “Hyperspectral imagery in the visible and shortwave infrared, multi- or hyperspectral imagery in the thermal IR.” This paper reviews some of the key new science and applications objectives, and outlines some measurement approaches that could be used to address this guidance in the area of global visible to short wavelength infrared imaging spectroscopy.

The Earth is arguably the most complex object in the solar system, both in terms of the diversity of materials and compounds as well as the diversity of processes occurring in the Earth system. Comprehensive global measurements are required to investigate, understand and model this complex system that includes processes within and between the biosphere, lithosphere, hydrosphere, cryosphere, and atmosphere. Key constituents and processes of the Earth system can be measured and monitored with imaging spectroscopy in the Visible to Shortwave Infrared (VSWIR) region of the electromagnetic spectrum from 380 to 2510 nm with ≤10 nm and spatial sampling of 30 m. This paper presents key science contributions and an approach for a near term global Earth imaging spectroscopy measurement.

In the Northern winter of 2015/16 the Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) acquired measurements in India. The measurements were collected as part of a joint campaign between National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) to foster research with imaging spectroscopy in a set of new diverse and unique environments. A total of 317 flight lines covering 57 sites were acquired. All measurements have been calibrated to radiance and a baseline atmospheric correction has also been applied to generate a reflectance product. Both the radiance and atmospherically corrected reflectance measurements are now available to support the science and applications research phase of the campaign.

The global retreat of Earth's cryosphere is the iconic symbol of climate change. Decades of satellite, airborne, and ground observations show clear evidence of increased melting of glaciers and ice sheets, declines in sea ice, and decreasing spring snow cover. This increased melting of cryosphere cover makes Earth more absorptive of sunlight and moves enormous volumes of stored water from frozen state to liquid, raising sea level and changing water availability to large populations. However, the distribution of forcings controlling this accelerated melting is poorly known. Atmospheric warming from greenhouse gases is contributing to this acceleration but its magnitude is uncertain due to our uncertainties in the controls on the dominant contributor to annual melt, absorbed sunlight, itself controlled by albedo. Despite this crucial role of albedo and solar radiation in snow and ice melt, sparse measurements have kept us from understanding the global distribution of controls on albedo, grain size and impurities, and from accurately modeling melt processes worldwide. Such an understanding is crucial to determining cryosphere melt and projecting its future behavior. Global spectroscopic measurements are required to improve our understanding of controls on cryosphere melt rates, enabling better predictions of future changes that will affect humankind.

Conventional VSWIR imaging spectrometer atmospheric correction evolved from multi-band approaches and generally does not exploit the full spectral measurement. We hypothesize that a pure spectroscopic approach can improve atmospheric inversion accuracy to minimize regional biases in global-scale investigations. Such techniques are pervasive in atmospheric remote sounding disciplines, where Optimal Estimation (OE) retrieval theory (Rodgers et al., 2000) inverts a radiance spectrum to recover a consistent physical model incorporating, for example, aerosol and H2O, the interactions between scattering, absorption, and the coupling between surface reflectance and atmosphere. This enables a statistically rigorous treatment of uncertainty with the potential to recover information on spectrally-broad signals. Here we demonstrate a proof of concept that overcomes the primary computational roadblock of these methods: fast execution of line-by-line Radiative Transfer (RT) models. Neural Network (NN) emulation can replicate the results of the MODTRAN 6.0 line-by-line A-band model to high accuracy and a five order of magnitude improvement in execution time. This demonstrates potential for OE to provide significant advances in the accuracy and modeling power of VSWIR atmospheric correction.

There is a pressing need to globally inventory and assess coastal and inland aquatic habitats; extremely valuable and productive regions that are vulnerable to global anthropogenic pressures and climatic change. Basic information about sessile communities (wetlands, coral reefs, and sea grasses) includes mapping their extent and distribution, which can be gleaned from spectral surface reflectance imagery at high spatial resolution, but moderate temporal resolution. Moderate to high temporal resolution is also required for detailed observations of sessile community change (e.g., phenology, disturbance) and high temporal resolution is required for environmental changes in the surrounding water, phytoplankton concentration and composition, and concentrations of sediment or chromophoric dissolved organic matter (CDOM). Current and upcoming satellite missions and technology could meet spatial and spectral challenges. Multiple orbiting and airborne platforms, along with a network of in situ measurements, could provide a more complete picture of how these vital resources are changing. This paper provides an overview of these resources.

We demonstrate real-time model-based atmospheric correction onboard the Next Generation Airborne Visible/Infrared Imaging Spectrometer. We achieve a reduction in processing time from hours or days to seconds by modifying a standard physics-based atmospheric correction algorithm to support real-time execution. We achieved this reduction by modifying the physics-based ATmospheric REMoval algorithm to leverage a large lookup table of precomputed scattering and transmission coefficients, indexed by parameters specifying the aircraft operating conditions at capture time. Interpolation among the precomputed coefficients allows surface reflectance retrieval at the sensor acquisition rate of 500 Mb/s. Our system produced science-quality reflectance products during over 30 test flights and, to our knowledge, is the first reported demonstration of real-time model-driven visible shortwave infrared atmospheric correction onboard an aircraft.

Next-generation orbital imaging spectrometers will generate unprecedented data volumes, demanding new methods to optimize storage and communication resources. Here, we demonstrate that onboard analysis can excise cloud-contaminated scenes, reducing data volumes while preserving science return. We calculate optimal cloud-screening parameters in advance, exploiting stable radiometric calibration and foreknowledge of illumination and viewing geometry. Channel thresholds expressed in raw instrument values can be then uploaded to the sensor where they execute in real time at gigabit-per-second (Gb/s) data rates. We present a decision theoretic method for setting these instrument parameters and characterize performance using a continuous three-year image archive from the “classic” Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-C). We then simulate the system onboard the International Space Station, where it provides factor-of-two improvements in data volume with negligible false positives. Finally, we describe a real-time demonstration onboard the AVIRIS Next Generation (AVIRIS-NG) flight platform during a recent science campaign. In this blind test, cloud screening is performed without error while keeping pace with instrument data rates.

Mapping localized spectral features in complex scenes demands sensitive and robust detection algorithms. This letter investigates two aspects of large images that can harm matched filter (MF) detection performance. First, multimodal backgrounds may violate normality assumptions. Second, outlier features can trigger false detections due to large projections onto the target vector. We review two state-of-the-art methods designed to resolve these issues. The background clustering of Funkmodels multimodal backgrounds, and the mixture-tuned (MT) MF of Boardman and Kruse addresses outliers. We demonstrate that combining the two methods has additional performance benefits. An MT cluster MF shows effective performance on simulated and airborne data sets. We demonstrate target detection scenarios that evidence multimodality, outliers, and their combination. These experiments explore the performance of the component algorithms and the practical circumstances that can favor a combined approach.

We discuss the DISORT-based radiative transfer pipeline (“CRISM_LambertAlb”) for atmospheric and thermal correction of MRO/CRISM data acquired in multispectral mapping mode ($ \sim$200 m/pixel, 72 spectral channels). Currently, in this phase-one version of the system, we use aerosol optical depths, surface temperatures, and lower atmospheric temperatures, all from climatology derived from Mars Global Surveyor Thermal Emission Spectrometer (MGS-TES) data and from surface altimetry derived from MGS Mars Orbiter Laser Altimeter (MOLA). The DISORT-based model takes the dust and ice aerosol optical depths (scaled to the CRISM wavelength range), the surface pressures (computed from MOLA altimetry, MGS-TES lower atmospheric thermometry, and Viking-based pressure climatology), the surface temperatures, the reconstructed instrumental photometric angles, and the measured$I/F$spectrum as inputs, and then a Lambertian albedo spectrum is computed as the output. The Lambertian albedo spectrum is valuable geologically because it allows the mineralogical composition to be estimated. Here,$I/F$is defined as the ratio of the radiance measured by CRISM to the solar irradiance at Mars divided by$\pi$; if there was no martian atmosphere,$I/F$divided by the cosine of the incidence angle would be equal to the Lambert albedo for a Lambertian surface. After discussing the capabilities and limitations of the pipeline software system, we demonstrate its application on several multispectral data cubes—particularly, the outer reaches of the northern ice cap of Mars, the Tyrrhena Terra area that is northeast of the Hellas basin, and an area near the landing site for the Phoenix mission in the northern plains. For the icy spectra near the northern polar cap, aerosols need to be included in order to properly correct for the$ \hbox{CO}_{2}$absorption in the$\hbox{H}_{2}\hbox{O}$ice bands at wavelengths near 2.0$\mu\hbox{m}$. In future phases of software development, we intend to use CRISM data directly in order to retrieve the spatiotemporal maps of aerosol optical depths, surface pressure, and surface temperature. This will allow a second level of refinement in the atmospheric and thermal correction of CRISM multispectral data.

Spectroscopy has existed for more than three centuries now. Nonetheless, significant scientific advances have been achieved. We discuss the history of spectroscopy in relation to emerging technologies and applications. Advanced focal plane arrays, optical design, and intelligent on-board logic are prime prospective technologies. Scalable approaches in pre-processing of imaging spectrometer data will receive additional focus. Finally, we focus on new applications monitoring transitional ecological zones, where human impact and disturbance have highest impact as well as in monitoring changes in our natural resources and environment. We conclude that imaging spectroscopy enables mapping of biophysical and biochemical variables of the Earth's surface and atmospheric composition with unprecedented accuracy.

A calibration experiment was orchestrated on February 7, 2001 at the Salar de Arizaro, Argentina to assess the on-orbit radiometric and spectral calibration of Hyperion. At this high-altitude homogeneous dry salt lakebed, Hyperion, Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) and in situ measurements were acquired. At a designated calibration target on Salar de Arizaro, the radiance spectra measured by Hyperion and AVIRIS were compared. In the spectral range from 430-900 nm [visible near-infrared (VNIR)], the ratio of Hyperion over AVIRIS was 0.89, and in the 900-2390-nm [shortwave infrared (SWIR)] spectral range the ratio was 0.79. A comparison of the Hyperion radiance spectrum with a radiative-transfer-code-predicted spectrum for the calibration target showed similar results. These results in conjunction with prelaunch laboratory measurements, on-orbit lunar measurements, other on-orbit calibration experiment results, as well as comparison with Landsat-7, lead to an update of Hyperion radiometric calibration in December 2001. The compromise update was to increase the Hyperion radiometric calibration coefficients by 8% in the VNIR and 18% in the SWIR spectrometers. In addition to radiometric accuracy, the on-orbit radiometric precision of Hyperion was assessed at Salar de Arizaro. Noise-equivalent delta radiance was calculated from Hyperion dark signal data and found to be five to ten times higher in comparison to AVIRIS. Also, from a homogeneous portion of Salar de Arizaro the Hyperion SNR was estimated at 140 in the VNIR and 60 in the 2200-nm region of the SWIR spectral range. Cross-track radiometric response was assessed with the AVIRIS dataset that spanned the full Hyperion swath. Within the accuracy of the registration of the datasets, the Hyperion cross-track response was shown to be uniform. Hyperion spectral calibration was assessed with a spectral fitting algorithm using the high spectral resolution radiative transfer modeled spectra for Salar de Arizaro.

Calibration results of the advanced visible and near infrared radiometer (AVNIR) on board ADEOS are presented. First, the AVNIR responses to the internal and external calibration sources are evaluated, and their short- and long-term stabilities are summarized. Second, absolute radiometric calibration of AVNIR is conducted by using the NASA JPL's calibrated airborne optical sensor, and the results from several different calibration methods (preflight calibration, and in-flight calibrations using the on-board data, the AVIRIS, and the vicarious method) are compared. Third, the geometric accuracy and the image quality are summarized.