Fuel moisture content is a key indicator of fire ignition risk and required to drive fire behavior models, however it often relies on in situ measurements which are sparse in time and space. Regional-scale and temporally dynamic measurements are required, and remote sensing provides an opportunity for more comprehensive mapping. In this paper, we demonstrate the use of fuel moisture metrics derived from imaging spectroscopy to inform pre- and post-fire conditions. Imaging spectroscopy can quantify canopy water content (CWC) using a physics-based retrieval, and distinguish landscape characteristics such as photosynthetic and non‐photosynthetic vegetation, and soils. Here, we demonstrate a use case of imaging spectroscopy-derived CWC and fractional cover for a field site on Monroe Mountain in the Fishlake National Forest, Utah. This location was heavily mapped by airborne AVIRIS-3 during a 2023 NASA/FASMEE field campaign. This region also underwent a prescribed burn in October 2023, permitting us to examine how we can use imaging spectroscopy to image fuel conditions pre- and post-fire. The relationship of CWC with in situ dead fuel moisture measurements is stronger for the DFMC surface fuels when filtering for points that contain 50% or more fraction of non-photosynthetic vegetation (R² = 0.50 to 0.54), except for 10-hour fuels (small branches and larger twigs) which had a decrease in agreement. CWC was also found to both decline following the prescribed burn, consistent with the spatial patterns of burn severity, indicating the use of these variables for assessing pre- and post-fire vegetation conditions.

The EnMAP (Environmental Mapping and Analysis Program) satellite, launched on 1 April 2022, is designed to provide high-resolution imaging spectroscopy data for environmental monitoring, resource management and land use mapping. The integrity and quality of the official data products are assessed by the mission's external product validation activities, which are independent of the calibration/validation activities from the ground segment.The external validation is intended to assess, monitor, and report the quality of the L1B (top-of-atmosphere radiance in sensor coordinates), L1C (top-of-atmosphere radiance in map coordinates), and L2A (surface reflectance in map coordinates) user products. This work presents an overview of the EnMAP product validation activities during the commissioning phase and the first two years of the operational phase. Radiometric, spectral, geometric, and general uniformity aspects are evaluated with scene-based analysis methods and through comparison with ground-based reference methods. We find that EnMAP's radiometric calibration is <5%, both smile and keystone are <20% of a pixel, and the Bottom-Of-Atmosphere (BOA) reflectance and Normalized Water-Leaving reflectance are well inside the mission requirements. These results confirm that the EnMAP products fulfill the mission requirements and pave the way for the scientific exploitation and use of the EnMAP products for land, water, and atmospheric applications.

The subject of cryosphere remote sensing has been largely investigated in the past. Many authors have developed a series of different reliable methodologies to remotely study a variety of properties of cryospheric surfaces. These methodologies are commonly based on reflectance band ratios and band areas, and can often be applied to a specific type of ice or snow surface only. In this work, we propose a flexible approach that utilizes the DIScrete Ordinate Radiative Transfer (DISORT) multiscattering code to inversely retrieve the properties of ice and snow surfaces. This code has been widely used in the past for the retrieval of snow surface properties on Earth. The main strength of this approach lies in the fact that every kind of cryospheric surface can be studied using the same methodology. Although we focus primarily on snow and ice on Earth, we aim to develop a tool to study planetary ice; an application of this method to the icy surfaces of Mars is therefore presented and discussed within the paper. Our results show good agreement with the expected distributions of surface properties within the studied ice caps and with the in situ measurements collected during previous field campaigns in the area. Also, our application to Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) data has shown promising results with DISORT being able to model planetary ice cap reflectance with very low estimated errors.

Atmospheric correction for global-scale imaging spectroscopy must accurately characterize environmental interfaces such as coastlines, snow lines, and wildfire fronts. However, atmosphere and surface retrievals are challenging across environmental boundaries when modeling assumptions change across surface types. This poses a challenge for the next generation of wide-swath imaging spectrometers, which can limit retrieval constraints where the assumptions for the retrieval method must be generic enough for all imaged land covers. We demonstrate multi-state atmospheric correction that allows multiple surface models within the same scene, enabling optimal estimation of surface reflectance and atmospheric conditions. We leverage recent advances in forward modeling for surface reflectance retrieval and a classification from radiance to provide consistent retrievals across environmental boundaries. We focus specifically on the land-water interface by incorporating a glint model in the atmospheric correction of water pixels and a topographic model in the correction over land. We show that our multi-state atmospheric retrieval, in conjunction with these nuanced surface modeling approaches for land and water, improves consistency in surface reflectance estimates across different observation geometries while ensuring a consistent atmospheric retrieval across the land-water transition. Critically, multi-state atmospheric retrieval can be extended to include any surface-specific forward model construction, for example by including black-body temperature over fires, or using custom surface property models over snow and ice. This work will help improve the accuracy of surface reflectance retrievals and continuity of atmospheric state retrievals for high-spatial resolution airborne and space-borne imaging spectrometers operating across diverse and mixed environments.

Abstract. The NASA airborne Arctic Radiation-Cloud-aerosol-Surface-Interaction Experiment (ARCSIX) collected a unique data set providing a near-simultaneous characterization of radiative fluxes, surface, cloud, and aerosol particle properties to address science questions on the surface radiation budget, the processes governing the cloud lifecycle, atmospheric composition, and the interactions between the surface and atmosphere. The overarching goal of ARCSIX was to quantify the contributions of surface, clouds, aerosol particles, and precipitation to summer sea ice melt. ARCSIX consisted of two deployments in 2024 (Spring: 2024-05-28 through 2024-06-13 and Summer: 2024-07-25 through 2024-08-15) to capture pre- and post-melt conditions. ARCSIX provided coordinated remote sensing and in situ sampling using three aircraft in a high-flyer/low-flyer configuration. The NASA G-III served as the high-flying remote sensing platform with two lower flying in situ and near-target remote sensor observing platforms, NASA P-3B and SPEC Inc. Learjet. ARCSIX data are well-suited to improve satellite remote sensing capabilities in the Arctic. ARCSIX included an array of sea ice mass balance buoys deployed in the Lincoln Sea that were regularly overflown during the campaign. ARCSIX research flights spanned the Baffin Bay, Lincoln Sea, west and north of the Canadian Archipelago, and the Greenland north and northeast coasts. During the spring deployment, 19 research flights took place covering 114 flight hours: 10 flights and 68 hours by the P-3B and nine flights and 46 hours by the G-III. During summer, 24 research flights covered 136 flight hours: nine flights and 75 hours by the P-3B, five flights and 26 hours by the G-III, and 10 flights and 35 hours by the Learjet. A total of 13 coordinated flights with 2+ aircraft were carried out. This paper describes the ARCSIX flight strategy, instrumentation, and data set access, and usage details. ARCSIX data are publicly available at https://doi.org/10.5067/SUBORBITAL/ARCSIX/DATA001.

Forecasting wildland fire location and behavior increases in importance as fires grow in frequency and severity worldwide. Observing and understanding pre-fire fuel dynamics through airborne and satellite imagery can support planning and management. The diversity of tools available for management include in-situ, airborne, and satellite remote sensing products, but they are better when used together. Here, we identify Imaging Spectroscopy and Synthetic Aperture Radar products including fuel moisture and load, canopy cover and water content, and photosynthetic or non-photosynthetic vegetation that can be utilized en tandem to understand pre-fire fuels, if they are pre-processed and validated with the combined use in mind. Making these data available to operational users can be leveraged through implementation in novel modeling frameworks, and ongoing assessment and validation.

Atmospheric interference has been a major challenge in the remote sensing of sun-induced chlorophyll fluorescence (SIF) from airborne platforms. While existing algorithms use ground-reference targets to tune the O2-A band transmittance, they often rely on static atmospheric assumptions that ignore the influence of subtle topographical changes on the O2-A transmittance. Such simplifications can introduce systematic biases into the resultant SIF estimates, and the impact on the spatial pattern of SIF remains poorly understood. To address this spatial SIF artifact, we present a joint retrieval algorithm with full atmospheric model. This algorithm has been optimized for accurate SIF retrieval in simulation and in-situ data. Within this joint retrieval framework, we constrained the key atmospheric states (i.e., aerosol optical thickness and pressure elevation) instead of treating them as constants, and retrieved SIF from a benchmark airborne sub-nanometer hyperspectral image obtained in a maize field in 2021. We retrieved the spatial pattern of airborne SIF which best matched the SIF field observations at a stringent constraint on the distribution of aerosol optical thickness (with a standard deviation of 10-6) and a loose constraint on the distribution of pressure elevation (with a standard deviation of 10-1). The strong constraint on the aerosol optical thickness restricted its spatial variability, maintaining a physically plausible, smooth pattern. The weak constraint on the pressure elevation allows for tuning the O2-A band transmittance, enabling the spatial pattern to better adapt to local spatial variability in topography and temperature. This combination of prior constraints resulted in a balanced SIF retrieval for the airborne imagery, avoiding over-fitting to microtopography without sacrificing spatial integrity. Our findings demonstrated that carefully constraining these atmospheric states in the optimal estimation inversion considerably alleviate the spatial artifact stemming from the static atmospheric assumption and improve the SIF quantification. The optimal constraints identified in this study align with the physical variability of AOT and effective pressure elevation at the airborne scale, suggesting that this configuration is transferable to other airborne missions. This underscores that for airborne SIF missions, atmospheric parameters should not be treated as fixed constants but rather as variables within a regularized joint inversion.

Space-based imaging spectrometers that monitor the Earth’s surface generate vast amounts of data, the processing of which requires fast and accurate retrieval algorithms. Estimating scientifically relevant surface properties from remotely measured radiance data typically involves first inferring spectral surface reflectance from the observed radiance, followed by discipline-specific algorithms to derive scientifically relevant properties. Probabilistic reflectance retrieval algorithms, such as the commonly used optimal estimation (OE), are computationally expensive. Furthermore, the Gaussian assumptions associated with OE have not been fully validated in the context of hyperspectral retrievals. To address these challenges, we introduce accelerated optimal estimation (AOE), a Bayesian algorithm that speeds up the OE reflectance inversion process by up to two orders of magnitude compared to a reference OE implementation (ROE), while also providing improved convergence over a number of selected test targets. We also demonstrate that, under given atmospheric conditions, Gaussian uncertainty estimates from OE-type algorithms are accurate. This is achieved by comparing the OE-type posterior distributions to non-Gaussian ones obtained with Markov chain Monte Carlo (MCMC). Finally, we demonstrate how AOE scales to a larger AVIRIS-NG scene, showcasing its ability to handle complex, large-scale data.

We stand at the threshold of a transformative era in Earth observation, marked by space‐borne visible‐to‐shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter‐ and intra‐annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub‐seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High‐Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer‐Next Generation (AVIRIS‐NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub‐seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open‐source processing methods and cloud‐based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high‐frequency time series data from space‐borne VSWIR imaging spectrometers.

Key to the success of spaceborne missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that surface reflectance products over snow show an erroneous hook with decreases in the visible wavelengths, causing per-band and broadband reflectance errors of up to 33 % and 11 %, respectively. This hook is sometimes mistaken for soot or dust but can result from three artifacts: (1) background reflectance that is too dark, (2) an assumption of level terrain, or (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are being implemented.

Remote imaging spectroscopy, with its ability to quantify phytoplankton pigments, is a powerful tool for monitoring aquatic ecosystems, giving valuable insights into environmental processes. However, the accuracy of these retrievals is impacted by specular glint at the air-water interface. Current imaging spectrometer missions either ignore glint-contaminated pixels, or apply post-hoc corrections, which can introduce substantial uncertainties in reflectance estimates. To address this challenge, we present a new approach that simultaneously models water-leaving reflectance and sun and sky glint contributions from imaging spectroscopy measurements. We reveal accurate performance of our model by utilizing measurements and derived chlorophyll-a products from the PRISM airborne imaging spectrometer during the SubMesoscale Ocean Dynamics Experiment (S-MODE) campaign.

Global patterns of snow darkening and melting, induced by grain metamorphism and the accumulation of small light-absorbing particles (LAPs), such as mineral dust, black carbon, volcanic ash, or algae cells, lead to an intensified radiative forcing and retreat of Earth's snow cover. Mapping and quantifying snow grain size and LAPs on both temporal and spatial scales are needed to improve the prediction of melt rates and their impacts on climate change. High-resolution visible-to-shortwave-infrared (VSWIR) imaging spectrometers herald a new era of passive spaceborne remote sensing, which will help to fulfill this objective. This technology provides measurements of reflected solar radiation in continuous spectral channels throughout the solar spectrum, allowing for the detection of narrow ice and LAP absorption bands. One of these instruments is NASA's Earth Surface Mineral Dust Source Investigation (EMIT) that was launched to the International Space Station (ISS) in July 2022. EMIT observations include snow cover in low- to mid-latitude mountainous regions, such as the western US, the Andes in South America, and high-mountain Asia. Accurate retrievals of snow surface properties, including grain size, liquid water content, and concentrations of mineral dust and algae, require precise, ideally joint accounting for atmospheric, topographic, and anisotropic effects in the reflected radiance. However, some methods still either neglect physical effects of the surface or utilize the surface reflectance as an intermediate non-physical quantity, in part without proper error propagation from atmospheric modeling and obtained from statistical modeling. Moreover, the term "surface reflectance" is often used with ambiguity in the literature, which instantly raises the question of whether we still need this quantity as a retrieval product. In this contribution, we present a novel forward model that couples the MODTRAN atmosphere radiative transfer code with a physics-based snow reflectance model that utilizes the multistream DISORT program. Our model allows us to estimate snow surface and atmosphere properties directly from measured radiance. We apply the approach to EMIT images from Patagonia, South America, and compare our results to the EMIT L2A products that retrieve surface reflectance as a free parameter. We find discrepancies in snow grain size of up to 200 & micro;m and in dust mass mixing ratio of up to 75 & micro;gg-1. Furthermore, we demonstrate differences in instantaneous LAP radiative forcing of up to 400Wm-2. We conclude that we still need reflectance but only if it is clearly defined and preferably modeled as a quantity within the forward model. These findings will be essential for the conception of retrieval algorithms for future orbital imaging spectroscopy missions, such as NASA's Surface Biology and Geology (SBG).

Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology, were sparse, starting with the CHRIS/PROBA and EO1/Hyperion missions in the early 2000s, and providing spectroscopy data with limited spectral coverage and/or low data quality in the SWIR. Since 2019, several countries and agencies have successfully launched a number of spaceborne imaging spectroscopy systems into orbit or deployed them on the International Space Station (ISS) such as DESIS, PRISMA, HISUI, GF-5, EnMAP and EMIT. Among these recent missions, the German Environmental Mapping and Analysis Program (EnMAP) stands for its long-term development, sophisticated design with on-board calibration, high data quality requirements, and extensive accompanying science program. EnMAP was launched in April 2022 and, following a successful commissioning phase, started its operational activities in November 2022. The EnMAP mission encompasses global coverage from 80 degrees N to 80 degrees S through on-demand data acquisitions. Data are free and open access with 30 m spatial resolution, a high spectral resolution with a spectral sampling distance of 6.5 nm and 10 nm in the VNIR and SWIR regions respectively, and a high signal-to-noise ratio. In this paper, we aim to present the mission's current status, coverage, science capabilities and performance two years after launch. We show the potential of EnMAP for space-based imaging spectroscopy to operate in various environments, including high and low light levels, dense forests, Antarctic glaciers, and arid agricultural areas. EnMAP enables various applications in fields such as agriculture and forestry, soil compositional, raw materials, and methane mapping, as well as water quality assessment, and snow and ice properties. The results show that EnMAP's performance exceeds the mission requirements, and highlights the significant potential for contribution to scientific exploitation in various geo- and biochemical sciences. EnMAP is also expected to serve as a key tool for the development and testing of data processing algorithms for upcoming global operational missions.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

PRISMA is a hyperspectral satellite mission launched by the Italian Space Agency (ASI) in April 2019. The mission is designed to collect data at global scale for a variety of applications, including those related to the cryosphere. This study presents an evaluation of PRISMA Level 1 (L1) and Level 2 (L2D) products for different snow conditions. To the aim, PRISMA data were collected at three sites: two in the Western European Alps (Torgnon and Plateau Rosa) and one in East Antarctica (Nansen Ice Shelf). PRISMA data were acquired contemporary to both field measurements and Sentinel‐2 data. Simulated Top of the Atmosphere (TOA) radiance data were then compared to L1 PRISMA and Sentinel‐2 TOA radiance. Bottom Of Atmosphere (BOA) reflectance from PRISMA L2D and Sentinel‐2 L2A data were then evaluated by direct comparison with field data. Both TOA radiance and BOA reflectance PRISMA products were generally in good agreement with field data, showing a Mean Absolute Difference (MAD) lower than 5%. L1 PRISMA TOA radiance products resulted in higher MAD for the site of Torgnon, which features the highest topographic complexity within the investigated areas. In Plateau Rosa we obtained the best comparison between PRISMA L2D reflectance data and in situ measurements, with MAD values lower than 5% for the 400–900 nm range. The Nansen Ice Shelf instead resulted in MAD values <10% between PRISMA L2D and field data, while Sentinel‐2 BOA reflectance showed higher values than other data sources.

EnMAP (Environmental Mapping and Analysis Program) is a high-resolution imaging spectroscopy remote sensing mission that was successfully launched on April 1st, 2022. Equipped with a prism-based dual-spectrometer, EnMAP performs observations in the spectral range between 418.2nm and 2445.5nm with 224 bands and a high radiometric and spectral accuracy and stability. EnMAP products, with a ground instantaneous field-of-view of 30m×30m at a swath width of 30km, allow for the qualitative and quantitative analysis of surface variables from frequently and consistently acquired observations on a global scale. This article presents the EnMAP mission and details the activities and results of the Launch and Early Orbit and Commissioning Phases until November 1st, 2022. The mission capabilities and expected performances for the operational Routine Phase are provided for existing and future EnMAP users.

The hyperspectral EnMAP (Environmental Mapping and Analysis Program) satellite was successfully launched in April 2022, passed its commissioning phase, and entered the nominal phase of operational data acquisition in November 2022. Since then, users may submit data acquisition proposals and download the data in three processing levels: Level-1B (radiometrically-corrected and spectrally-characterized top-of-atmosphere (TOA) radiance), Level-1C (geometrically-corrected L1B data), and Level-2A (atmospherically-corrected Level-1C data, i.e., bottom-of-atmosphere (BOA) reflectance). The official product generation is usually done by the ground segment processing chain. Alternatively, the EnMAP processing tool (EnPT) provides a highly customizable free and open-source pre-processing chain enabling additional functionalities and options to fulfill individual user requirements and quality expectations. Here, we provide an overview of the implemented pre-processing chain and its modular design with a specific focus on the additional functionalities of EnPT to obtain highly accurate and customizable hyperspectral EnMAP Level-2A data.

We introduce a new unified atmospheric-topographic correction approach that estimates surface geometry directly from the radiance measurement. Surface topography influences the at-sensor radiance measurement, making precise topography modeling critical in applications like vegetation or snow studies in mountainous terrain. Currently, elevation maps are used to derive topographic variables such as the slope and sky-view factor. This process is error-prone since static global digital elevation models do not generally achieve the accuracy required, and even minor mismatches in spatial resolution can introduce significant artifacts in downstream processing. Here we demonstrate that it is possible to estimate topographic parameters directly from spectral data, ensuring perfect physical consistency, temporal coincidence, and spatial alignment. We present experiments estimating topographic slope in two scenes in Southern California, with data from NASA's Next Generation Airborne Visible/Near Infrared Imaging Spectrometer (AVIRIS-NG). We compared our radiance-based estimates against high-resolution lidar datasets. Our initial validation result showed a correlation of R2 = 0.864 (n = 160) over the homogeneous surface of Beckman Auditorium's cone-shaped roof on the Caltech campus in Pasadena, California. We then validate the model over a larger study site near Santa Clarita, California, finding R2 = 0.923 (n = 40, 000) in a 350 x 350 m area. The accuracy of our model estimates, combined with its systematic advantages over the alternative, show the potential of the approach for use in both airborne campaigns and orbital missions.