Paddy rice methane emissions across Monsoon Asia

作者：Zutao Ouyang, Robert B. Jackson, Gavin McNicol, Etienne Fluet‐Chouinard, Benjamin R. K. Runkle, Dario Papale, Sara Knox, Sarah Cooley, Kyle Delwiche, Sarah Féron, Jeremy Irvin, Avni Malhotra, Muhammad Muddasir, Simone Sabbatini, Ma. Carmelita Alberto, Alessandro Cescatti, Chi‐Ling Chen, Jinwei Dong, B. Fong, Haiqiang Guo, Hao Lu, Hiroki Iwata, Qingyu Jia, Weimin Ju, Minseok Kang, Hong Li, Joon Kim, Michele L. Reba, A. K. Nayak, Débora Regina Roberti, Youngryel Ryu, Chinmaya Kumar Swain, Ben‐Jei Tsuang, Xiangming Xiao, Wenping Yuan, Geli Zhang, Yongguang Zhang · 发表于：Remote Sensing of Environment · 年份：2022 · DOI：10.1016/j.rse.2022.113335 · 被引用次数：78 · 研究领域：Atmospheric and Environmental Gas Dynamics、Climate variability and models、Hydrology and Watershed Management Studies

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼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°) 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 ∼87% of global rice area is cultivated and ∼ 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-...