Paddy rice methane emissions across Monsoon Asia
Journal
Remote Sensing of Environment
Journal Volume
284
Pages
113335
Date Issued
2023-01
Author(s)
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
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.
Subjects
remote sensing
climate change
Greenhouse gas emission
machine learning
eddy covariance
Publisher
Elsevier Science Inc.
Type
journal article