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Paddy rice methane emissions across Monsoon Asia

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dc.contributor.authorOuyang, Zutao-
dc.contributor.authorJackson, Robert B.-
dc.contributor.authorMcNicol, Gavin-
dc.contributor.authorFluet-Chouinard, Etienne-
dc.contributor.authorRunkle, Benjamin R. K.-
dc.contributor.authorPapale, Dario-
dc.contributor.authorKnox, Sara H.-
dc.contributor.authorCooley, Sarah-
dc.contributor.authorDelwiche, Kyle B.-
dc.contributor.authorFeron, Sarah-
dc.contributor.authorIrvin, Jeremy Andrew-
dc.contributor.authorMalhotra, Avni-
dc.contributor.authorMuddasir, Muhammad-
dc.contributor.authorSabbatini, Simone-
dc.contributor.authorAlberto, Ma Carmelita R.-
dc.contributor.authorCescatti, Alessandro-
dc.contributor.authorChen, Chi-Ling-
dc.contributor.authorDong, Jinwei-
dc.contributor.authorFong, Bryant N.-
dc.contributor.authorGuo, Haiqiang-
dc.contributor.authorHao, Lu-
dc.contributor.authorIwata, Hiroki-
dc.contributor.authorJia, Qingyu-
dc.contributor.authorJu, Weimin-
dc.contributor.authorKang, Minseok-
dc.contributor.authorLi, Hong-
dc.contributor.authorKim, Joon-
dc.contributor.authorReba, Michele L.-
dc.contributor.authorNayak, Amaresh Kumar-
dc.contributor.authorRoberti, Debora Regina-
dc.contributor.authorRyu, Youngryel-
dc.contributor.authorSwain, Chinmaya Kumar-
dc.contributor.authorTsuang, Benjei-
dc.contributor.authorXiao, Xiangming-
dc.contributor.authorYuan, Wenping-
dc.contributor.authorZhang, Geli-
dc.contributor.authorZhang, Yongguang-
dc.date.accessioned2024-03-20T06:03:06Z-
dc.date.available2024-03-20T06:03:06Z-
dc.date.created2023-02-06-
dc.date.created2023-02-06-
dc.date.created2023-02-06-
dc.date.issued2023-01-
dc.identifier.citationRemote Sensing of Environment, Vol.284, p. 113335-
dc.identifier.issn0034-4257-
dc.identifier.urihttps://hdl.handle.net/10371/199141-
dc.description.abstractAlthough rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes similar to 8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr(-1)) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5 degrees) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where similar to 87% of global rice area is cultivated and similar to 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 +/- 1.1 Tg yr(-1) for 2001-2015, which is at the lower range of previous inventory-based estimates (20-32 CH4 Tg yr(-1)). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years.-
dc.language영어-
dc.publisherElsevier BV-
dc.titlePaddy rice methane emissions across Monsoon Asia-
dc.typeArticle-
dc.identifier.doi10.1016/j.rse.2022.113335-
dc.citation.journaltitleRemote Sensing of Environment-
dc.identifier.wosid000898666500001-
dc.identifier.scopusid2-s2.0-85141747462-
dc.citation.startpage113335-
dc.citation.volume284-
dc.description.isOpenAccessY-
dc.contributor.affiliatedAuthorKim, Joon-
dc.contributor.affiliatedAuthorRyu, Youngryel-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordPlusGREENHOUSE-GAS EMISSIONS-
dc.subject.keywordPlusNET ECOSYSTEM EXCHANGE-
dc.subject.keywordPlusWATER MANAGEMENT-
dc.subject.keywordPlusFLUX TOWERS-
dc.subject.keywordPlusCHINA-
dc.subject.keywordPlusCARBON-
dc.subject.keywordPlusFUTURE-
dc.subject.keywordPlusREPRESENTATIVENESS-
dc.subject.keywordPlusASSIMILATION-
dc.subject.keywordPlusRESPIRATION-
dc.subject.keywordAuthorRemote sensing-
dc.subject.keywordAuthorClimate change-
dc.subject.keywordAuthorGreenhouse gas emission-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorEddy covariance-
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  • College of Agriculture and Life Sciences
  • Department of Landscape Architecture and Rural System Engineering
Research Area Crop, Forest Carbon, Sensing Network, Water Cycles

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