Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6755-2020
© Author(s) 2020. 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-13-6755-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Dec 2020
Research article |
| 15 Dec 2020
| 15 Dec 2020
Quantifying the impact of aerosol scattering on the retrieval of methane from airborne remote sensing measurements
Yunxia Huang
School of Science, Nantong University, Nantong, 226007, China
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Zhao-Cheng Zeng
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Joint Institute for Regional Earth System Science and Engineering,
University of California, Los Angeles, CA 90095, USA
Pushkar Kopparla
Graduate School of Frontier Sciences, The University of Tokyo,
Kashiwa, Chiba 277-0882, Japan
Yuk L. Yung
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
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Cited
14 citations as recorded by crossref.
- High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations D. Varon et al. 10.5194/amt-14-2771-2021
- Influences of aerosols and thin cirrus clouds on GOSAT XCO2 and XCH4 using Total Carbon Column Observing Network, sky radiometer, and lidar data T. Trieu et al. 10.1080/01431161.2022.2038395
- Earth as a Proxy Exoplanet: Simulating DSCOVR/EPIC Observations Using the Earth Spectrum Simulator L. Gu et al. 10.3847/1538-3881/ac5e2e
- 煤炭行业甲烷排放卫星遥感研究进展与展望 秦. Qin Kai et al. 10.3788/AOS231293
- Atmospheric Methane Retrieval Based on Back Propagation Neural Network and Simulated AVIRIS-NG Data Y. Huang et al. 10.1109/LGRS.2024.3379119
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
- Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method A. del Águila & D. Efremenko 10.3390/rs13030434
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. 10.1109/JSTARS.2024.3482278
- A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B Z. He et al. 10.5194/amt-17-2937-2024
- Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution S. Zhu et al. 10.1007/s00376-022-1434-6
- Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants E. Kimei et al. 10.3390/su16052219
- The GHGSat-D imaging spectrometer D. Jervis et al. 10.5194/amt-14-2127-2021
- Accounting for the effect of aerosols in GHGSat methane retrieval Q. Yu et al. 10.5194/amt-17-3347-2024
- Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region W. Hu et al. 10.1007/s40789-024-00700-1
14 citations as recorded by crossref.
- High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations D. Varon et al. 10.5194/amt-14-2771-2021
- Influences of aerosols and thin cirrus clouds on GOSAT XCO2 and XCH4 using Total Carbon Column Observing Network, sky radiometer, and lidar data T. Trieu et al. 10.1080/01431161.2022.2038395
- Earth as a Proxy Exoplanet: Simulating DSCOVR/EPIC Observations Using the Earth Spectrum Simulator L. Gu et al. 10.3847/1538-3881/ac5e2e
- 煤炭行业甲烷排放卫星遥感研究进展与展望 秦. Qin Kai et al. 10.3788/AOS231293
- Atmospheric Methane Retrieval Based on Back Propagation Neural Network and Simulated AVIRIS-NG Data Y. Huang et al. 10.1109/LGRS.2024.3379119
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
- Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method A. del Águila & D. Efremenko 10.3390/rs13030434
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. 10.1109/JSTARS.2024.3482278
- A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B Z. He et al. 10.5194/amt-17-2937-2024
- Decadal Methane Emission Trend Inferred from Proxy GOSAT XCH4 Retrievals: Impacts of Transport Model Spatial Resolution S. Zhu et al. 10.1007/s00376-022-1434-6
- Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants E. Kimei et al. 10.3390/su16052219
- The GHGSat-D imaging spectrometer D. Jervis et al. 10.5194/amt-14-2127-2021
- Accounting for the effect of aerosols in GHGSat methane retrieval Q. Yu et al. 10.5194/amt-17-3347-2024
- Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region W. Hu et al. 10.1007/s40789-024-00700-1
Latest update: 13 Dec 2024
Short summary
As a greenhouse gas with strong global warming potential, atmospheric methane emissions have attracted a great deal of attention. However, accurate assessment of these emissions is challenging in the presence of atmospheric particulates called aerosols. We quantify the aerosol impact on methane quantification from airborne measurements using two techniques, one that has traditionally been used by the imaging spectroscopy community and the other commonly employed in trace gas remote sensing.
As a greenhouse gas with strong global warming potential, atmospheric methane emissions have...
