Prioritizing methane for near-term climate action is crucial due to its significant impact on global warming. Previous work used columnwise matched filter products from the airborne AVIRIS-NG imaging spectrometer to detect methane plume sources; convolutional neural networks (CNNs) discerned anthropogenic methane plumes from false positive enhancements. However, as an increasing number of remote sensing platforms are used for methane plume detection, there is a growing need to address cross-platform alignment. In this work, we describe model- and data-driven machine learning approaches that leverage airborne observations to improve spaceborne methane plume detection, reconciling the distributional shifts inherent with performing the same task across platforms. We develop a spaceborne methane plume classifier using data from the EMIT imaging spectroscopy mission. We refine classifiers trained on airborne imagery from AVIRIS-NG campaigns using transfer learning, outperforming the standalone spaceborne model. Finally, we use CycleGAN, an unsupervised image-to-image translation technique, to align the data distributions between airborne and spaceborne contexts. Translating spaceborne EMIT data to the airborne AVIRIS-NG domain using CycleGAN and applying airborne classifiers directly yields the best plume detection results. This methodology is useful not only for data simulation, but also for direct data alignment. Though demonstrated on the task of methane plume detection, our work more broadly demonstrates a data-driven approach to align related products obtained from distinct remote sensing instruments.

BioSCape, or the Biodiversity Survey of the Cape, is NASA’s first biodiversity-focused integrated remote sensing field campaign. The campaign aims to better understand the structure, function, and composition of the region’s ecosystems, and to learn about how and why they are changing in time. To do this, airborne and field data were collected across aquatic and terrestrial ecosystems in the Greater Cape Floristic Region in southwestern South Africa in 2023. Airborne acquisitions included data from three imaging spectrometers sampling across the electromagnetic spectrum (UVSWIR and TIR) and coincident full-waveform lidar data. Field datasets included measurements to quantify the diversity of plant communities including alien invasives and kelp, phytoplankton functional types, phylogenetic histories, eDNA in watersheds, bird and frog acoustics, plant functional and spectral traits, blue carbon, and in-water radiometry. BioSCape’s airborne and field datasets are diverse and complex, and the airborne data in particular is not inherently easy to use. In this paper we outline the procedures BioSCape followed to ensure maximum impact of the data and support of Open Science and FAIR data principles [1].

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).

Accurate atmospheric correction is critical for remote imaging spectroscopy of Earth’s surface. We present an overview of recent advances in the open-source atmospheric correction codebase ISOFIT that is designed for the application to NASA’s Earth Surface Mineral Dust Source Investigation (EMIT). The approach uses Bayesian Maximum A Posteriori (MAP) inference to simultaneously solve for the most likely surface and atmospheric state given a particular measurement. The main advantage is a rigorous uncertainty accounting and propagation, and the flexibility to incorporate diverse radiative transfer modeling assumptions. Released in 2018, the codebase has been used for a multitude of validation studies at several terrestrial and aquatic field sites, and is the current operational retrieval algorithm for the EMIT, AVIRIS-NG, and AVIRIS-C instruments.