Articles | Volume 15, issue 6
https://doi.org/10.5194/amt-15-1657-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-1657-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Mar 2022
Research article |
| 21 Mar 2022
| 21 Mar 2022
Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite
Elena Sánchez-García
CORRESPONDING AUTHOR
Research Institute of Water and Environmental Engineering (IIAMA),
Universitat Politècnica de València, Valencia, Spain
Javier Gorroño
Research Institute of Water and Environmental Engineering (IIAMA),
Universitat Politècnica de València, Valencia, Spain
Itziar Irakulis-Loitxate
Research Institute of Water and Environmental Engineering (IIAMA),
Universitat Politècnica de València, Valencia, Spain
Daniel J. Varon
School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA, USA
Luis Guanter
Research Institute of Water and Environmental Engineering (IIAMA),
Universitat Politècnica de València, Valencia, Spain
Related authors
No articles found.
Daniel Cusworth, Daniel Varon, Dylan Jervis, Jason McKeever, Alexander Turner, Christian Frankenberg, John Worden, Andrew Thorpe, Jay Fahlen, Sudhanshu Pandey, Joannes Maasakkers, Ilse Aben, Annmarie Eldering, Riley Duren, Bradley Conrad, Tia Scarpelli, Daniel Bon, Mackenzie Huffman, Itziar Irakulis-Loitxate, Luis Guanter, Jason Schroeder, Benjamin Hmiel, Evan Sherwin, Sean Crowell, Eric Kort, Adam Brandt, Frances Reuland, Maryann Sargent, James East, and Daniel Jacob
EGUsphere, https://doi.org/10.5194/egusphere-2026-6060, https://doi.org/10.5194/egusphere-2026-6060, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
In this review, we summarize the current and anticipated state of satellite remote sensing for point sources. In particular, we are provide and update to the fleet of point source imaging satellites, summarize the nuance around data availability and revisit, detail data processing pipelines and validation procedures, describe how to benchmark detection performance, and finally, describe demonstrated use-cases for satellite point source observations.
Dandan Zhang, Daniel J. Jacob, Elizabeth W. Lundgren, Melissa P. Sulprizio, Nicholas Balasus, Lucas A. Estrada, Emily Reidy, and Daniel J. Varon
EGUsphere, https://doi.org/10.5194/egusphere-2026-4399, https://doi.org/10.5194/egusphere-2026-4399, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Dense satellite observations call for global high-resolution inversions to infer surface fluxes. The computational cost for explicit Jacobian construction has limited global analytical inversions to coarse resolution despite the availability of fine-resolution satellite observations. Here we show the stretched-grid high-performance GEOS-Chem model can considerably reduce the computational and storage costs while closely reproducing the posterior fluxes from the standard inversion results.
Lulu Si, Shanyu Zhou, Itziar Irakulis-Loitxate, Javier Roger, and Luis Guanter
Earth Syst. Sci. Data, 18, 6545–6564, https://doi.org/10.5194/essd-18-6545-2026, https://doi.org/10.5194/essd-18-6545-2026, 2026
Short summary
Short summary
We constructed a dataset of offshore oil and gas platforms to improve knowledge of human activities at sea and their environmental impacts. Using satellite images from the Sentinel-1 and Sentinel-2 missions, we identified over 5000 platforms in six major offshore basins between 2017 and 2023 and recorded their locations and status. This open dataset extends existing records and supports environmental monitoring, offshore infrastructure management, and planning of mitigation strategies.
Daniel A. Potts, Harjinder Sembhi, Matthieu Dogniaux, Joannes D. Maasakkers, Berend J. Schuit, Dirk Schuettemeyer, Timon Hummel, Rocio Barrio Guillo, Oliver Schneising, Luis Guanter, and Ilse Aben
EGUsphere, https://doi.org/10.5194/egusphere-2026-3797, https://doi.org/10.5194/egusphere-2026-3797, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Routine monitoring of industrial emissions is vital to understand the location, frequency, and intensity of methane emissions. We used satellite and aircraft measurements to understand methane emissions from Madrid across various scales and sectors. Using a range of methods, we identify a clear gap between top-down and bottom-up approaches, discuss challenges associated with satellite interpretation, connect emissions to on-site practices, and make recommendations towards operational use.
Lucas A. Estrada, Daniel J. Jacob, Megan He, James D. East, Daniel J. Varon, Nicholas Balasus, Sarah E. Hancock, Melissa Sulprizio, Kevin W. Bowman, John R. Worden, Emily Reidy, and Benjamin R. K. Runkle
Atmos. Chem. Phys., 26, 10629–10646, https://doi.org/10.5194/acp-26-10629-2026, https://doi.org/10.5194/acp-26-10629-2026, 2026
Short summary
Short summary
We use satellite observations to track US methane emissions from 2019–2024 at national, state, and oil and gas field scales. While total emissions remain stable, oil and gas emissions decline even as production rises, indicating progress in emissions management for the oil and gas sector.
Sarah E. Hancock, Daniel J. Jacob, Rodrigo Jimenez, Andrés Ardila, Luis Morales-Rincon, Néstor Rojas, Lucas A. Estrada, Nicholas Balasus, James D. East, Melissa P. Sulprizio, Xiaolin Wang, James L. France, Lauren Potyk, Elise Penn, Zichong Chen, Daniel J. Varon, Christian Frankenberg, Marci Baranski, Andreea Calcan, and Robert J. Parker
Atmos. Chem. Phys., 26, 10455–10476, https://doi.org/10.5194/acp-26-10455-2026, https://doi.org/10.5194/acp-26-10455-2026, 2026
Short summary
Short summary
We combined satellite observations with detailed national data to improve estimates of Colombia’s methane emissions. Our results show that total methane emissions are higher than reported, with the biggest differences from fossil fuels. This approach demonstrates how satellite data can make national greenhouse gas reporting more accurate and transparent under the Paris Agreement.
John Worden, Sudhanshu Pandey, Hannah Nesser, Kevin Bowman, Colin Harkins, Congmeng Lyu, Joannes D. Maasakkers, Deborah Gordon, Daniel Jacob, Lucas Estrada, Daniel J. Varon, James D. East, Lauren Schmeisser, and Zhen Qu
Atmos. Chem. Phys., 26, 8855–8873, https://doi.org/10.5194/acp-26-8855-2026, https://doi.org/10.5194/acp-26-8855-2026, 2026
Short summary
Short summary
To support methane reduction, we compared three widely used maps of methane emissions in the United States with estimates derived from a methane-measuring satellite from 2012 to 2020. The satellite indicates higher emissions from oil and gas production and from livestock in several regions, while other sources were harder to pin down. Neither approach shows a clear change over time. The results point to where better measurements can most improve emissions reporting and guide mitigation.
Javier Gorroño, Zhipeng Pei, Adriana Valverde, and Luis Guanter
Atmos. Meas. Tech., 19, 1245–1257, https://doi.org/10.5194/amt-19-1245-2026, https://doi.org/10.5194/amt-19-1245-2026, 2026
Short summary
Short summary
Methane plumes can be detected with several instruments from space at a high spatial resolution nowadays. We see a ground projection of these methane plumes from the satellites that, similarly to clouds or buildings, are distorted depending on the observation and illumination angle. Here we highlight this issue and propose a methodology to account for it using simulations that could enhance current and upcoming retrieval and quantification algorithms.
Srijana Lama, Joannes D. Maasakkers, Xin Zhang, Marianne Girard, Swarna Dutt, Suyash Nandgaonkar, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Nicholas Balasus, Robert J. Parker, Yukio Terao, and Ilse Aben
EGUsphere, https://doi.org/10.5194/egusphere-2025-6528, https://doi.org/10.5194/egusphere-2025-6528, 2026
Short summary
Short summary
Methane is the second most important anthropogenic greenhouse gas in terms of radiative forcing, with a global warming potential 27–30 times that of CO2 over a 100-year timescale. Effective mitigation requires a detailed understanding of the spatial distribution and magnitude of emissions. Here, we quantify India’s 2021 methane emissions at a resolution of up to 0.25° × 0.3125° using TROPOMI observations within the Bayesian Integrated Methane Inversion framework.
Hannah Nesser, Kevin W. Bowman, Matthew D. Thill, Daniel J. Varon, Cynthia A. Randles, Ashutosh Tewari, Felipe J. Cardoso-Saldaña, Emily Reidy, Joannes D. Maasakkers, and Daniel J. Jacob
Geosci. Model Dev., 18, 9279–9291, https://doi.org/10.5194/gmd-18-9279-2025, https://doi.org/10.5194/gmd-18-9279-2025, 2025
Short summary
Short summary
Regional analyses of atmospheric trace gases can improve knowledge of fluxes at high resolution but rely on specified boundary conditions (BCs) at the domain edges. Biases in the often-uncertain BCs propagate to the inferred fluxes. We develop a framework to explain how errors in the BCs influence the optimized fluxes, derive two metrics to estimate this influence, and compare two methods to correct for the biases. We demonstrate correcting BCs directly is more effective at reducing bias.
Jack H. Bruno, Daniel J. Jacob, Xiaolin Wang, Melissa P. Sulprizio, Lucas A. Estrada, Daniel J. Varon, Steven C. Wofsy, Mark Omara, and Ritesh Gautam
EGUsphere, https://doi.org/10.5194/egusphere-2025-4626, https://doi.org/10.5194/egusphere-2025-4626, 2025
Preprint archived
Short summary
Short summary
We use data from both aircraft (MethaneAIR) and satellite (TROPOMI) based measurements of methane in the atmosphere to better understand methane emissions from fossil fuel extraction. Data from these instruments are combined with a computer model of the atmosphere to improve estimates of methane emissions. We find that combining data from multiple sources provides more information than either source on its own. The tools and data we use are freely available.
Drew C. Pendergrass, Daniel J. Jacob, Nicholas Balasus, Lucas Estrada, Daniel J. Varon, James D. East, Megan He, Todd A. Mooring, Elise Penn, Hannah Nesser, and John R. Worden
Atmos. Chem. Phys., 25, 14353–14369, https://doi.org/10.5194/acp-25-14353-2025, https://doi.org/10.5194/acp-25-14353-2025, 2025
Short summary
Short summary
We use satellite observations of atmospheric methane, a greenhouse gas, to calculate emissions from both human and natural sources. We find that methane emissions surged in 2020–2021 before declining in 2022–2023. We attribute the surge in large part to emissions from eastern Africa, which experienced large methane-generating floods. Wetland models underestimate emissions in that region, which has led some previous work to incorrectly attribute the African surge in methane emissions to livestock.
Javier Roger, Luis Guanter, and Javier Gorroño
Atmos. Meas. Tech., 18, 5545–5567, https://doi.org/10.5194/amt-18-5545-2025, https://doi.org/10.5194/amt-18-5545-2025, 2025
Short summary
Short summary
Detection of methane point source emissions with satellite-based imaging spectrometers is assessed in offshore areas. After integrating simulations into data from 2 different satellite missions, we extract the probability of detection. We compare our results with real plumes, which shows the consistency of the models. Detection and monitoring efforts can be optimized by filtering data based on these results, reducing unnecessary searches and increasing the action taken on these emissions.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Shutao Zhao, Yuzhong Zhang, Shuang Zhao, Xinlu Wang, and Daniel J. Varon
Atmos. Chem. Phys., 25, 4035–4052, https://doi.org/10.5194/acp-25-4035-2025, https://doi.org/10.5194/acp-25-4035-2025, 2025
Short summary
Short summary
We target the challenge of detecting methane super-emitters in oil and gas fields, which is critical for mitigating climate change. Traditional satellite-based detectors struggle due to interference from complex surfaces. We developed a novel method using deep transfer learning that improves detection efficiency and accuracy by reducing artifacts and adapting methane knowledge to different regions. Application revealed significant methane emissions, demonstrating the potential of our method.
Sarah E. Hancock, Daniel J. Jacob, Zichong Chen, Hannah Nesser, Aaron Davitt, Daniel J. Varon, Melissa P. Sulprizio, Nicholas Balasus, Lucas A. Estrada, María Cazorla, Laura Dawidowski, Sebastián Diez, James D. East, Elise Penn, Cynthia A. Randles, John Worden, Ilse Aben, Robert J. Parker, and Joannes D. Maasakkers
Atmos. Chem. Phys., 25, 797–817, https://doi.org/10.5194/acp-25-797-2025, https://doi.org/10.5194/acp-25-797-2025, 2025
Short summary
Short summary
We quantify 2021 methane emissions in South America at up to 25 km × 25 km resolution using satellite methane observations. We find a 55 % upward adjustment to anthropogenic emission inventories, including those reported to the UN Framework Convention on Climate Change under the Paris Agreement. Our estimates match inventories for Brazil, Bolivia, and Paraguay but are much higher for other countries. Livestock emissions (65 % of anthropogenic emissions) show the largest discrepancies.
Brian Nathan, Joannes D. Maasakkers, Stijn Naus, Ritesh Gautam, Mark Omara, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Alba Lorente, Tobias Borsdorff, Robert J. Parker, and Ilse Aben
Atmos. Chem. Phys., 24, 6845–6863, https://doi.org/10.5194/acp-24-6845-2024, https://doi.org/10.5194/acp-24-6845-2024, 2024
Short summary
Short summary
Venezuela's Lake Maracaibo region is notoriously hard to observe from space and features intensive oil exploitation, although production has strongly decreased in recent years. We estimate methane emissions using 2018–2020 TROPOMI satellite observations with national and regional transport models. Despite the production decrease, we find relatively constant emissions from Lake Maracaibo between 2018 and 2020, indicating that there could be large emissions from abandoned infrastructure.
Matthieu Dogniaux, Joannes D. Maasakkers, Daniel J. Varon, and Ilse Aben
Atmos. Meas. Tech., 17, 2777–2787, https://doi.org/10.5194/amt-17-2777-2024, https://doi.org/10.5194/amt-17-2777-2024, 2024
Short summary
Short summary
We analyze Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the 2022 Nord Stream 2 methane leak and show how challenging this case is for usual data analysis methods. We provide customized calibrations for this Nord Stream 2 case and assess that no firm conclusion can be drawn from L8 or S-2B single overpasses. However, if we opportunistically assume that L8 and S-2B results are independent, we find an averaged L8 and S-2B combined methane leak rate of 502 ± 464 t h−1.
Jack H. Bruno, Dylan Jervis, Daniel J. Varon, and Daniel J. Jacob
Atmos. Meas. Tech., 17, 2625–2636, https://doi.org/10.5194/amt-17-2625-2024, https://doi.org/10.5194/amt-17-2625-2024, 2024
Short summary
Short summary
Methane is a potent greenhouse gas and a current high-priority target for short- to mid-term climate change mitigation. Detection of individual methane emitters from space has become possible in recent years, and the volume of data for this task has been rapidly growing, outpacing processing capabilities. We introduce an automated approach, U-Plume, which can detect and quantify emissions from individual methane sources in high-spatial-resolution satellite data.
Anna Vaughan, Gonzalo Mateo-García, Luis Gómez-Chova, Vít Růžička, Luis Guanter, and Itziar Irakulis-Loitxate
Atmos. Meas. Tech., 17, 2583–2593, https://doi.org/10.5194/amt-17-2583-2024, https://doi.org/10.5194/amt-17-2583-2024, 2024
Short summary
Short summary
Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming since the industrial revolution. Consequently identifying and mitigating methane emissions comprise an important step in combating the climate crisis. We develop a new deep learning model to automatically detect methane plumes from satellite images and demonstrate that this can be applied to monitor large methane emissions resulting from the oil and gas industry.
Russell Doughty, Yujie Wang, Jennifer Johnson, Nicholas Parazoo, Troy Magney, Zoe Pierrat, Xiangming Xiao, Luis Guanter, Philipp Köhler, Christian Frankenberg, Peter Somkuti, Shuang Ma, Yuanwei Qin, Sean Crowell, and Berrien Moore III
EGUsphere, https://doi.org/10.22541/essoar.168167172.20799710/v1, https://doi.org/10.22541/essoar.168167172.20799710/v1, 2024
Preprint archived
Short summary
Short summary
Here we present a novel model of global photosynthesis, ChloFluo, which uses spaceborne chlorophyll fluorescence to estimate the amount of photosynthetically active radiation absorbed by chlorophyll. Potential uses of our model are to advance our understanding of the timing and magnitude of photosynthesis, its effect on atmospheric carbon dioxide fluxes, and vegetation response to climate events and change.
Javier Roger, Luis Guanter, Javier Gorroño, and Itziar Irakulis-Loitxate
Atmos. Meas. Tech., 17, 1333–1346, https://doi.org/10.5194/amt-17-1333-2024, https://doi.org/10.5194/amt-17-1333-2024, 2024
Short summary
Short summary
Methane emissions can be identified using remote sensing, but surface-related structures disturb detection. In this work, a variation of the matched filter method that exploits a large fraction of the near-infrared range (1000–2500 nm) is applied. In comparison to the raw matched filter, it reduces background noise and strongly attenuates the surface-related artifacts, which leads to a greater detection capability. We propose this variation as a standard methodology for methane detection.
Berend J. Schuit, Joannes D. Maasakkers, Pieter Bijl, Gourav Mahapatra, Anne-Wil van den Berg, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Sander Houweling, Daniel J. Varon, Jason McKeever, Dylan Jervis, Marianne Girard, Itziar Irakulis-Loitxate, Javier Gorroño, Luis Guanter, Daniel H. Cusworth, and Ilse Aben
Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, https://doi.org/10.5194/acp-23-9071-2023, 2023
Short summary
Short summary
Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
Short summary
Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Nicholas Balasus, Daniel J. Jacob, Alba Lorente, Joannes D. Maasakkers, Robert J. Parker, Hartmut Boesch, Zichong Chen, Makoto M. Kelp, Hannah Nesser, and Daniel J. Varon
Atmos. Meas. Tech., 16, 3787–3807, https://doi.org/10.5194/amt-16-3787-2023, https://doi.org/10.5194/amt-16-3787-2023, 2023
Short summary
Short summary
We use machine learning to remove biases in TROPOMI satellite observations of atmospheric methane, with GOSAT observations serving as a reference. We find that the TROPOMI biases relative to GOSAT are related to the presence of aerosols and clouds, the surface brightness, and the specific detector that makes the observation aboard TROPOMI. The resulting blended TROPOMI+GOSAT product is more reliable for quantifying methane emissions.
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, https://doi.org/10.5194/acp-23-7503-2023, 2023
Short summary
Short summary
We use TROPOMI satellite observations to quantify weekly methane emissions from the US Permian oil and gas basin from May 2018 to October 2020. We find that Permian emissions are highly variable, with diverse economic and activity drivers. The most important drivers during our study period were new well development and natural gas price. Permian methane intensity averaged 4.6 % and decreased by 1 % per year.
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, https://doi.org/10.5194/acp-23-5945-2023, 2023
Short summary
Short summary
We quantify methane emissions from individual countries in the Middle East and North Africa by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We show that the ability to simply relate oil/gas emissions to activity metrics is compromised by stochastic nature of local infrastructure and management practices. We find that the industry target for oil/gas methane intensity is achievable through associated gas capture, modern infrastructure, and centralized operations.
Javier Gorroño, Daniel J. Varon, Itziar Irakulis-Loitxate, and Luis Guanter
Atmos. Meas. Tech., 16, 89–107, https://doi.org/10.5194/amt-16-89-2023, https://doi.org/10.5194/amt-16-89-2023, 2023
Short summary
Short summary
We present a methane flux rate retrieval methodology using the Sentinel-2 mission, validating the algorithm for different scenes and plumes. The detection limit is 1000–2000 kg h−1 for homogeneous scenes and temporally invariant surfaces and above 5000 kg h−1 for heterogeneous ones. Dominant quantification errors are wind-related or plume mask-related. For heterogeneous scenes, the surface structure underlying the methane plume can become a dominant source of uncertainty.
Zhan Zhang, Evan D. Sherwin, Daniel J. Varon, and Adam R. Brandt
Atmos. Meas. Tech., 15, 7155–7169, https://doi.org/10.5194/amt-15-7155-2022, https://doi.org/10.5194/amt-15-7155-2022, 2022
Short summary
Short summary
This work developed a multi-band–multi-pass–multi-comparison-date Sentinel-2 methane retrieval algorithm, and the method was calibrated by data from a controlled release test. To our knowledge, this is the first study that validates the performance of a Sentinel-2 methane detection algorithm by calibration with a ground-truth testing. It illustrates the potential for additional validation with systematic future experiments wherein algorithms can be tuned to meet different detection expectations.
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, https://doi.org/10.5194/acp-22-11203-2022, 2022
Short summary
Short summary
We use 22 months of TROPOMI satellite observations to quantity methane emissions from the oil (O) and natural gas (G) sector in the US and Canada at the scale of both individual basins as well as country-wide aggregates. We find that O/G-related methane emissions are underestimated in these inventories by 80 % for the US and 40 % for Canada, and 70 % of the underestimate in the US is from five O/G basins, including Permian, Haynesville, Anadarko, Eagle Ford, and Barnett.
Zichong Chen, Daniel J. Jacob, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Elise Penn, and Xueying Yu
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, https://doi.org/10.5194/acp-22-10809-2022, 2022
Short summary
Short summary
We quantify methane emissions in China and contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We find that anthropogenic methane emissions for China are underestimated in the national inventory. Our estimate of emissions indicates a small life-cycle loss rate, implying net climate benefits from the current
coal-to-gasenergy transition in China. However, this small loss rate can be misleading given China's high gas imports.
Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, and Riley M. Duren
Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, https://doi.org/10.5194/acp-22-9617-2022, 2022
Short summary
Short summary
We review the capability of satellite observations of atmospheric methane to quantify methane emissions on all scales. We cover retrieval methods, precision requirements, inverse methods for inferring emissions, source detection thresholds, and observations of system completeness. We show that current instruments already enable quantification of regional and national emissions including contributions from large point sources. Coverage and resolution will increase significantly in coming years.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary
Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Luis Guanter, Cédric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher, Philipp Köhler, Christian Frankenberg, Joanna Joiner, and Yongguang Zhang
Earth Syst. Sci. Data, 13, 5423–5440, https://doi.org/10.5194/essd-13-5423-2021, https://doi.org/10.5194/essd-13-5423-2021, 2021
Short summary
Short summary
Sun-induced chlorophyll fluorescence (SIF) is an electromagnetic signal emitted by plants in the red and far-red parts of the spectrum. It has a functional link to photosynthesis and can be measured by satellite instruments, which makes it an important variable for the remote monitoring of the photosynthetic activity of vegetation ecosystems around the world. In this contribution we present a SIF dataset derived from the new Sentinel-5P TROPOMI missions.
Cited articles
Borchardt, J., Gerilowski, K., Krautwurst, S., Bovensmann, H., Thorpe, A. K., Thompson, D. R., Frankenberg, C., Miller, C. E., Duren, R. M., and Burrows, J. P.: Detection and quantification of CH4 plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data, Atmos. Meas. Tech., 14, 1267–1291, https://doi.org/10.5194/amt-14-1267-2021, 2021.
Cusworth, D. H., Jacob, D. J., Varon, D. J., Chan Miller, C., Liu, X., Chance, K., Thorpe, A. K., Duren, R. M., Miller, C. E., Thompson, D. R., Frankenberg, C., Guanter, L., and Randles, C. A.: Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space, Atmos. Meas. Tech., 12, 5655–5668, https://doi.org/10.5194/amt-12-5655-2019, 2019.
Cusworth, D. H., Duren, R. M., Thorpe, A. K., Tseng, E., Thompson, D., Guha,
A., Newman, S., Foster, K. T., and Miller, C. E.: Using remote sensing to
detect, validate, and quantify methane emissions from California solid waste
operations, Environ. Res. Lett., 15, 054012, https://doi.org/10.1088/1748-9326/ab7b99, 2020.
Cusworth, D. H., Duren, R. M., Thorpe, A. K., Olson-duvall, W., Heckler, J.,
Chapman, J. W., Eastwood, M. L., Helmlinger, M. C., Green, R. O., Asner, G.
P., Dennison, P. E., and Miller, C. E.: Intermittency of Large Methane
Emitters in the Permian Basin, Environ. Sci. Technol. Lett., 8, 567–573, https://doi.org/10.1021/acs.estlett.1c00173, 2021.
De Gouw, J. A., Vee, J. P., Roosenbrand, E., Dix, B., Lin, J. C., Landgraf,
J., and Levelt, P. F.: Daily Satellite Observations of Methane from Oil and
Gas Production Regions in the United States, Sci. Rep., 10, 1379,
https://doi.org/10.1038/s41598-020-57678-4, 2020.
Duren, R. M., Thorpe, A. K., Foster, K. T., Rafiq, T., Hopkins, F. M.,
Yadav, V., Bue, B. D., Thompson, D. R., Conley, S., Colombi, N. K.,
Frankenberg, C., McCubbin, I. B., Eastwood, M. L., Falk, M., Herner, J. D.,
Croes, B. E., Green, R. O., and Miller, C. E.: California's methane
super-emitters, Nature, 575, 180–184, https://doi.org/10.1038/s41586-019-1720-3,
2019.
European Commision: Communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions, https://ec.europa.eu/info/publications/communication-commission-european-parliament-council-european-economic-and-social-committee-and-committee-regions_es, last access: 15 March 2020.
European Space Imaging: Worldview-3 datasheet, European Space Imaging, https://www.euspaceimaging.com/worldview-3/ (last access: 15 March 2022), 2020.
Frankenberg, C., Thorpe, A. K., Thompson, D. R., Hulley, G., Kort, E. A.,
Vance, N., Borchardt, J., Krings, T., Gerilowski, K., Sweeney, C., Conley,
S., Bue, B. D., Aubrey, A. D., Hook, S., and Green, R. O.: Airborne methane
remote measurements reveal heavytail flux distribution in Four Corners
region, P. Natl. Acad. Sci. USA, 113, 9734–9739,
https://doi.org/10.1073/pnas.1605617113, 2016.
Guanter, L., Irakulis-Loitxate, I., Gorroño, J.,
Sánchez-García, E., Cusworth, D. H., Varon, D. J., Cogliati, S., and
Colombo, R.: Mapping methane point emissions with the PRISMA spaceborne
imaging spectrometer, Remote Sens. Environ., 265, 112671,
https://doi.org/10.1016/j.rse.2021.112671, 2021.
Hu, H., Landgraf, J., Detmers, R., Borsdorff, T., Aan de Brugh, J., Aben,
I., Butz, A., and Hasekamp, O.: Toward Global Mapping of Methane With
TROPOMI: First Results and Intersatellite Comparison to GOSAT, Geophys. Res.
Lett., 45, 3682–3689, https://doi.org/10.1002/2018GL077259, 2018.
Irakulis-Loitxate, I., Guanter, L., Liu, Y. N., Varon, D. J., Maasakkers, J.
D., Zhang, Y., Chulakadabba, A., Wofsy, S. C., Thorpe, A. K., Duren, R. M.,
Frankenberg, C., Lyon, D. R., Hmiel, B., Cusworth, D. H., Zhang, Y., Segl,
K., Gorroño, J., Sánchez-García, E., Sulprizio, M. P., Cao, K.,
Zhu, H., Liang, J., Li, X., Aben, I., and Jacob, D. J.: Satellite-based
survey of extreme methane emissions in the Permian basin, Sci. Adv., 7, eabf4507, https://doi.org/10.1126/sciadv.abf4507, 2021.
Irakulis-Loitxate, I., Guanter, L., Maasakkers, J. D., Zavala-Araiza, D., and
Aben, I.: Satellites Detect Abatable Super-Emissions in One of the World's
Largest Methane Hotspot Regions, Environ. Sci. Technol., 56, 2143–2152, https://doi.org/10.1021/acs.est.1c04873, 2022.
Jackson, R. B., Saunois, M., Bousquet, P., Canadell, J. G., Poulter, B., Stavert, A. R., Bergamaschi, P., Niwa, Y., Segers, A., and Tsuruta, A.: Increasing anthropogenic methane emissions arise equally from agricultural and fossil fuel sources, Environ. Res. Lett., 15, 071002, https://doi.org/10.1088/1748-9326/ab9ed2, 2020.
Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sheng, J., Sun, K., Liu, X., Chance, K., Aben, I., McKeever, J., and Frankenberg, C.: Satellite observations of atmospheric methane and their value for quantifying methane emissions, Atmos. Chem. Phys., 16, 14371–14396, https://doi.org/10.5194/acp-16-14371-2016, 2016.
JCGM: Evaluation of measurement data – Supplement 1 to the “Guide to the
expression of uncertainty in measurement” – Propagation of distributions
using a Monte Carlo method, in: JCGM guidance document, vol. JCGM 101, p. 90, ISO/IEC Guide 98-3/Suppl.1:2008, https://www.bipm.org/en/committees/jc/jcgm/publications (last access: 15 March 2022), 2008.
Jervis, D., McKeever, J., Durak, B. O. A., Sloan, J. J., Gains, D., Varon, D. J., Ramier, A., Strupler, M., and Tarrant, E.: The GHGSat-D imaging spectrometer, Atmos. Meas. Tech., 14, 2127–2140, https://doi.org/10.5194/amt-14-2127-2021, 2021.
Jongaramrungruang, S., Frankenberg, C., Matheou, G., Thorpe, A. K., Thompson, D. R., Kuai, L., and Duren, R. M.: Towards accurate methane point-source quantification from high-resolution 2-D plume imagery, Atmos. Meas. Tech., 12, 6667–6681, https://doi.org/10.5194/amt-12-6667-2019, 2019.
Kochanov, R. V, Gordon, I. E., Rothman, L. S., Wcis, P., Hill, C., and
Wilzewski, J. S.: Journal of Quantitative Spectroscopy & Radiative
Transfer HITRAN Application Programming Interface (HAPI): A comprehensive
approach to working with spectroscopic data, J. Quant. Spectrosc. Ra., 177, 15–30, https://doi.org/10.1016/j.jqsrt.2016.03.005, 2016.
Lauvaux, T., Giron, C., Mazzolini, M., Duren, R., Cusworth, D., Shindell, D., and Ciais, P.: Global Assessment of Oil and Gas Methane Ultra-Emitters,
Science, 375, 557–561, https://doi.org/10.1126/science.abj4351, 2022.
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I., and Eichmann,
A.: NASA/TM – 2012-104606/Vol 28, Technical Report Series on Global
Modeling and Data Assimilation, NASA/TM–2012-104606/Vol. 28, The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna April 2012, National Aeronautics and Space Administration, Goddard Space Flight Center Greenbelt, Maryland 20771, USA, 2012.
Sadavarte, P., Pandey, S., Maasakkers, J. D., Lorente, A., Gon, H. D. Van
Der, Houweling, S., and Aben, I.: Methane Emissions from Super-emitting Coal
Mines in Australia quantified using TROPOMI Satellite Observations, Environ. Sci. Technol., 55, 16573–16580, https://doi.org/10.1021/acs.est.1c03976, 2021.
Thompson, D. R., Thorpe, A. K., Frankenberg, C., Green, R. O., Duren, R.,
Guanter, L., Hollstein, A., Middleton, E., Ong, L., and Ungar, S.:
Space-based remote imaging spectroscopy of the Aliso Canyon CH4
superemitter, Geophys. Res. Lett., 43, 6571–6578,
https://doi.org/10.1002/2016GL069079, 2016.
Thorpe, A. K., Frankenberg, C., and Roberts, D. A.: Retrieval techniques for airborne imaging of methane concentrations using high spatial and moderate spectral resolution: application to AVIRIS, Atmos. Meas. Tech., 7, 491–506, https://doi.org/10.5194/amt-7-491-2014, 2014.
Varon, D. J., Jacob, D. J., McKeever, J., Jervis, D., Durak, B. O. A., Xia, Y., and Huang, Y.: Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes, Atmos. Meas. Tech., 11, 5673–5686, https://doi.org/10.5194/amt-11-5673-2018, 2018.
Varon, D. J., McKeever, J., Jervis, D., Maasakkers, J. D., Pandey, S.,
Houweling, S., Aben, I., Scarpelli, T., and Jacob, D. J.: Satellite discovery
of anomalously large methane point sources from oil/gas production, Geophys.
Res. Lett., 46, 13507–13516, https://doi.org/10.1029/2019GL083798, 2019.
Varon, D. J., Jacob, D. J., Jervis, D., and Mckeever, J.: Quantifying
Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat
– D Satellite Observations, Environ. Sci. Technol., 54, 10246–10253, https://doi.org/10.1021/acs.est.0c01213, 2020.
Varon, D. J., Jervis, D., McKeever, J., Spence, I., Gains, D., and Jacob, D. J.: High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations, Atmos. Meas. Tech., 14, 2771–2785, https://doi.org/10.5194/amt-14-2771-2021, 2021.
Short summary
This study seeks to present the as-yet-unknown potential use of WorldView-3 for the mapping of methane point source emissions. The proposed retrieval methodology is based on the idea that the spectral channels not affected by methane can be used to predict the methane-affected band through regression analysis. The results show the precise location of 26 independent point emissions over different methane hotspot regions worldwide, which prove the game-changing potential that this mission entails.
This study seeks to present the as-yet-unknown potential use of WorldView-3 for the mapping of...
