气候变化领域集成服务门户(CCPortal): XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees

CCPortal

DOI	10.3390/atmos15040440
	XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees
	Li, Xuwen; Jiang, Sheng; Wang, Xiangyuan; Wang, Tiantian; Zhang, Su; Guo, Jinjin; Jiao, Donglai
发表日期	2024
EISSN	2073-4433
起始页码	15
结束页码	4
卷号	15 期号:4
英文摘要	Carbon dioxide (CO2) is currently the most harmful greenhouse gas in the atmosphere. Obtaining long-term, high-resolution atmospheric column CO2 concentration (XCO2) datasets is of great practical significance for mitigating the greenhouse effect, identifying and controlling carbon emission sources, and achieving carbon cycle management. However, mainstream satellite observations provide XCO2 datasets with coarse spatial resolution, which is insufficient to support the needs of higher-precision research. To address this gap, in this study, we integrate spatial information with the extreme random trees model and develop a new machine learning model called spatial extreme random trees (SExtraTrees) to reconstruct a 1 km spatial resolution XCO2 dataset for China from 2016 to 2020. The results indicate that the predictive ability of spatial extreme random trees is more stable and has higher fitting accuracy compared to other methods. Overall, XCO2 in China shows an increasing trend year by year, with the spatial distribution revealing significantly higher XCO2 levels in eastern coastal regions compared to western inland areas. The contributions of this study are primarily in the following areas: (1) Considering the spatial heterogeneity of XCO2 and combining spatial features with the advantages of machine learning, we construct the spatial extreme random trees model, which is verified to have high predictive accuracy. (2) Using the spatial extreme random trees model, we reconstruct high-resolution XCO(2 )datasets for China from 2016 to 2020, providing data support for carbon emission reduction and related decision making. (3) Based on the generated dataset, we analyze the spatiotemporal distribution patterns of XCO2 in China, thereby improving emission reduction policies and sustainable development measures.
英文关键词	XCO2; spatial downscaling; SExtraTrees; high resolution; spatial features
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001211153300001
来源期刊	ATMOSPHERE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299477
作者单位	Nanjing University of Posts & Telecommunications
推荐引用方式 GB/T 7714	Li, Xuwen,Jiang, Sheng,Wang, Xiangyuan,et al. XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees[J],2024,15(4).
APA	Li, Xuwen.,Jiang, Sheng.,Wang, Xiangyuan.,Wang, Tiantian.,Zhang, Su.,...&Jiao, Donglai.(2024).XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees.ATMOSPHERE,15(4).
MLA	Li, Xuwen,et al."XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees".ATMOSPHERE 15.4(2024).