利用高分五号AHSI载荷的高分辨率XCH<sub>4</sub>异常探测方法研究

煤矿开采是最重要的甲烷排放源, 然而其排放清单的准确性很低, 一个关键的原因在于缺乏精准识别和定位该类排放源的能力。近年来, 前沿研究表明可以利用卫星高光谱数据反演高分辨率的甲烷异常, 从而帮助识别排放源。但是, 在地表类型复杂地区该算法会完全失效。针对这一问题, 率先提出一种基于 L1 重加权和迭代收缩阈值算法 (ISTA) 匹配滤波器的算法。利用高分五号 (GF-5) 数据在山西地区的实验表明, 该方法性能显著优于现有的其他方法。实验中, 本方法识别出 23 个甲烷强点源, 这些点源全部位于 TROPOMI 的甲烷高值区内, 且高分辨遥感影像显示这些点源处存在典型的煤矿开采设施。该方法的提出为利用 GF-5 卫星数据在世界范围实现甲烷点源排查奠定了技术基础。

Abstract

Coal mining is the most important methane emission source, yet a key reason for the low accuracy of its emission inventories is the lack of capability to accurately identify and locate this type of emission source. In recent years, cutting-edge research has shown that it is possible to use satellite hyperspectral data to invert high-resolution methane anomalies and thus help to identify emission sources. However, this algorithm will fail completely inareas with complex surface types. To address this problem, the paper proposes the L1 reweighted iterative shrinkage thresholding algorithm (ISTA) matched filter algorithm for the first time. Experiments in Shanxi region using GF-5 advanced hyperspectral imager (AHSI) data show that the performance of the modified method is significantly better than that of the other existing methods. In the experiments, this method identifies 23 strong methane point sources, all of which are located in the methane high value area of TROPOMI, and the high-resolution remote sensing images also show the presence of typical coal mining facilities at these point sources. This method has laid a technical foundation for the worldwide implementation of methane point source identification using GF-5 AHSI data.

参考文献

[1] Wang W, Gao Q, Qin H, et al . The study on greenhouse effect, emission quantification and control of methane [J]. Urban Gas , 2020, (4): 4-9.

[2] Schwietzke S, Sherwood O A, Bruhwiler L M P, et al . Upward revision of global fossil fuel methane emissions based on isotope database [J]. Nature , 2016, 538(7623): 88-91.

[3] Zhang Y Z, Gautam R, Pandey S, et al . Quantifying methane emissions from the largest oil-producing basin in the United States from space [J]. Science Advances , 2020, 6(17): eaaz5120.

[4] 李丽旻. 国际能源署：全球甲烷排放监控力度不足 [N]. 中国能源报, 2022-02-28(005).

[5] Jacob D J, Turner A J, Maasakkers J D, et al . Satellite observations of atmospheric methane and their value for quantifying methane emissions [J]. Atmospheric Chemistry and Physics , 2016, 16(22): 14371-14396.

[6] Kuze A, Suto H, Nakajima M, et al . Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring [J]. Applied Optics , 2009, 48(35): 6716-6733.

[7] de Gouw J A, Veefkind J P, Roosenbrand E, et al . Daily satellite observations of methane from oil and gas production regions in the United States [J]. Scientific Reports , 2020, 10(1): 1379.

[8] Liu L Y, Chen L F, Liu Y, et al . Satellite remote sensing for global stocktaking: Methods, progress and perspectives [J]. National Remote Sensing Bulletin , 2022, 26(2): 243-267.

[9] Bradley E S, Leifer I, Roberts D A, et al . Detection of marine methane emissions with AVIRIS band ratios [J]. Geophysical Research Letters , 2011, 38(10): L10702.

[10] Thorpe A K, Roberts D A, Bradley E S, et al . High resolution mapping of methane emissions from marine and terrestrial sources using a cluster-tuned matched filter technique and imaging spectrometry [J]. Remote Sensing of Environment , 2013, 134: 305-318.

[11] Thorpe A K, Frankenberg C, Aubrey A D, et al . Mapping methane concentrations from a controlled release experiment using the next generation airborne visible/infrared imaging spectrometer (AVIRIS-NG) [J]. Remote Sensing of Environment , 2016, 179: 104-115.

[12] Guanter L, Irakulis-Loitxate I, Gorroo J, et al . Mapping methane point emissions with the PRISMA spaceborne imaging spectrometer [J]. Remote Sensing of Environment , 2021, 265: 112671.

[13] Varon D J, McKeever J, Jervis D, et al . Satellite discovery of anomalously large methane point sources from oil/gas production [J]. Geophysical Research Letters , 2019, 46(22): 13507-13516.

[14] Frankenberg C, Thorpe A K, Thompson D R, et al . Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region [J]. Proceedings of the National Academy of Sciences of the United States of America , 2016, 113(35): 9734-9739.

[15] Yue Q, Zhang G J, Wang Z. Preliminary estimation of methane emission and its distribution in China [J]. Geographical Research , 2012, 31(9): 1559-1570.

[16] Liu Y N, Sun D X, Hu X N, et al . Development of visible and short-wave infrared hyperspectral imager onboard GF-5 satellite [J]. Journal of Remote Sensing , 2020, 24(4): 333-344.

[17] Foote M D, Dennison P E, Thorpe A K, et al . Fast and accurate retrieval of methane concentration from imaging spectrometer data using sparsity prior [J]. IEEE Transactions on Geoscience and Remote Sensing , 2020, 58(9): 6480-6492.

[18] Scafutto R D M, van der Werff H, Bakker W H, et al . An evaluation of airborne SWIR imaging spectrometers for CH 4 mapping: implications of band positioning, spectral sampling and noise [J]. International Journal of Applied Earth Observationand Geoinformation , 2021, 94: 102233.

[19] Funk C C, Theiler J, Roberts D A, et al . Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery [J]. IEEE Transactions on Geoscience and Remote Sensing , 2001, 39(7): 1410-1420.

杨可意, 韩舸, 毛慧琴, 董燕妮, 马昕, 李四维, 龚威. 利用高分五号AHSI载荷的高分辨率XCH₄异常探测方法研究[J]. 大气与环境光学学报, 2022, 17(6): 670. YANG Keyi, HAN Ge, MAO Huiqin, DONG Yanni, MA Xin, LI Siwei, GONG Wei. High-resolution XCH₄ anomaly detection method using GF-5 AHSI payload[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(6): 670.

利用高分五号AHSI载荷的高分辨率XCH₄异常探测方法研究

关于本站 Cookie 的使用提示

全站搜索

利用高分五号AHSI载荷的高分辨率XCH4异常探测方法研究

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索

利用高分五号AHSI载荷的高分辨率XCH₄异常探测方法研究