1.
Ouyang, Zutao; Jackson, Robert B.; McNicol, Gavin; Fluet-Chouinard, Etienne; Runkle, Benjamin R. K.; Papale, Dario; Knox, Sara H.; Cooley, Sarah; Delwiche, Kyle B.; Feron, Sarah; Irvin, Jeremy Andrew; Malhotra, Avni; Muddasir, Muhammad; Sabbatini, Simone; Alberto, Ma. Carmelita R.; Cescatti, Alessandro; Chen, Chi-Ling; Dong, Jinwei; Fong, Bryant N.; Guo, Haiqiang; Hao, Lu; Iwata, Hiroki; Jia, Qingyu; Ju, Weimin; Kang, Minseok; Li, Hong; Kim, Joon; Reba, Michele L.; Nayak, Amaresh Kumar; Roberti, Debora Regina; Ryu, Youngryel; Swain, Chinmaya Kumar; Tsuang, Benjei; Xiao, Xiangming; Yuan, Wenping; Zhang, Geli; Zhang, Yongguang
Paddy rice methane emissions across Monsoon Asia Journal Article
In: Remote Sensing of Environment, vol. 284, pp. 113335, 0000, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: climate change, machine learning, remote sensing
@article{nokey,
title = {Paddy rice methane emissions across Monsoon Asia},
author = {Zutao Ouyang and Robert B. Jackson and Gavin McNicol and Etienne Fluet-Chouinard and Benjamin R.K. Runkle and Dario Papale and Sara H. Knox and Sarah Cooley and Kyle B. Delwiche and Sarah Feron and Jeremy Andrew Irvin and Avni Malhotra and Muhammad Muddasir and Simone Sabbatini and Ma. Carmelita R. Alberto and Alessandro Cescatti and Chi-Ling Chen and Jinwei Dong and Bryant N. Fong and Haiqiang Guo and Lu Hao and Hiroki Iwata and Qingyu Jia and Weimin Ju and Minseok Kang and Hong Li and Joon Kim and Michele L. Reba and Amaresh Kumar Nayak and Debora Regina Roberti and Youngryel Ryu and Chinmaya Kumar Swain and Benjei Tsuang and Xiangming Xiao and Wenping Yuan and Geli Zhang and Yongguang Zhang},
url = {https://www.sciencedirect.com/science/article/pii/S0034425722004412},
doi = {https://doi.org/10.1016/j.rse.2022.113335},
issn = {0034-4257},
journal = {Remote Sensing of Environment},
volume = {284},
pages = {113335},
abstract = {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-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.},
keywords = {climate change, machine learning, remote sensing},
pubstate = {published},
tppubtype = {article}
}
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-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.