水稻甲烷排放遥感监测研究综述

刘勇丽; 赵晨尧; 刘希平; 王众; 李隆章; 陈仁淦; 陈水森; 曹弘毅

doi:10.12153/j.issn.1674-991X.20240285

水稻甲烷排放遥感监测研究综述

doi: 10.12153/j.issn.1674-991X.20240285

刘勇丽^{1, 2,},
赵晨尧^{3, 4, ,},
刘希平¹,
王众¹,
李隆章³,
陈仁淦⁴,
陈水森^{3, 4},
曹弘毅¹

1.
生态环境部土壤与农业农村生态环境监管技术中心
2.
土壤中心嘉善双碳创新研究院
3.
广东省遥感大数据应用工程技术研究中心，广东省遥感与地理信息系统应用实验室，广东省地理空间信息技术与应用公共实验室，广东省科学院广州地理研究所
4.
低碳数字监测联合实验室，广东碳中和研究院（韶关）

基金项目: 生态环境部土壤与农业农村生态环境监管技术中心项目（双碳课题-2023-08，双碳课题-2023-13）；广东省科技计划项目（2023A1414020039）

详细信息

作者简介:
刘勇丽（1989—），女，高级工程师，主要从事农业农村碳资产核算研究，252895055@qq.com

通讯作者:
赵晨尧（1997—），男，硕士研究生，主要从事农林碳汇研究，zhaochenyao21@mails.ucas.ac.cn

中图分类号: X831
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出版历程
- 收稿日期: 2024-05-06

A review of research on remote sensing monitoring of rice methane emissions

LIU Yongli^{1, 2
,},
ZHAO Chenyao^{3, 4
, ,},
LIU Xiping¹,
WANG Zhong¹,
LI Longzhang³,
CHEN Rengan⁴,
CHEN Shuisen^{3, 4},
CAO Hongyi¹

1.
Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment
2.
Innovation Institute of Carbon Peaking and Carbon Neutrality, TCARE & Jiashan
3.
Guangdong Remote Sensing Big Data application Engineering Technology Research Center, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences
4.
Joint Laboratory on Low-Carbon Digital Monitoring, Guangdong Institute of Carbon Neutrality (Shaoguan)

摘要

摘要:
水稻甲烷排放是农业甲烷排放的重要来源，及时准确地估算水稻甲烷排放可为政策制定提供参考。通过概念辨析和文献调研的方法，综述了水稻甲烷排放遥感监测的数据来源、方法、不确定性，论述了水稻甲烷遥感监测技术的发展现状和未来展望。结果表明，遥感技术在水稻甲烷排放监测方面具有较大潜力，不仅能够通过自上而下的方法直接监测水稻甲烷排放情况，也能结合自下而上的方法实现水稻甲烷排放的间接估算。但如何提高自上而下和自下而上方法的准确性，缩小2类方法间的差异是亟需解决的关键问题。未来，新的遥感技术和性能更优越的传感器可为准确估算水稻甲烷排放提供更多保障；多源遥感数据融合以及自上而下和自下而上方法之间的结合是定量水稻甲烷排放遥感监测不确定性的重要研究方向。
- 水稻甲烷 /
- 遥感 /
- 不确定性 /
- 全球变暖
Abstract:
Rice methane emissions are an important source of agricultural methane emissions, and timely and accurate estimation of rice methane emissions can provide valuable information for policymakers. The data sources, methods, and uncertainties of remote sensing monitoring of rice methane emissions, as well as its current status of development and future outlook, were summarized by means of conceptual analysis and literature research. The results show that remote sensing technology is largely promising for rice methane emission monitoring. It can not only directly monitor rice methane emissions through top-down methods but also indirectly estimate rice methane emissions by combining them with bottom-up methods. However, how to improve the accuracy of top-down and bottom-up methods and narrow the differences between the two types of methods is the key issue that needs to be addressed. In the future, new remote sensing technologies and sensors with better performance can provide additional assurance for accurate estimation of rice methane emissions. The fusion of remote sensing data from multiple sources and the combination of top-down and bottom-up methods are important research directions for quantifying the uncertainty of remote sensing monitoring of rice methane emissions.
- rice methane emission /
- remote sensing /
- uncertainty /
- global warming

HTML全文

图 1 遥感平台类型

Figure 1. Types of remote sensing platforms

下载: 全尺寸图片幻灯片

图 2 自上而下的测量水稻甲烷排放的卫星方法

Figure 2. Satellite methods for top-down measurement of methane emissions from rice

下载: 全尺寸图片幻灯片

表 1 监测甲烷的卫星传感器

Table 1. Satellite sensors for monitoring methane

传感器类型	名称	波段	光谱分辨率/nm	空间分辨率/km²	精度/%	国家或地区
大气传感器	SCIAMACHY	SWIR	0.4、1.4、0.2	30×60	1.5	欧盟
	GOSAT	SWIR	0.02、0.06	10×10	0.7	日本
	GHGSat	SWIR	0.3	0.03×0.03	1.5	加拿大
	TROPOMI	SWIR	0.25	7×7	0.8	欧盟
	MethaneSat	TIR	0.3	0.13×0.40	0.1~0.2	美国
多光谱成像仪	Landsat-8/OLI	TIR/SWIR	200	0.03×0.03	30~90	美国
	Sentinel-2/MSI	TIR/SWIR	200	0.02×0.02	30~90	欧盟
	Sentinel-3	TIR/SWIR	50	0.5×0.5		欧盟
	WorldView-3	TIR/SWIR	50	0.004×0.004	6~19	美国
高光谱成像仪	PRISMA/HYC	SWIR	10	0.03×0.03	3~9	意大利
	EnMap/HSI	SWIR	10	0.03×0.03	3~9	德国
	EMIT	SWIR	9	0.06×0.06	2~9	美国
	GF-5/AHSI	SWIR	8.5	0.03×0.03	3.3	中国
	ZY-1/AHSI	SWIR	16.8	0.03×0.03	5	中国
	FY-3D/GAS	SWIR	0.07、0.14	10×10		中国
	FY-3H/GAS-2	SWIR	0.07、0.10	3×3		中国
激光雷达	MERLIN		3×10⁻⁴	0.1×50	1.5	法国、德国

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表 2 地面甲烷固定观测站点网络

Table 2. Network of ground-based methane fixed observation sites

名称	光谱分辨率/nm	覆盖范围
TCCON^[46]	0.002	全球（30个站点）
NDACC^[47]	0.002	全球（118个站点）
ICOS^[48]	0.002	欧洲（39个站点）
注：TCCON为全球总碳柱观测网络，NDACC为全球大气成分变化探测网络，ICOS为综合碳观测系统。

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表 3 自下而上的测量水稻甲烷排放的方法

Table 3. Bottom-up methods for measuring methane emissions from rice

名称		原理	优点	缺点
排放因子法1^[52]		根据IPCC提供的水稻甲烷排放因子参考值，计算水稻甲烷排放量	可采用IPCC指南提供的默认参数，计算简单	误差较大，只适用于水稻种植较少的国家/地区；无法空间化
排放因子法2^[52]		同排放因子法1，但需自行计算当地水稻甲烷排放因子	相较于排放因子法1计算结果更加准确	需要额外试验获得当地排放因子；无法空间化
遥感排放因子法^[53]		将遥感水稻快速制图和植被指数与排放因子法相结合，获取水稻甲烷排放空间分布情况	可在像元尺度进行水稻甲烷排放估算，实现空间化；并可通过植被指数修订排放因子，提高精度	非常依赖高精度的水稻制图数据和高质量的植被指数数据
模型模拟法	CH₄MOD模型^[54]	基于水稻甲烷产生、氧化和传输过程，考虑水稻生长、土壤环境和管理措施等因素进行水稻甲烷估算	模型结构相对简单，输入参数较少且易于获得；考虑了管理制度对水稻甲烷排放的影响；可使用遥感数据作为特定输入参数，提高估算精度	由于模型简化了许多复杂的过程和参数，导致估算精度下降；在复杂的气候和土壤条件下，模型表现不佳
	DNDC 模型^[55]	基于一系列生物地球化学过程，将生态驱动因子、环境因子和相应的物理化学过程相结合估算水稻甲烷排放	机理明确,考虑了更全面的生态系统过程和参数，精度较高，适用范围广；可模拟多种管理模式下的水稻甲烷排放情况；可使用遥感数据作为特定输入参数，提高估算精度	模型较为复杂，需要较多的数据和参数，对数据质量和完整性要求较高
	MERES模型^[56]	将气候、土壤性质、种植模式、施肥等空间信息与甲烷通量的机理模型相结合估算水稻甲烷排放	可评估不同管理措施和气候条件下的甲烷排放，可使用遥感数据作为特定输入参数，提高估算精度	数据需求量大，获取难度高；不适用于短期水稻甲烷排放估算

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表 4 大气甲烷浓度反演方法

Table 4. Atmospheric methane concentration inversion method

名称		原理	优点	缺点
经验公式法		基于历史数据和观察结果来建立模型	易于实施，精度高	不易推广
物理算法	DOAS算法^[65]	通过拟合低阶多项式，消除散射造成的波长变化	对光谱分辨率的要求不高，计算相对容易	对光谱定标精度和气象因素的测量精度要求较高，受云雾影响较大
	Proxy算法^[66]	基于大气中甲烷和二氧化碳浓度（或其他大气成分）之间的强相关性	简化了监测过程，可以利用现有的设备和数据	精度与大气成分高度相关，无法单独提供甲烷的详细排放源信息
	PPDF算法^[67]	通过校正光程来减少薄卷云与气溶胶所造成的反演误差	所需参数少，效率高	天气和地表反射的影响很大
	全物理算法^[68]	基于辐射传输模型反演大气甲烷浓度	解释性和预测性强，能够全面模拟甲烷的传输和变化	复杂的参数化，对初值和边界条件敏感，计算量大
人工智能算法^[69]		通过人工智能算法优化物理算法	计算速度快，精确性好	对光谱分辨率的要求相对较高，需要大量数据进行训练，对未知的情况缺少处理能力

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参考文献(84)

[1]	SHI Y F, LOU Y S, ZHANG Z, et al. Estimation of methane emissions based on crop yield and remote sensing data in a paddy field[J]. Greenhouse Gases: Science and Technology,2020,10(1):196-207. doi: 10.1002/ghg.1946
[2]	SAUNOIS M, STAVERT A, POULTER B, et al. The global methane budget 2000-2017[J]. Earth System Science Data,2020,12(3):1561-623. doi: 10.5194/essd-12-1561-2020
[3]	STOCKER T. Climate Change 2013: the physical science basis: working group Ⅰ contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change[M]. Cambridge: Cambridge University Press, 2014.
[4]	ASELMANN I, CRUTZEN P J. Global distribution of natural freshwater wetlands and rice paddies, their net primary productivity, seasonality and possible methane emissions[J]. Journal of Atmospheric Chemistry,1989,8(4):307-358. doi: 10.1007/BF00052709
[5]	CHEN Y H, PRINN R G. Estimation of atmospheric methane emissions between 1996 and 2001 using a three-dimensional global chemical transport model[J]. Journal of Geophysical Research: Atmospheres,2006,111(D10):D10307.
[6]	FRANKENBERG C, MEIRINK J F, van WEELE M, et al. Assessing methane emissions from global space-borne observations[J]. Science,2005,308(5724):1010-1014. doi: 10.1126/science.1106644
[7]	YAN X Y, AKIYAMA H, YAGI K, et al. Global estimations of the inventory and mitigation potential of methane emissions from rice cultivation conducted using the 2006 Intergovernmental Panel on Climate Change Guidelines[J]. Global Biogeochemical Cycles,2009.doi: 10.1029/2008GB003299.
[8]	BERGAMASCHI P, FRANKENBERG C, MEIRINK J F, et al. Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: 2. evaluation based on inverse model simulations[J]. Journal of Geophysical Research: Atmospheres,2007,112(D2):D02304.
[9]	BLOOM A A, PALMER P I, FRASER A, et al. Large-scale controls of methanogenesis inferred from methane and gravity spaceborne data[J]. Science,2010,327:322-325. doi: 10.1126/science.1175176
[10]	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. doi: 10.5194/acp-16-14371-2016
[11]	ZHANG B W, TIAN H Q, REN W, et al. Methane emissions from global rice fields: magnitude, spatiotemporal patterns, and environmental controls[J]. Global Biogeochemical Cycles,2016,30(9):1246-1263. doi: 10.1002/2016GB005381
[12]	LI M, ZHANG Q, KUROKAWA J I, et al. MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP[J]. Atmospheric Chemistry and Physics,2017,17(2):935-963. doi: 10.5194/acp-17-935-2017
[13]	VARON D J, JACOB D J, McKEEVER J, et al. Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes[J]. Atmospheric Measurement Techniques,2018,11(10):5673-5686. doi: 10.5194/amt-11-5673-2018
[14]	SHENG J X, SONG S J, ZHANG Y Z, et al. Bottom-up estimates of coal mine methane emissions in China: a gridded inventory, emission factors, and trends[J]. Environmental Science & Technology Letters,2019,6(8):473-478.
[15]	VAUGHN T L, BELL C S, PICKERING C K, et al. Temporal variability largely explains top-down/bottom-up difference in methane emission estimates from a natural gas production region[J]. Proceedings of the National Academy of Sciences of the United States of America,2018,115(46):11712-11717.
[16]	FAIRLEY D, FISCHER M L. Top-down methane emissions estimates for the San Francisco Bay Area from 1990 to 2012[J]. Atmospheric Environment,2015,107:9-15. doi: 10.1016/j.atmosenv.2015.01.065
[17]	GUHA A, NEWMAN S, FAIRLEY D, et al. Assessment of regional methane emission inventories through airborne quantification in the San francisco bay area[J]. Environmental Science & Technology,2020,54(15):9254-9264.
[18]	LAUVAUX T, GIRON C, MAZZOLINI M, et al. Global assessment of oil and gas methane ultra-emitters[J]. Science,2022,375(6580):557-561. doi: 10.1126/science.abj4351
[19]	GNYP M L, MIAO Y X, YUAN F, et al. Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages[J]. Field Crops Research,2014,155:42-55. doi: 10.1016/j.fcr.2013.09.023
[20]	LI X L, MA J, YAO Y J, et al. Methane and nitrous oxide emissions from irrigated lowland rice paddies after wheat straw application and midseason aeration[J]. Nutrient Cycling in Agroecosystems,2014,100(1):65-76. doi: 10.1007/s10705-014-9627-8
[21]	FU C, YU G R. Estimation and spatiotemporal analysis of methane emissions from agriculture in China[J]. Environmental Management,2010,46(4):618-632. doi: 10.1007/s00267-010-9495-1
[22]	NAVALGUND R, JAYARAMAN V, ROY P S. Remote sensing applications: an overview[J]. Current Science,2007,93:1747-1766.
[23]	刘双慧, 李小英, 曹西凤, 等. 大气甲烷探测进展与全球甲烷分布分析[J]. 遥感技术与应用,2022,37(2):436-450. LIU S H, LI X Y, CAO X F, et al. Development of atmospheric methane observation and distribution of global methane[J]. Remote Sensing Technology and Application,2022,37(2):436-450.
[24]	JACOB D J, VARON D J, CUSWORTH D H, et al. Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane[J]. Atmospheric Chemistry and Physics,2022,22(14):9617-9646. doi: 10.5194/acp-22-9617-2022
[25]	何卓, 李正强, 樊程, 等. 大气甲烷卫星传感器和遥感算法研究综述[J]. 光学学报,2023,43(18):3788/AOS230429.
[26]	AYASSE A K, DENNISON P E, FOOTE M, et al. Methane mapping with future satellite imaging spectrometers[J]. Remote Sensing,2019,11(24):3054. doi: 10.3390/rs11243054
[27]	CUSWORTH D H, JACOB D J, VARON D J, et al. Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space[J]. Atmospheric Measurement Techniques,2019,12(10):5655-5668. doi: 10.5194/amt-12-5655-2019
[28]	SHENG J X, JACOB D J, MAASAKKERS J D, et al. Comparative analysis of low-Earth orbit (TROPOMI) and geostationary (GeoCARB, GEO-CAPE) satellite instruments for constraining methane emissions on fine regional scales: application to the Southeast US[J]. Atmospheric Measurement Techniques,2018,11(12):6379-6388. doi: 10.5194/amt-11-6379-2018
[29]	WORDEN J R, TURNER A J, BLOOM A, et al. Quantifying lower tropospheric methane concentrations using GOSAT near-IR and TES thermal IR measurements[J]. Atmospheric Measurement Techniques,2015,8(8):3433-3445. doi: 10.5194/amt-8-3433-2015
[30]	WÜHRER C, KÜHL C, LUCARELLI S, et al. MERLIN: overview of the design status of the lidarinstrument[C]//International Conference on Space Optics: ICSO 2018. Chania: SPIE, 2019: 839-851.
[31]	KUENZER C, KNAUER K. Remote sensing of rice crop areas[J]. International Journal of Remote Sensing,2013,34(6):2101-2139. doi: 10.1080/01431161.2012.738946
[32]	ZHU A X, ZHAO F H, PAN H B, et al. Mapping rice paddy distribution using remote sensing by coupling deep learning with phenological characteristics[J]. Remote Sensing,2021,13(7):1360. doi: 10.3390/rs13071360
[33]	KEPPLER F, HAMILTON J T G, BRASS M, et al. Methane emissions from terrestrial plants under aerobic conditions[J]. Nature,2006,439:187-191. doi: 10.1038/nature04420
[34]	GONG S Y, SHI Y S. Evaluation of comprehensive monthly-gridded methane emissions from natural and anthropogenic sources in China[J]. Science of the Total Environment,2021,784:147116. doi: 10.1016/j.scitotenv.2021.147116
[35]	ZHANG Y, WANG C Z, WU J P, et al. Mapping paddy rice with multitemporal ALOS/PALSAR imagery in Southeast China[J]. International Journal of Remote Sensing,2009,30(23):6301-6315. doi: 10.1080/01431160902842391
[36]	SUN Y W, LI Z L, LUO J C, et al. Farmland parcel-based crop classification in cloudy/rainy mountains using Sentinel-1 and Sentinel-2 based deep learning[J]. International Journal of Remote Sensing,2022,43(3):1054-1073. doi: 10.1080/01431161.2022.2032458
[37]	OMIA E, BAE H, PARK E, et al. Remote sensing in field crop monitoring: a comprehensive review of sensor systems, data analyses and recent advances[J]. Remote Sensing,2023,15(2):354. doi: 10.3390/rs15020354
[38]	SINGH A, SINGH A K, RAWAT S, et al. Satellite-based quantification of methane emissions from wetlands and rice paddies ecosystems in north and northeast India[J]. Hydrobiology,2022,1(3):317-330. doi: 10.3390/hydrobiology1030023
[39]	AYASSE A K, THORPE A K, CUSWORTH D H, et al. Methane remote sensing and emission quantification of offshore shallow water oil and gas platforms in the Gulf of Mexico[J]. Environmental Research Letters,2022,17(8):084039. doi: 10.1088/1748-9326/ac8566
[40]	DUREN R M, THORPE A K, FOSTER K T, et al. California's methane super-emitters[J]. Nature,2019,575:180-184. doi: 10.1038/s41586-019-1720-3
[41]	JOHNSON M R, TYNER D R, SZEKERES A J. Blinded evaluation of airborne methane source detection using Bridger Photonics LiDAR[J]. Remote Sensing of Environment,2021,259:112418. doi: 10.1016/j.rse.2021.112418
[42]	TYNER D R, JOHNSON M R. Where the methane is-insights from novel airborne LiDAR measurements combined with ground survey data[J]. Environmental Science & Technology,2021,55(14):9773-9783.
[43]	RUPPERT L, BARTHOLOMEW J, LYMAN P, et al. Wide area methane emissions mapping with airborne IPDA lidar[C]//Lidar Remote Sensing for Environmental Monitoring 2017. San Diego: SPIE, 2017: 34-47.
[44]	GANESAN A L, SCHWIETZKE S, POULTER B, et al. Advancing scientific understanding of the global methane budget in support of the Paris agreement[J]. Global Biogeochemical Cycles,2019,33(12):1475-1512. doi: 10.1029/2018GB006065
[45]	WONG C K, PONGETTI T J, ODA T, et al. Monthly trends of methane emissions in Los Angeles from 2011 to 2015 inferred by CLARS-FTS observations[J]. Atmospheric Chemistry and Physics,2016,16(20):13121-13130. doi: 10.5194/acp-16-13121-2016
[46]	MALINA E, VEIHELMANN B, BUSCHMANN M, et al. On the consistency of methane retrievals using the Total Carbon Column Observing Network (TCCON) and multiple spectroscopic databases[J]. Atmospheric Measurement Techniques,2022,15(8):2377-2406. doi: 10.5194/amt-15-2377-2022
[47]	de MAZIÈRE M, THOMPSON A M, KURYLO M J, et al. The Network for the Detection of Atmospheric Composition Change (NDACC): history, status and perspectives[J]. Atmospheric Chemistry and Physics,2018,18(7):4935-4964. doi: 10.5194/acp-18-4935-2018
[48]	HEISKANEN J, BRÜMMER C, BUCHMANN N, et al. The integrated carbon observation system in Europe[J]. Bulletin of the American Meteorological Society,2022,103(3):855-872. doi: 10.1175/BAMS-D-19-0364.1
[49]	MORIN T H, BOHRER G, STEFANIK K C, et al. Combining eddy-covariance and chamber measurements to determine the methane budget from a small, heterogeneous urban floodplain wetland park[J]. Agricultural and Forest Meteorology,2017,237:160-170.
[50]	ZHAO G Y, LIAN M, LI Y Y, et al. Mobile lidar system for environmental monitoring[J]. Applied Optics,2017,56(5):1506-1516. doi: 10.1364/AO.56.001506
[51]	WU X K, ZHANG Y, HAN Y H, et al. Advances in methane emissions from agricultural sources: Part Ⅰ. accounting and mitigation[J]. Journal of Environmental Sciences,2024,140:279-291. doi: 10.1016/j.jes.2023.08.029
[52]	IPCC. Climate change 2007: the physical science basis contribution of working group I[J]. 2007.
[53]	ZHANG Y, WANG Y Y, SU S L, et al. Quantifying methane emissions from rice paddies in Northeast China by integrating remote sensing mapping with a biogeochemical model[J]. Biogeosciences,2011,8(5):1225-1235. doi: 10.5194/bg-8-1225-2011
[54]	HUANG Y, SASS R L, FISHER F M. A semi-empirical model of methane emission from flooded rice paddy soils[J]. Global Change Biology,1998,4(3):247-268. doi: 10.1046/j.1365-2486.1998.00129.x
[55]	LI C S, FROLKING S, FROLKING T A. A model of nitrous oxide evolution from soil driven by rainfall events: 1. model structure and sensitivity[J]. Journal of Geophysical Research: Atmospheres,1992,97(D9):9759-9776. doi: 10.1029/92JD00509
[56]	MATTHEWS R B, WASSMANN R, ARAH J. Using a crop/soil simulation model and GIS techniques to assess methane emissions from rice fields in Asia: Ⅰ. model development[J]. Nutrient Cycling in Agroecosystems,2000,58(1):141-159.
[57]	刘志明, 晏明, 闫敏华, 等. 稻田甲烷排放的卫星遥感监测试验[J]. 气象科技,2002,30(5):278-281.
[58]	SUDARMANIAN N, PAZHANIVELAN S, PANNEERSELVAM S J J O A. Estimation of methane emission from paddy fields using sar and modis satellite data[J]. Journal of Agrometeorology,2019,21(1):102-109.
[59]	胡永浩, 张昆扬, 胡南燕, 等. 中国农业碳排放测算研究综述[J]. 中国生态农业学报(中英文),2023,31(2):163-176. HU Y H, ZHANG K Y, HU N Y, et al. Review on measurement of agricultural carbon emission in China[J]. Chinese Journal of Eco-Agriculture,2023,31(2):163-176.
[60]	NIKOLAISEN M, CORNULIER T, HILLIER J, et al. Methane emissions from rice paddies globally: a quantitative statistical review of controlling variables and modelling of emission factors[J]. Journal of Cleaner Production,2023,409:137245. doi: 10.1016/j.jclepro.2023.137245
[61]	陈强, 潘英姿, 蒋卫国, 等. 湿地甲烷排放估算模型的研究进展[J]. 环境工程技术学报,2012,2(1):67-75. CHEN Q, PAN Y Z, JIANG W G, et al. Advances in the research on estimation models of wetlands methane emission[J]. Journal of Environmental Engineering Technology,2012,2(1):67-75.
[62]	TORBICK N, SALAS W, CHOWDHURY D, et al. Mapping rice greenhouse gas emissions in the Red River Delta, Vietnam[J]. Carbon Management,2017,8(1):99-108. doi: 10.1080/17583004.2016.1275816
[63]	MATTHEWS R, WASSMANN R. Modelling the impacts of climate change and methane emission reductions on rice production: a review[J]. European Journal of Agronomy,2003,19(4):573-598. doi: 10.1016/S1161-0301(03)00005-4
[64]	ZHANG W, YU Y Q, HUANG Y, et al. Modeling methane emissions from irrigated rice cultivation inChina from 1960 to 2050[J]. Global Change Biology,2011,17(12):3511-3523. doi: 10.1111/j.1365-2486.2011.02495.x
[65]	STUTZ J, PLATT U. Numerical analysis and estimation of the statistical error of differential optical absorption spectroscopy measurements with least-squares methods[J]. Applied Optics,1996,35(30):6041-6053. doi: 10.1364/AO.35.006041
[66]	SCHEPERS D, GUERLET S, BUTZ A, et al. Methane retrievals from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared measurements: Performance comparison of proxy and physics retrieval algorithms[J]. Journal of Geophysical Research: Atmospheres,2012,117(D10):e2012jd017549. doi: 10.1029/2012JD017549
[67]	OSHCHEPKOV S, BRIL A, YOKOTA T. PPDF-based method to account for atmospheric light scattering in observations of carbon dioxide from space[J]. Journal of Geophysical Research: Atmospheres,2008,113(D23):e2008jd010061. doi: 10.1029/2008JD010061
[68]	LORENTE A, BORSDORFF T, MARTINEZ-VELARTE M C, et al. Evaluation of the methane full-physics retrieval applied to TROPOMI ocean Sun glint measurements[J]. Atmospheric Measurement Techniques,2022,15(22):6585-6603. doi: 10.5194/amt-15-6585-2022
[69]	TURQUETY S, HADJI-LAZARO J, CLERBAUX C, et al. Operational trace gas retrieval algorithm for the Infrared Atmospheric Sounding Interferometer[J]. Journal of Geophysical Research: Atmospheres,2004,109(D21):e2004jd004821. doi: 10.1029/2004JD004821
[70]	HUANG Y X, LIU G Z, WANG L X, et al. Atmospheric methane retrieval based on back propagation neural network and simulated AVIRIS-NG data[J]. IEEE Geoscience and Remote Sensing Letters,2024,21:1001505.
[71]	LUNT M F, PALMER P I, FENG L, et al. An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data[J]. Atmospheric Chemistry and Physics,2019,19(23):14721-14740. doi: 10.5194/acp-19-14721-2019
[72]	BORCHARDT J, GERILOWSKI K, KRAUTWURST S, et al. Detection and quantification of CH₄ plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data[J]. Atmospheric Measurement Techniques,2021,14(2):1267-1291. doi: 10.5194/amt-14-1267-2021
[73]	JONGARAMRUNGRUANG S, THORPE A K, MATHEOU G, et al. MethaNet–An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery[J]. Remote Sensing of Environment,2022,269:112809. doi: 10.1016/j.rse.2021.112809
[74]	HUANG X T, DENG Z, JIANG F, et al. Improved consistency of satellite XCO₂ retrievals based on machine learning[J]. Geophysical Research Letters,2024,51(8):e2023GL107536. doi: 10.1029/2023GL107536
[75]	JOYCE P, RUIZ VILLENA C, HUANG Y H, et al. Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images[J]. Atmospheric Measurement Techniques,2023,16(10):2627-2640. doi: 10.5194/amt-16-2627-2023
[76]	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. doi: 10.1126/sciadv.aaz5120
[77]	PLANT G, KORT E A, MURRAY L T, et al. Evaluating urban methane emissions from space using TROPOMI methane and carbon monoxide observations[J]. Remote Sensing of Environment,2022,268:112756. doi: 10.1016/j.rse.2021.112756
[78]	COLOMB V, TOUCHEMOULIN O, BOCKEL L, et al. Selection of appropriate calculators for landscape-scale greenhouse gas assessment for agriculture and forestry[J]. Environmental Research Letters,2013,8(1):015029. doi: 10.1088/1748-9326/8/1/015029
[79]	TORBICK N, CHOWDHURY D, SALAS W, et al. Monitoring rice agriculture across Myanmar using time series sentinel-1 assisted by landsat-8 and PALSAR-2[J]. Remote Sensing,2017,9(2):119. doi: 10.3390/rs9020119
[80]	EHRET T, de TRUCHIS A, MAZZOLINI M, et al. Global tracking and quantification of oil and gas methane emissions from recurrent Sentinel-2 imagery[J]. Environmental Science & Technology,2022,56(14):10517-10529.
[81]	JIANG Y H, ZHANG L, ZHANG X Y, et al. Methane retrieval algorithms based on satellite: a review[J]. Atmosphere,2024,15(4):449. doi: 10.3390/atmos15040449
[82]	ERLAND B M, THORPE A K, GAMON J A. Recent advances toward transparent methane emissions monitoring: a review[J]. Environmental Science & Technology,2022,56(23):16567-16581.
[83]	ALVAREZ R A, ZAVALA-ARAIZA D, LYON D R, et al. Assessment of methane emissions from the U. S. oil and gas supply chain[J]. Science,2018,361(6398):186-188. doi: 10.1126/science.aar7204
[84]	HAQUE M M, BISWAS J C. Emission factors and global warming potential as influenced by fertilizer management for the cultivation of rice under varied growing seasons[J]. Environmental Research,2021,197:111156. ⊕ doi: 10.1016/j.envres.2021.111156