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The large uncertainty in the mineral dust direct radiative effect (DRE) hinders projections of future climate change due to anthropogenic activity. Resolving modeled dust mineral speciation allows for spatially and temporally varying refractive indices consistent with dust aerosol composition. Here, for the first time, we quantify the range in dust DRE at the top of the atmosphere (TOA) due to current uncertainties in the surface soil mineralogical content using a dust mineral-resolving climate model. We propagate observed uncertainties in soil mineral abundances from two soil mineralogy atlases along with the optical properties of each mineral into the DRE and compare the resultant range with other sources of uncertainty across six climate models. The shortwave DRE responds region-specifically to the dust burden depending on the mineral speciation and underlying shortwave surface albedo: positively when the regionally averaged annual surface albedo is larger than 0.28 and negatively otherwise. Among all minerals examined, the shortwave TOA DRE and single scattering albedo at the 0.44–0.63 µm band are most sensitive to the fractional contribution of iron oxides to the total dust composition. The global net (shortwave plus longwave) TOA DRE is estimated to be within −0.23 to +0.35 W/sq. m. Approximately 97 % of this range relates to uncertainty in the soil abundance of iron oxides. Representing iron oxide with solely hematite optical properties leads to an overestimation of shortwave DRE by +0.10 W/sq. m at the TOA, as goethite is not as absorbing as hematite in the shortwave spectrum range. Our study highlights the importance of iron oxides to the shortwave DRE: they have a disproportionally large impact on climate considering their small atmospheric mineral mass fractional burden (∼2 %). An improved description of iron oxides, such as those planned in the Earth Surface Mineral Dust Source Investigation (EMIT), is thus essential for more accurate estimates of the dust DRE.

We describe advanced spectral and radiometric calibration techniques developed for NASA’s Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). By employing both statistically rigorous analysis and utilizing in situ data to inform calibration procedures and parameter estimation, we can dramatically reduce undesirable artifacts and minimize uncertainties of calibration parameters notoriously difficult to characterize in the laboratory. We describe a novel approach for destriping imaging spectrometer data through minimizing a Markov Random Field model. We then detail statistical methodology for bad pixel correction of the instrument, followed by the laboratory and field protocols involved in the corrections and evaluate their effectiveness on historical data. Finally, we review the geometric processing procedure used in production of the radiometrically calibrated image data.

In the near‐infrared from about 2 μm to beyond 3 μm, the light from the Moon is a combination of reflected sunlight and emitted thermal emission. There are multiple complexities in separating the two signals, including knowledge of the local solar incidence angle due to topography, phase angle dependencies, emissivity, and instrument calibration. Thermal emission adds to apparent reflectance, and because the emission's contribution increases over the reflected sunlight with increasing wavelength, absorption bands in the lunar reflectance spectra can be modified. In particular, the shape of the 2 μm pyroxene band can be distorted by thermal emission, changing spectrally determined pyroxene composition and abundance. Because of the thermal emission contribution, water and hydroxyl absorptions are reduced in strength, lowering apparent abundances. It is important to quantify and remove the thermal emission for these reasons. We developed a method for deriving the temperature and emissivity from spectra of the lunar surface and removing the thermal emission in the near infrared. The method is fast enough that it can be applied to imaging spectroscopy data on the Moon. Key Points Lunar spectra contain enough information to remove the thermal component Spectra of some locations on the Moon contain water absorptions Hottest areas are in craters where multiply scattered sunlight adds heat

Accurate and timely detection, quantification, and attribution of methane emissions from Underground Gas Storage (UGS) facilities is essential for improving confidence in greenhouse gas inventories, enabling emission mitigation by facility operators, and supporting efforts to assess facility integrity and safety. We conducted multiple airborne surveys of the 12 active UGS facilities in California between January 2016 and November 2017 using advanced remote sensing and in situ observations of near-surface atmospheric methane (CH4). These measurements where combined with wind data to derive spatially and temporally resolved methane emission estimates for California UGS facilities and key components with spatial resolutions as small as 1-3 m and revisit intervals ranging from minutes to months. The study spanned normal operations, malfunctions, and maintenance activity from multiple facilities including the active phase of the Aliso Canyon blowout incident in 2016 and subsequent return to injection operations in summer 2017. We estimate that the net annual methane emissions from the UGS sector in California averaged between 11.0 3.8 GgCH4 yr−1 (remote sensing) and 12.3 3.8 GgCH4 yr−1 (in situ). Net annual methane emissions for the 7 facilities that reported emissions in 2016 were estimated between 9.0 3.2 GgCH4 yr−1 (remote sensing) and 9.5 3.2 GgCH4 yr−1 (in situ), in both cases around 5 times higher than reported. The majority of methane emissions from UGS facilities in this study are likely dominated by anomalous activity: higher than expected compressor loss and leaking bypass isolation valves. Significant variability was observed at different time-scales: daily compressor duty-cycles and infrequent but large emissions from compressor station blow-downs. This observed variability made comparison of remote sensing and in situ observations challenging given measurements were derived largely at different times, however, improved agreement occurred when comparing simultaneous measurements. Temporal variability in emissions remains one of the most challenging aspects of UGS emissions quantification, underscoring the need for more systematic and persistent methane monitoring.

Biological invasions contribute to global environmental change, but the dynamics and consequences of most invasions are difficult to assess at regional scales. We deployed an airborne remote sensing system that mapped the location and impacts of five highly invasive plant species across 221,875 ha of Hawaiian ecosystems, identifying four distinct ways that these species transform the three-dimensional (3D) structure of native rain forests. In lowland to montane forests, three invasive tree species replace native midcanopy and understory plants, whereas one understory invader excludes native species at the ground level. A fifth invasive nitrogen-fixing tree, in combination with a midcanopy alien tree, replaces native plants at all canopy levels in lowland forests. We conclude that this diverse array of alien plant species, each representing a different growth form or functional type, is changing the fundamental 3D structure of native Hawaiian rain forests. Our work also demonstrates how an airborne mapping strategy can identify and track the spread of certain invasive plant species, determine ecological consequences of their proliferation, and provide detailed geographic information to conservation and management efforts.

The Carbon Mapper emissions monitoring system contributes to the broader ecosystem of greenhouse gas observations by locating and quantifying CH4 and CO2 super emitters at facility scale across priority regions globally and making the data accessible and actionable. The system includes observing platforms, an operational monitoring strategy optimized for mitigation impact, and a data platform that delivers CH4 and CO2 data products for diverse stakeholders. Operational scale-up of the system is centered around a new constellation of hyperspectral satellites. The Carbon Mapper Coalition (hereafter Tanager) satellites are each equipped with an imaging spectrometer instrument designed by NASA's Jet Propulsion Laboratory that are assembled, launched and operated by Planet Labs. The first Tanager satellite (Tanager-1) was launched 16 August 2024 completed commissioning in January 2025 and continued to improve observational efficiency through summer 2025. Planet is currently working to expand the constellation to four Tanagers. Each imaging spectrometer instrument has a spectral range of about 400–2500 nm, 5 nm spectral sampling, a nadir spatial resolution of 30 m, and nadir swath width of about 19 km at the lowest orbital altitude. Each satellite is capable of imaging 250 000 km2 per day on average. By combining the results of independent controlled release testing with empirical evaluation of the radiometric, spectral, spatial, and retrieval noise performance of the Tanager-1 spectrometer, we predict minimum detection limits of about 64–126 kgCH4 h−1 for CH4 point sources and about 10 078–18 994 kgCO2 h−1 for CO2 point sources for images with 25 % albedo, 45° solar zenith angle, and 3 m s−1 wind speed. A review of the first 11 months of Tanager-1 CH4 and CO2 observations including initial validation with coordinated aircraft under-flights and non-blind controlled release testing indicates that the system is meeting performance requirements and, in many cases, surpassing expectations. We also present early evaluations in challenging onshore and offshore observational conditions and summarize the first use of Tanager data to guide the timely mitigation of a CH4 super emitter.

Remote sensing data provide essential input for today's climate and ecosystem models. It is generally agreed that many model processes are not accurately depicted by current remotely sensed indices of vegetation and that new observational capabilities are needed at different spatial and spectral scales to reduce uncertainty. Recent advances in materials and optics have allowed the development of smaller, more stable, accurately calibrated imaging spectrometers that can quantify biophysical properties on the basis of the spectral absorbing and scattering characteristics of the land surface. Airborne and spaceborne imaging spectrometers, which measure large numbers (hundreds) of narrow spectral bands, are becoming more widely available from government and commercial sources; thus, it is increasingly feasible to use data from imaging spectroscopy for environmental research. In contrast to multispectral sensors, imaging spectroscopy produces quantitative estimates of biophysical absorptions, which can be used to improve scientific understanding of ecosystem functioning and properties. We present the recent advances in imaging spectroscopy and new capabilities for using it to quantify a range of ecological variables.

Anthropogenic carbon dioxide (CO2) emissions dominate uncertainties in the global carbon budget. Global inventories, such as the National Greenhouse Gas Inventories, have latencies of 12–24 months and may not keep pace with rapidly changing infrastructure, particularly in the developing world. Our work reveals that airborne and satellite imaging spectrometers provide 3–30 m spatial resolution and accurate quantification of CO2 emissions at the facility scale. Examples from 17 coal and gas fired power plants across the United States demonstrate robust correlation and 21% agreement on average between our remotely sensed estimates and simultaneous in situ measured emissions. We highlight four examples of coal‐fired power plants in India, Poland, and South Korea, where we quantify significant carbon dioxide emissions from power plants where limited public emissions data exist. Leveraging previous work on methane (CH4) plume detection, we present a strategy to exploit joint CO2 and CH4 plume imaging to quantify carbon emissions across widely distributed industrial infrastructure, including facilities that co‐emit CO2 and CH4. We show an example of a coal operation, where we attribute 25% of greenhouse gas emissions to coal extraction (CH4) and the remaining 75% to energy generation (CO2). Satellite spectrometers could track high emitting coal‐fired power plants that collectively contribute to 60% or more of global coal CO2 emissions. Multiple revisits and coordinated targeting of these high emitting facilities by multiple spaceborne instruments will be key to reducing uncertainties in global anthropogenic CO2 emissions and supporting emissions mitigation strategies. Plain Language Summary Carbon dioxide (CO2) emissions from power plants represents one of the largest sources of greenhouse gases from humans. Keeping track of CO2 emissions from all global power plants is difficult, as good emission data can depend on a country's emission reporting protocols. Remote sensing with imaging spectrometer instruments offers a new capability to do top‐down monitoring. These instruments provide high spatial resolution CO2 plume maps which can be used to quantify emissions. In this study, we show examples where we quantified and validated CO2 emissions at 21 global gas and coal fired power plants using airborne and satellite imaging spectrometers. With repeated targeting by satellites, we estimate that we could constrain 60% of all global power plant emissions. This capability is key to reducing uncertainties in global anthropogenic CO2 emission budgets and supporting emissions mitigation strategies. Key Points CO2 emissions are quantified and validated at 21 power plants using airborne and satellite imaging spectrometers With sufficient targeting, satellites could constrain at least 60% of global coal power plant CO2 emissions Imaging spectrometers are capable of joint CO2 and CH4 monitoring, enabling quantification of supply chain emissions

Remote sensing of inland waters can provide timely and global water quality information to a wide variety of stakeholders. One of the parameters that determines the feasibility of using optical space-based instruments for monitoring inland waters is the ground sampling distance (GSD), defined as the width of a pixel projected on the Earth’s surface. We assume that to analyze a body of water with optical imagery, its characteristic width must be larger than 3 times the GSD to obtain an ‘unmixed’ pixel that doesn’t contain signal from the adjacent land. Here we obtain the size distribution of river lengths, river areas, and lake areas—as a function of width—for rivers and lakes in the Western United States (US) and in Australia. We base this analysis on the Surface Water and Ocean Topography River Database (SWORD) and HydroLAKES databases, extrapolated to 5 m-wide features. We show that the fraction of river length and river area larger than a certain width increases sharply as the width decreases, indicating that even small decreases in the GSD result in significant increases in the number of bodies that can be surveyed. On the other hand, the distribution of lake areas shows a ‘knee’ at around 400 m, indicating that gains from GSDs smaller than 130 m will be modest. We found that a satellite instrument with a GSD capability of 18 m can provide coverage of 4.4% of total river lengths, 38% of total river area, and 94% of total lake area within the study areas. We argue that decreasing the GSD incurs penalties associated with loss of signal-to-noise, larger instrument, smaller swath, and longer revisit times.

Uncertainty in desert dust composition poses a big challenge to understanding Earth’s climate across different epochs. Of particular concern is hematite, an iron-oxide mineral dominating the solar absorption by dust particles, for which current estimates of absorption capacity vary by over two orders of magnitude. Here, we show that laboratory measurements of dust composition, absorption, and scattering provide valuable constraints on the absorption potential of hematite, substantially narrowing its range of plausible values. The success of this constraint is supported by results from an atmospheric transport model compared with station-based measurements. Additionally, we identify substantial bias in simulating hematite abundance in dust aerosols with current soil mineralogy descriptions, underscoring the necessity for improved data sources. Encouragingly, the next-generation imaging spectroscopy remote sensing data hold promise for capturing the spatial variability of hematite. These insights have implications for enhancing dust modeling, thus contributing to efforts in climate change mitigation and adaptation.