https://scholars.tari.gov.tw/handle/123456789/18786
Title: | Paddy rice methane emissions across Monsoon Asia | Authors: | Zutao Ouyang Robert B. Jackson Gavin McNicol Etienne Fluet-Chouinard Benjamin R.K. Runkle Dario Papale Sara H. Knox Sarah Cooley Kyle B. Delwiche Sarah Feron Jeremy Andrew Irvin Avni Malhotra Muhammad Muddasir Simone Sabbatini Ma. Carmelita R. Alberto Alessandro Cescatti Chi-Ling Chen Jinwei Dong Bryant N. Fong Haiqiang Guo Lu Hao Hiroki Iwata Oingyu Jia Weimin Ju Minseok Kang Hong Li Joon Kim Michele L. Reba Amaresh Kumar Nayak Debora Regina Roberti Youngryel Ryu Chinmaya Kumar Swain Benjei Tsuang Xiangming Xiao Wenping Yuan Gei Zhang Yongguang Zhang |
Keywords: | remote sensing;climate change;Greenhouse gas emission;machine learning;eddy covariance | Issue Date: | Jan-2023 | Publisher: | Elsevier Science Inc. | Journal Volume: | 284 | Start page/Pages: | 113335 | Source: | Remote Sensing of Environment | Abstract: | Although 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. |
URI: | https://www.sciencedirect.com/science/article/pii/S0034425722004412?via%3Dihub https://scholars.tari.gov.tw/handle/123456789/18786 |
ISSN: | 0034-4257 | DOI: | 10.1016/j.rse.2022.113335 |
Appears in Collections: | SCI期刊 |
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