Remotely sensed data from imaging spectrometers can identify both methane (CH4) and carbon dioxide (CO2) enhancements, subsequently enabling the quantification and direct sectoral attribution necessary to better constrain greenhouse gas emissions and inform mitigation strategies at local, regional, and national scales. We validate the ability of the Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3), a NASA imaging spectrometer, to detect greenhouse gas enhancements from an airborne platform for the first time. Multiple CH4 controlled release experiments demonstrate that AVIRIS-3 can detect emissions at variable emission rates (<4-10 kg CH4/h) and multiple flight altitudes (e.g., both 2500 ft and 27,000 ft above ground level). Finally, we highlight a coincident acquisition of a CH4 plume from both AVIRIS-3 and the Earth surface Mineral dust source InvesTigation (EMIT) imaging spectrometer, located onboard the International Space Station. The AVIRIS-3 instrument design provides several key improvements over earlier generations of airborne imaging spectrometers that enable detailed and precise greenhouse gas mapping from a range of emission sectors.

Wildfires in wildland-urban interfaces (WUIs) are a growing concern due to their devastating impact on human communities and ecosystems. Low-latency impact assessment is critical for wildfire response, yet immediate access to fire-affected communities can be limited. Here, we demonstrate that unmixing char/ash fractional cover using imaging spectroscopy data can support rapid structural damage assessment. Using acquisitions over the 2025 Eaton Fire burn scar collected by the Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3), we demonstrate that a generic spectral endmember library is sufficient for binary structural damage classification between undamaged and destroyed buildings with an accuracy of 86.3%. Incorporating locally collected Eaton Fire endmembers into the library improves the accuracy by 1.3%. This demonstrates the feasibility of assessing structural damage from airborne imaging spectroscopy to rapidly inform post-fire response and recovery efforts in WUI communities.

Orbital imaging spectroscopy in the shortwave infrared is a powerful measurement technique that can lead to the detection and monitoring of both anthropogenic and natural methane point sources. Analysts map the absorption of methane by applying spectroscopic detection methods like the matched filter (MF) or Differential Optical Absorption Spectroscopy (DOAS) at each pixel in a scene. Unfortunately, methane plume identification is complicated by false detections due to instrument noise, especially for subtle methane enhancements at low signal levels, and surface spectroscopy features that mimic gas methane absorption features. Current systems rely on manual review to vet these detections. This review process is time consuming and can be subjective for plumes close to the instrument detection limit. As observations scale up from proof of concept studies with hundreds of plumes to global monitoring systems having thousands, improved automation will be necessary to reduce the cost and subjectivity of manual review. This paper presents a new purely spectroscopic approach for rejecting false positives based on the aggregate plume transmittance. Specifically, we use the total plume transmittance as a discriminator with improved model properties and noise statistics that can dramatically improve false positive rejection rates. A sampling approach provides rigorous detection p-values that account for spatial correlations in background interference. We demonstrate the approach on a catalog of thousands of plumes from the EMIT (Earth Surface Mineral Dust Source Investigation) imaging spectrometer, and illustrate its use in the context of a methane plume mapping workflow. This process identifies 92 potential errors in the existing review process: 42 likely false positives and 50 false negatives. Including spectral information during the plume review process can disambiguate marginal detections, making methane point source observing campaigns more robust and sensitive than a review process based primarily on plume morphology.

Imaging spectrometers like NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) and the Airborne Visible/Infrared Imaging Spectrometer 3 (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. We provide an update of EMIT methane and CO2 observations to date and highlight examples from the oil and gas, waste, and agriculture sectors. For the first time, we present AVIRIS-3 methane and CO2 results. The fine spatial resolution of these instruments allows pinpointing of multiple emission sources in close proximity from different sectors, which is not possible with coarser spatial resolution instruments. These instruments offer the potential to improve understanding of greenhouse gas budgets, inform mitigation strategies, and in some cases lead to voluntary mitigation. In support of NASA’s Open Source Science Initiative, all EMIT data and greenhouse gas data products are available through the Land Processes Distributed Active Archive Center (LP DAAC) and code is open source. EMIT results are also available through the greenhouse gas applications online mapping tool (https://earth.jpl.nasa.gov/emit/data/data-portal/Greenhouse-Gfases/) and U.S. Greenhouse Gas Center (https://earth.gov/ghgcenter/). Figure 1: Over 900 methane plume complexes observed by NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) are available through the EMIT greenhouse gas applications online mapping tool (https://earth.jpl.nasa.gov/emit/data/data-portal/Greenhouse-Gfases/) and U.S. Greenhouse Gas Center (https://earth.gov/ghgcenter/).

This report is about an intercomparison experiment that was performed to compare the methane plume detection and quantification based on EMIT remote sensing measurements. The experiment consisted of eight teams analyzing four measurement scenes from EMIT and reporting back results such as plume origins, plume outlines, estimated emission rates, and uncertainties. Comparisons of results across the team are reported, as well as the aggregate statistics, such as box and whisker plots of the emission rates, scatter of the plume origins, and coefficient of variance (COV) for the results. The small size of the datasets limits the generalizability of the results. One key outcome of this work was the development of methods for comparison of results, including application of rotation to plume origins to evaluate in the context of the wind field. We also tested the impact of holding variables constants, such as the wind speed. In this report, we show that the emission rate estimates have COVs of 30% to 50% in the majority of cases, and using the same wind speed across all teams did not drastically change this. The plume origin analysis showed that the cross wind spread of plume origin results was 50 m to 110 m in all but one case, and across wind was 60 m to 120 m in 13 of the 15 cases. The EMIT footprint is 60m, so the variations were roughly 1 to 2 pixels. In future work, we will build on the methods developed here and include measurements over controlled releases where the bias of the emission estimates can be assessed.

Spaceborne and airborne imaging spectrometers can identify methane (CH4) plumes and enable emission quantification and direct sectoral attribution necessary to better constrain methane emissions and inform mitigation strategies. We will show CH4 emission quantification results and accompanying uncertainty products for CH4 plume observations from NASA’s Earth surface Mineral dust source InvesTigation (EMIT) imaging spectrometer onboard the International Space Station, as well as the recently developed Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3). The differing spatial resolution and instrument sensitivity of the EMIT and AVIRIS-3 sensors are highly complementary for tiered CH4 plume detection and quantification. The large spatial coverage from EMIT allows us to identify and quantify previously unknown emissions from CH4 point sources across large regions of the Earth’s surface, while AVIRIS-3 has higher sensitivity and increased spatial resolution for characterizing CH4 emissions below EMIT’s detection limit. Building on a legacy of greenhouse gas retrievals first developed for airborne imaging spectrometers (e.g., AVIRIS, AVIRIS-NG), we use a matched filter approach to retrieve CH4 enhancements and the per-plume integrated mass enhancement (IME) method with windspeed data to estimate hourly CH4 emission rates. We take a two-pronged approach to validating our CH4 emission detection and quantification method: (1) an AVIRIS-3 CH4 controlled release experiment with multiple flow rates, and (2) evaluation of a simultaneous collection of AVIRIS-3 and EMIT in West Texas’ Permian Basin oil-and-gas producing region. This validation work will help provide confidence in EMIT’s plume quantification approach, which is important as imaging spectrometers are necessary for more comprehensive understanding of global CH4 point source emissions and greenhouse gas budgets, particularly in areas with limited reporting requirements. Lastly, the EMIT greenhouse gas portal (https://earth.jpl.nasa.gov/emit/data/data-portal/Greenhouse-Gases/) is actively distributing methane data products in support of NASA’s Open Source Science Initiative and AVIRIS-3 data will soon be publicly available for interested decision-makers and users (e.g., U.S. Greenhouse Gas Center).

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.

The Earth surface Mineral dust source InvesTigation (EMIT) is a remote visible to shortwave infrared (VSWIR) imaging spectrometer that has been operating onboard the International Space Station since July 2022. This article describes EMIT's on-orbit spectroradiometric calibration and validation. Accurate spectroscopy is vital to achieve consistent mapping results with orbital imaging spectrometers. EMIT takes a unique approach to this challenge, with just six optical elements, no shutter, and no onboard calibration systems. Its simple design focuses on uniformity and stability to enable vicarious spectroradiometric calibration. Our experiments demonstrate that this approach is successful, approaching the fidelity of manual field spectroscopy in some cases, and enabling new and more accurate products across diverse Earth science disciplines. EMIT achieves several notable firsts for an instrument of its class. It demonstrates successful on-orbit adjustments of Focal Plane Array (FPA) alignment with sub-micron precision. It offers spectral uniformity better than 98%. Optical artifacts in the measurement channels are at least three orders of magnitude below the primary solar-reflected surface signals. Its noise performance enables percent-level discrimination in the depths of mineral absorption features. In these aspects, EMIT satisfies the stringent performance needs for the next generation of VSWIR imaging spectrometers to observe the Earth's ecosystems, geology, and water resources.

The Earth surface Mineral dust source InvesTigation (EMIT) is an imaging spectrometer launched to the International Space Station in July 2022 to measure the mineral composition of Earth’s dust-producing regions. We present a systematic accuracy assessment of the EMIT surface reflectance product in two parts. First, we characterize the surface reflectance product’s overall performance using multiple independent vicarious calibration field experiments with hand-held and automated field spectrometers. We find that the EMIT surface reflectance product has a standard error of ±1.0% in absolute reflectance units for temporally coincident observations. Discrepancies rise to ±2.7 % for spectra acquired at different dates and times of day, which we attribute mainly to changes in solar geometry. Second, we develop an error budget that explains the differences between EMIT and in-situ field spectrometer data. We find that uncertainties in spatial footprints, field spectroscopy, and the EMIT-reported measurement were sufficient to explain discrepancies in most cases. Our approach did not detect any systematic calibration or reflectance errors in the timespan considered. Together, these findings demonstrate that a space-based imaging spectrometer can acquire high-quality spectra across a wide range of observational and atmospheric conditions.

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.

A fundamental challenge in verifying urban CO2 emissions reductions is estimating the biological influence that can confound emission source attribution across heterogeneous and diverse landscapes. Recent work using atmospheric radiocarbon revealed a substantial seasonal influence of the managed urban biosphere on regional carbon budgets in the Los Angeles megacity, but lacked spatially explicit attribution of the diverse biological influences needed for flux quantification and decision making. New high-resolution maps of land cover (0.6 in) and irrigation (30 in) derived from optical and thermal sensors can simultaneously resolve landscape influences related to vegetation type (tree, grass, shrub), land use, and fragmentation needed to accurately quantify biological influences on CO2 exchange in complex urban environments. We integrate these maps with the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM) to quantify spatial and seasonal variability in gross primary production (GPP) across urban and non-urban regions of Southern California Air Basin (SoCAB). Results show that land use and landscape fragmentation have a significant influence on urban GPP and canopy temperature within the water-limited Mediterranean SoCAB climate. Irrigated vegetation accounts for 31% of urban GPP, driven by turfgrass, and is more productive (1.7 vs 0.9 mu mol m(-2) s(-1)) and cooler (2.2 +/- 0.5 K) than non-irrigated vegetation during hot dry summer months. Fragmented landscapes, representing mostly vegetated urban greenspaces, account for 50% of urban GPP. Cooling from irrigation alleviates strong warming along greenspace edges within 100 m of impervious surfaces, and increases GPP by a factor of two, compared to non-irrigated edges. Finally, we note that non-irrigated shrubs are typically more productive than non- irrigated trees and grass, and equally productive as irrigated vegetation. These results imply a potential water savings benefit of urban shrubs, but more work is needed to understand carbon vs water usage tradeoffs of managed vs unmanaged vegetation. (C) 2021 Elsevier B.V. All rights reserved.

It is important to understand the distribution of irrigated and non-irrigated vegetation in rapidly expanding urban areas that are experiencing climate-induced changes in water availability, such as Los Angeles, California. Mapping irrigated vegetation in Los Angeles is necessary for developing sustainable water use practices and accurately accounting for the megacity’s carbon exchange and water balance changes. However, pre-existing maps of irrigated vegetation are largely limited to agricultural regions and are too coarse to resolve heterogeneous urban landscapes. Previous research suggests that irrigation has a strong cooling effect on vegetation, especially in semi-arid environments. The July 2018 launch of the ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) offers an opportunity to test this hypothesis using retrieved land surface temperature (LST) data in complex, heterogeneous urban/non-urban environments. In this study, we leverage Landsat 8 optical imagery and 30 m sharpened afternoon summertime ECOSTRESS LST, then apply very high-resolution (0.6–10 m) vegetation fraction weighting to produce a map of irrigated and non-irrigated vegetation in Los Angeles. This classification was compared to other classifications using different combinations of sensors in order to offer a preliminary accuracy and uncertainty assessment. This approach verifies that ECOSTRESS LST data provides an accurate map (98.2% accuracy) of irrigated urban vegetation in southern California that has the potential to reduce uncertainties in regional carbon and hydrological cycle models.

High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities.