Published Research

This study presents a sensitive and computationally inexpensive method for detecting methane plumes in MethaneAIR data using a matched-filter algorithm. The performance of the method was demonstrated through comparison with controlled release experiments, comparison with simulated plumes, and intercomparison with other methods. Authors applied this processing chain to MethaneAIR data mosaics acquired over the Permian Basin during flights in 2021 and 2023, which resulted in the detection of hundreds of point sources above 100–200 kg h−1, with a conservative detection limit of around 120 kg h−1.

MethaneSAT conducts monthly lunar calibration scans to ensure the high radiometric accuracy needed to detect and quantify methane emissions around the globe. By using the moon as a stable, well-characterized light source and comparing observations to the European Space Agency’s Lunar Irradiance Model, the team can monitor instrument performance, correct for drift, and strengthen confidence in methane retrievals. Successful lunar scans in 2024 demonstrate the viability of this approach and establish a foundation for long-term calibration trending that supports MethaneSAT’s mission.

This study shows that most U.S. oil and gas-sector methane emissions originate from many facilities that emit at low rates but are widespread. Using sensitive measurement techniques, the authors estimate that ~70% of total oil and gas methane emissions in the continental U.S. in 2021 came from low-emitting upstream and midstream facilities emitting below 100 kg/hour. The findings indicate that addressing only high-emitting sites is insufficient; and that effective methane mitigation must also include the many small, diffuse sources that collectively drive the majority of emissions.

Compiles over 1,500 ground-based facility measurements to develop a high-resolution, measurement-based inventory of 2021 U.S. oil and gas methane emissions. Total methane emissions are estimated to be roughly twice the U.S. EPA Greenhouse Gas Inventory, corresponding to a 2.6% gas-production-normalized methane loss rate, consistent with satellite data. Emissions vary widely by basin, with oil-dominant regions like the Permian, Bakken, and Uinta showing much higher loss rates than gas-dominant regions such as the Appalachian and Haynesville. Comparisons with airborne MethaneAIR data show good agreement with regional estimates and indicate that diffuse sources account for most emissions in key basins.

This study developed an artificial intelligence system that uses high-resolution satellite images to automatically map oil and gas well pads and storage tanks, which are important sources of methane emissions. When tested in two major U.S. oil-producing regions, the system accurately identified most known well sites and discovered more than 70,000 previously unlisted well pads and over 169,000 storage tanks, showing that satellite-based machine learning tools can help build more complete and transparent databases to better track and reduce methane pollution.

Authors independently evaluated five major aircraft-based methane sensing platforms, including MethaneAIR, through over 700 single-blind controlled methane releases ranging from 1 to 1,500 kg CH₄/h. Most platforms reliably detected and quantified emissions above 10 kg/h, indicating strong agreement with metered release rates. The findings demonstrate that aircraft-based methane monitoring has substantially matured and is well positioned to support industrial methane management and climate policy.

Authors describe details of the processor used in MethaneAIR, which converts raw (Level 0) sensor data into calibrated, geolocated, and scientifically usable radiance data (Level 1B) through dark current correction, noise estimate, stray-light removal, spectral calibration, radiometric correction, and orthorectification. It sets the foundation for operational MethaneSAT Level 0 to Level 1B processor.

Presents a probabilistic method for detecting and removing shadows in hyperspectral imagery from MethaneAIR. The approach estimates shadow fraction at each pixel and corrects spectra while preserving key methane-sensitive features. By reducing shadow-related artifacts, particularly in critical CO₂ and CH₄ absorption bands, the method expands usable area for methane detection and strengthens accuracy of emissions monitoring from airborne and satellite platforms.

Describes two independent quantification methods for methane plumes detected by MethaneAIR and demonstrates their accuracy using controlled release experiments. Authors quantified methane emissions using a modified integrated mass enhancement method and a divergence integral method. Comparison with controlled release experiments in 2021 and 2022 show that the accuracy of the sensor and algorithms is better than 25% for sources emitting 200 kg h−1 or more.