Articles | Volume 18, issue 2
https://doi.org/10.5194/essd-18-1203-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-1203-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The newly developed Multi-ensemble Biomass-burning Emissions Inventory (MBEI): characterizing and unraveling spatiotemporal uncertainty in global biomass burning emissions
Xinlu Liu
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
School of Ecology, Hainan University, Haikou 570228, China
Zhongyi Sun
CORRESPONDING AUTHOR
School of Ecology, Hainan University, Haikou 570228, China
Chong Shi
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Peng Wang
Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, 200438, China
Tangzhe Nie
School of Water Conservancy and Electric Power, Heilongjiang University, Harbin 150006, China
Qingnan Chu
Centro de Biotecnologia y Genómica de Planta (UPM-INIA). Universidad Politécnica de Madrid, Campus de Montegancedo, Madrid, Spain
Huazhe Shang
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Lu Sun
School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an, 710049, China
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Meng Guo
School of Geographical Sciences, Northeast Normal University, Changchun 130024, China
Kunpeng Yi
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Zhenghong Tan
School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China
Lan Wu
School of Ecology, Hainan University, Haikou 570228, China
Xinchun Lu
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
School of Water Conservancy and Electric Power, Heilongjiang University, Harbin 150006, China
Shuai Yin
CORRESPONDING AUTHOR
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Data sets
Multi-ensemble Biomass-burning Emissions Inventory (MBEI)_v1.0 Xinlu Liu and Shuai Yin https://doi.org/10.5281/zenodo.18104830
Short summary
Estimates of global biomass burning emissions differ, posing a challenge for environment and climate change research. In response to this challenge, our new 2003–2023 dataset integrates top-down and bottom-up methods with multi-source data. This provides a plausible emissions range to quantify uncertainty, revealing that the greatest uncertainty is not in traditional hotspots but in regions with infrequent, extreme fires. This work offers vital data for more robust climate models.
Estimates of global biomass burning emissions differ, posing a challenge for environment and...
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