Aerosol-Calibrated Matched Filter Method for Retrievals of Methane Point Source Emissions Over the Los Angeles Basin

IF 2.9 3区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS Earth and Space Science Pub Date : 2024-08-05 DOI:10.1029/2024EA003519
Chenxi Feng, Sihe Chen, Zhao-Cheng Zeng, Yangcheng Luo, Vijay Natraj, Yuk L. Yung
{"title":"Aerosol-Calibrated Matched Filter Method for Retrievals of Methane Point Source Emissions Over the Los Angeles Basin","authors":"Chenxi Feng,&nbsp;Sihe Chen,&nbsp;Zhao-Cheng Zeng,&nbsp;Yangcheng Luo,&nbsp;Vijay Natraj,&nbsp;Yuk L. Yung","doi":"10.1029/2024EA003519","DOIUrl":null,"url":null,"abstract":"<p>Methane, with a global warming potential roughly 86 times greater than carbon dioxide over a 20-year timeframe, plays a crucial role in global warming. Remote sensing retrieval is a pivotal methodology for identifying methane emission sources, with accuracy influenced largely by surface and atmospheric properties, including aerosols. In this study, we propose an Aerosol-Calibrated Matched Filter (ACMF) algorithm to improve the traditional Matched Filter (MF) method. Our new approach incorporates an aerosol scattering correction factor to reduce the aerosol-induced bias on methane retrievals. Validating our algorithm through simulated spectra, we demonstrate that considering the aerosol scattering effect significantly reduces retrieval errors compared to MF methods by an average of approximately 90%. We apply our newly developed algorithm to hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer—Next Generation in the Los Angeles Basin and focus on 11 plumes identified through case studies. Our results reveal that ACMF estimates of emission rates and inversion uncertainties exhibit an average reduction of approximately 4% compared to corresponding MF results, with deviation increasing with aerosol optical depth (AOD).</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003519","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth and Space Science","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024EA003519","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0

Abstract

Methane, with a global warming potential roughly 86 times greater than carbon dioxide over a 20-year timeframe, plays a crucial role in global warming. Remote sensing retrieval is a pivotal methodology for identifying methane emission sources, with accuracy influenced largely by surface and atmospheric properties, including aerosols. In this study, we propose an Aerosol-Calibrated Matched Filter (ACMF) algorithm to improve the traditional Matched Filter (MF) method. Our new approach incorporates an aerosol scattering correction factor to reduce the aerosol-induced bias on methane retrievals. Validating our algorithm through simulated spectra, we demonstrate that considering the aerosol scattering effect significantly reduces retrieval errors compared to MF methods by an average of approximately 90%. We apply our newly developed algorithm to hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer—Next Generation in the Los Angeles Basin and focus on 11 plumes identified through case studies. Our results reveal that ACMF estimates of emission rates and inversion uncertainties exhibit an average reduction of approximately 4% compared to corresponding MF results, with deviation increasing with aerosol optical depth (AOD).

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
气溶胶校准匹配滤波法用于检索洛杉矶盆地上空的甲烷点源排放量
甲烷的全球变暖潜能值在 20 年内大约是二氧化碳的 86 倍,在全球变暖中起着至关重要的作用。遥感检索是确定甲烷排放源的关键方法,其准确性在很大程度上受地表和大气特性(包括气溶胶)的影响。在这项研究中,我们提出了一种气溶胶校准匹配滤波(ACMF)算法,以改进传统的匹配滤波(MF)方法。我们的新方法纳入了气溶胶散射校正因子,以减少气溶胶引起的甲烷检索偏差。我们通过模拟光谱验证了我们的算法,结果表明,与 MF 方法相比,考虑气溶胶散射效应可显著降低检索误差,平均降低约 90%。我们将新开发的算法应用于下一代机载可见光/红外成像光谱仪在洛杉矶盆地获得的高光谱数据,并重点关注通过案例研究确定的 11 个羽流。我们的结果表明,与相应的 MF 结果相比,ACMF 估算的排放率和反演不确定性平均降低了约 4%,偏差随气溶胶光学深度(AOD)的增加而增大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Earth and Space Science
Earth and Space Science Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
5.50
自引率
3.20%
发文量
285
审稿时长
19 weeks
期刊介绍: Marking AGU’s second new open access journal in the last 12 months, Earth and Space Science is the only journal that reflects the expansive range of science represented by AGU’s 62,000 members, including all of the Earth, planetary, and space sciences, and related fields in environmental science, geoengineering, space engineering, and biogeochemistry.
期刊最新文献
Reconstruction of Nearshore Surface Gravity Wave Heights From Distributed Acoustic Sensing Data The Self-Calibrating Tilt Accelerometer: A Method for Observing Tilt and Correcting Drift With a Triaxial Accelerometer A Rare Tropical Cyclone Associated With Southwest Monsoon Over the Northern Part of the South China Sea—Tropical Storm Maliksi Improving Interpolating Accuracy of Weighted Mean Temperature by Using a Novel Lapse Rate Model in Compact VMF1 Product A Scalable, Cloud-Based Workflow for Spectrally-Attributed ICESat-2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1