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This study developed a random forest model to obtain a daily 5 km resolution atmospheric methane concentration dataset with full spatial coverage (100%) from 2019 to 2021 in mainland China, thereby filling the gap in the methane product data from the Tropospheric Monitoring Instrument (TROPOMI). The coefficients of determination for a sample-based and spatial-based cross-validation are 0.97 and 0.93, respectively. The average deviation of the seamless methane product reconstructed by the random forest model is less than 1%, validated with the measured methane concentration data from the Total Carbon Column Observing Network sites. Methane concentrations in China show a distribution of high in the east and south and low in the west and north. The high-concentration areas include Central China, the Sichuan Basin, the Pearl River Delta, and the Yangtze River Delta. In terms of time scale, the methane concentration has evident seasonal variation, as it is low in spring (average 1852 ppb) and winter (average 1881 ppb) and high in summer (average 1885 ppb) and autumn (average 1886 ppb). This is mainly due to the significant increase in emissions from rice cultivation and wetlands during the summer and autumn. During the COVID-19 pandemic, the methane concentration decreases significantly and then starts to return to normal around 70 days after the Lunar New Year, indicating that the seamless methane product can potentially detect anomalous changes in methane concentration.<\/jats:p>","DOI":"10.3390\/rs16142525","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T09:23:08Z","timestamp":1720603388000},"page":"2525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhili","family":"Jin","sequence":"first","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2801-7176","authenticated-orcid":false,"given":"Junchen","family":"He","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7930-9147","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/s43247-023-00969-1","article-title":"Trends in atmospheric methane concentrations since 1990 were driven and modified by anthropogenic emissions","volume":"4","author":"Skeie","year":"2023","journal-title":"Commun. Earth Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15351","DOI":"10.5194\/acp-22-15351-2022","article-title":"Estimating emissions of methane consistent with atmospheric measurements of methane and \u03b413C of methane","volume":"22","author":"Basu","year":"2022","journal-title":"Atmos. Chem. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Intergovernmental Panel on Climate Change (2014). Summary for Policymakers. Climate Change 2013\u2014The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.","DOI":"10.1017\/CBO9781107415324"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/0048-9697(90)90173-R","article-title":"The role of the CH4-CO-OH cycle in the greenhouse problem","volume":"94","author":"Rotmans","year":"1990","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14545","DOI":"10.5194\/acp-16-14545-2016","article-title":"Inventory of anthropogenic methane emissions in mainland China from 1980 to 2010","volume":"16","author":"Peng","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13033","DOI":"10.1029\/91JD01247","article-title":"Three-dimensional model synthesis of the global methane cycle","volume":"96","author":"Fung","year":"1991","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"064001","DOI":"10.1088\/1748-9326\/ad4617","article-title":"Unraveling the dynamics of atmospheric methane: The impact of anthropogenic and natural emissions","volume":"19","author":"Fu","year":"2024","journal-title":"Environ. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.5194\/essd-12-1561-2020","article-title":"The Global Methane Budget 2000\u20132017","volume":"12","author":"Saunois","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.tree.2006.05.017","article-title":"Scaling methane emissions from vegetation","volume":"21","author":"Parsons","year":"2006","journal-title":"Trends Ecol. Evol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1071\/FP06051","article-title":"Comment on the quantitative significance of aerobic methane release by plants","volume":"33","author":"Kirschbaum","year":"2006","journal-title":"Funct. Plant Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1038\/nature04420","article-title":"Methane emissions from terrestrial plants under aerobic conditions","volume":"439","author":"Keppler","year":"2006","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20200451","DOI":"10.1098\/rsta.2020.0451","article-title":"Agricultural methane emissions and the potential formitigation","volume":"379","author":"Smith","year":"2021","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1093\/plankt\/fbab069","article-title":"Phytoplankton photosynthesis: An unexplored source of biogenic methane emission from oxic environments","volume":"43","author":"Bizic","year":"2021","journal-title":"J. Plankton Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1038\/s41586-020-1991-8","article-title":"Preindustrial 14CH4 indicates greater anthropogenic fossil CH4 emissions","volume":"578","author":"Hmiel","year":"2020","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1126\/science.1106644","article-title":"Assessing Methane Emissions from Global Space-Borne Observations","volume":"308","author":"Frankenberg","year":"2005","journal-title":"Science"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1109\/TGRS.2002.808356","article-title":"AIRS\/AMSU\/HSB on the Aqua mission: Design, science objective, data products, and processing systems","volume":"41","author":"Aumann","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113","DOI":"10.5194\/acp-15-113-2015","article-title":"Inverse modelling of CH4 emissions for 2010\u20132011 using different satellite retrieval products from GOSAT and SCIAMACHY","volume":"15","author":"Alexe","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16573","DOI":"10.1021\/acs.est.1c03976","article-title":"Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations","volume":"55","author":"Sadavarte","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1038\/s41598-020-57678-4","article-title":"Daily Satellite Observations of Methane from Oil and Gas Production Regions in the United States","volume":"10","author":"Veefkind","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113652","DOI":"10.1016\/j.rse.2023.113652","article-title":"Improving quantification of methane point source emissions from imaging spectroscopy","volume":"295","author":"Pei","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"443","DOI":"10.5194\/acp-9-443-2009","article-title":"Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite\u2014Part 2: Methane","volume":"9","author":"Schneising","year":"2009","journal-title":"Atmos. Chem. Phys."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kuze, A., Kikuchi, N., Kataoka, F., Suto, H., Shiomi, K., and Kondo, Y. (2020). Detection of Methane Emission from a Local Source Using GOSAT Target Observations. Remote Sens., 12.","DOI":"10.3390\/rs12020267"},{"key":"ref_23","first-page":"161","article-title":"Temporal and Spatial Distribution Character of CH4 Near Surface","volume":"40","author":"Chang","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.1007\/s11434-011-4666-x","article-title":"Spatiotemporal variations in mid-upper tropospheric methane over China from satellite observations","volume":"56","author":"Zhang","year":"2011","journal-title":"Chin. Sci. Bull."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"D22301","DOI":"10.1029\/2009JD012287","article-title":"Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals","volume":"114","author":"Bergamaschi","year":"2009","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1080\/01431161.2010.517804","article-title":"Spatial variations of atmospheric methane concentrations in China","volume":"32","author":"Zhang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Meng, X., Chang, H., and Wang, X. (2022). Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis. Energies, 15.","DOI":"10.3390\/en15062262"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1007\/s12517-021-07998-0","article-title":"Forecasting and modeling of atmospheric methane concentration","volume":"14","author":"Rehman","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.5194\/acp-24-4047-2024","article-title":"Extending the wind profile beyond the surface layer by combining physical and machine learning approaches","volume":"24","author":"Liu","year":"2024","journal-title":"Atmos. Chem. Phys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6561","DOI":"10.1109\/JSTARS.2024.3373395","article-title":"An Improved Method for Individual Tree Segmentation in Complex Urban Scenes Based on Using Multispectral LiDAR by Deep Learning","volume":"17","author":"Yang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"47239","DOI":"10.1007\/s11356-021-14007-0","article-title":"Spatiotemporal variation in near-surface CH4 concentrations in China over the last two decades","volume":"28","author":"Xu","year":"2021","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3099","DOI":"10.5194\/acp-16-3099-2016","article-title":"Atmospheric methane evolution the last 40 years","volume":"16","author":"Myhre","year":"2016","journal-title":"Atmos. Chem. Phys."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s00376-013-3018-y","article-title":"Simulated spatial distribution and seasonal variation of atmospheric methane over China: Contributions from key sources","volume":"31","author":"Zhang","year":"2014","journal-title":"Adv. Atmos. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, J., Han, G., Mao, H., Pei, Z., Ma, X., Jia, W., and Gong, W. (2022). The Spatial and Temporal Distribution Patterns of XCH4 in China: New Observations from TROPOMI. Atmosphere, 13.","DOI":"10.3390\/atmos13020177"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14159","DOI":"10.5194\/acp-21-14159-2021","article-title":"Global distribution of methane emissions: A comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments","volume":"21","author":"Qu","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, J., Wang, W., Fu, M., and Wang, Y. (J. Clean. Prod., 2024). Insights into Global Visibility Patterns: Spatiotemporal Distributions Revealed by Satellite Remote Sensing, J. Clean. Prod., in press.","DOI":"10.1016\/j.jclepro.2024.143069"},{"key":"ref_39","unstructured":"Breiman, L. (2023, October 14). Out-of-Bag Estimation. Available online: https:\/\/api.semanticscholar.org\/CorpusID:17166335."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wu, X., Zhang, X., Chuai, X., Huang, X., and Wang, Z. (2019). Long-Term Trends of Atmospheric CH4 Concentration across China from 2002 to 2016. Remote Sens., 11.","DOI":"10.3390\/rs11050538"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, J., Wang, W., and Wang, N. (Adv. Space Res., 2024). Seamless Reconstruction and Spatiotemporal Analysis of Satellite-based XCO2 Incorporating Temporal Characteristics: A Case Study in China during 2015\u20132020, Adv. Space Res., in press.","DOI":"10.1016\/j.asr.2024.07.007"},{"key":"ref_42","unstructured":"(2011). A method for evaluating bias in global measurements of CO2 total columns from space. Atmos. Chem. Phys., 11, 12317\u201312337."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.5194\/amt-10-2627-2017","article-title":"Investigating the performance of a greenhouse gas observatory in Hefei, China","volume":"10","author":"Wang","year":"2017","journal-title":"Atmos. Meas. Tech."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.5194\/essd-12-1679-2020","article-title":"New ground-based Fourier-transform near-infrared solar absorption measurements of XCO2, XCH4 and XCO at Xianghe, China","volume":"12","author":"Yang","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1038\/s41467-019-14155-5","article-title":"Fingerprint of rice paddies in spatial\u2013temporal dynamics of atmospheric methane concentration in monsoon Asia","volume":"11","author":"Zhang","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ni, Q., Zhou, M., Wang, J., Wang, T., Wang, G., and Wang, P. (2023). Intercomparison of CH4 Products in China from GOSAT, TROPOMI, IASI, and AIRS Satellites. Remote Sens., 15.","DOI":"10.3390\/rs15184499"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1126\/science.1175176","article-title":"Large-scale controls of methanogenesis inferred from methane and gravity spaceborne data","volume":"327","author":"Bloom","year":"2010","journal-title":"Science"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1029\/91GB02586","article-title":"Methane emission from rice fields as influenced by solar radiation, temperature, and straw incorporation","volume":"5","author":"Sass","year":"1991","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_49","unstructured":"Crippa, M., Guizzardi, D., Banja, M., Solazzo, E., Muntean, M., Schaaf, E., Pagani, F., Monforti, F., Olivier, J., and Quadrelli, R. (2022). CO2 Emissions of All World Countries. JRC\/IEA\/PBL 2022 Report, Publications Office of the European Union."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1038\/321148a0","article-title":"Atmospheric CH4, CO and OH from 1860 to 1985","volume":"321","author":"Thompson","year":"1986","journal-title":"Nature"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"D11306","DOI":"10.1029\/2004JD005650","article-title":"Interannual variation of 13C in tropospheric methane: Implications for a possible atomic chlorine sink in the marine boundary layer","volume":"110","author":"Allan","year":"2005","journal-title":"J. Geophys. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1029\/2018GB006009","article-title":"Very Strong Atmospheric Methane Growth in the 4 Years 2014\u20132017: Implications for the Paris Agreement","volume":"33","author":"Nisbet","year":"2019","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9988","DOI":"10.1021\/acs.est.2c03834","article-title":"Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence","volume":"56","author":"Wei","year":"2022","journal-title":"Environ. Sci. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4737","DOI":"10.1007\/s13762-022-04314-5","article-title":"Short-term effect of COVID-19 lockdowns on atmospheric CO2, CH4 and PM2.5 concentrations in urban environment","volume":"20","author":"Gulyaev","year":"2022","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/j.jes.2021.09.034","article-title":"Dramatic decline of observed atmospheric CO2 and CH4 during the COVID-19 lockdown over the Yangtze River Delta of China","volume":"124","author":"Liang","year":"2023","journal-title":"J. Environ. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.5194\/bg-18-2047-2021","article-title":"Methane dynamics in three different Siberian water bodies under winter and summer conditions","volume":"18","author":"Bussmann","year":"2021","journal-title":"Biogeosciences"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1134\/S000143701402009X","article-title":"Methane in the water and bottom sediments of the mouth area of the Severnaya Dvina River during the winter time","volume":"54","author":"Fedorov","year":"2014","journal-title":"Oceanology"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s11069-014-1074-y","article-title":"Cold and warm air temperature spells during the winter and summer seasons and their impact on energy consumption in urban areas","volume":"73","author":"Selakov","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"128061","DOI":"10.1016\/j.jhydrol.2022.128061","article-title":"Switches of methane production pathways and emissions with human activity intensity in subtropical estuaries","volume":"612","author":"Li","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"105861","DOI":"10.1016\/j.resconrec.2021.105861","article-title":"Estimation of Chinese city-level anthropogenic methane emissions in 2015","volume":"175","author":"Wang","year":"2021","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e2202742119","DOI":"10.1073\/pnas.2202742119","article-title":"Observed changes in China\u2019s methane emissions linked to policy drivers","volume":"119","author":"Zhang","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"147116","DOI":"10.1016\/j.scitotenv.2021.147116","article-title":"Evaluation of comprehensive monthly-gridded methane emissions from natural and anthropogenic sources in China","volume":"784","author":"Gong","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1073\/pnas.1814297116","article-title":"Interpreting contemporary trends in atmospheric methane","volume":"116","author":"Turner","year":"2019","journal-title":"Proc. Natl. Acad. Sci. 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