气候变化领域集成服务门户(CCPortal): Establishing the global isoscape of leaf carbon in C3 plants through the integrations of remote sensing, carbon, geographic, and physiological information

CCPortal

DOI	10.1016/j.rse.2023.113987
	Establishing the global isoscape of leaf carbon in C3 plants through the integrations of remote sensing, carbon, geographic, and physiological information
	Wang, Xiang; Chen, Guo; Awange, Joseph; Song, Yongze; Wu, Qi; Li, Xiaowei; February, Edmund; Saiz, Gustavo; Kiese, Ralf; Li, Xing; Xiao, Jingfeng; Zhao, Xiaoxiang; Wen, Bo
发表日期	2024
ISSN	0034-4257
EISSN	1879-0704
起始页码	302
卷号	302
英文摘要	The carbon isotope composition (813CLeaf) of C3 plant leaves provides valuable information on the carbon-water cycle of vegetation and their responses to climate change within terrestrial ecosystems. However, global applications of 813CLeaf are hindered by a lack of global long-term spatial maps (isoscapes) that capture vegetation 813CLeaf variations. The ways in which 813CLeaf varies over time and across regions are still unknown. In this study, we collected leaf carbon isotope samples across the globe and selected the optimal predictive model from three machine learning algorithms to construct long-term annual global 813CLeaf isoscapes at a spatial resolution of 0.05 degrees for natural C3 plants between 2001 and 2020. We also assessed the potential of remotely sensed spectral bands, atmospheric CO2 characteristics, geographic, and physiological information to estimate the 813CLeaf of the global C3 plants. Our results show that the random forest (RF) algorithm can more accurately construct the 813CLeaf isoscape (R2 = 0.61, Nash Sutcliffe = 0.61, RMSE = 1.21%o, MAE = 0.91%o) than the multilayer perceptron (MLP) and support vector machine (SVM). The inclusion of atmospheric CO2 characteristics, geographical, and physiological information greatly improves prediction compared to relying only on spectral bands. Among the variables, elevation, band 3 spectral reflectance, and solar-induced chlorophyll fluorescence were the three most important variables for constructing the isoscape model, and their relative importance all exceeded 85%. The predicted isoscape revealed strong spatial heterogeneity of 813CLeaf for C3 plants at a global scale between different continental regions, with enriched values occurring in high-altitude cold and arid regions, and depleted values occurring in warm, humid, or tropical regions. For the first time, we estimated the global depleted rate of 813CLeaf in C3 plant (-0.0491 +/- 0.07 %o year-1 from 2001 to 2020). Over the past two decades, 86.49% and 1.00% of global grid cells showed more depleted and more enriched trends in 813CLeaf of C3 plants, respectively. Our results demonstrated the potential for establishing isoscapes by combining remote sensing, atmospheric CO2 characteristics, physiological, and geographic variables using the RF machine learning algorithm. The isoscape products generated in this study are valuable for assessing carbon-water coupling of terrestrial ecosystems and for improving land surface models.
英文关键词	Remotely sensed data; Spectral bands; Stable isotope; MODIS; Machine learning; Solar -induced fluorescence; Global scale
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001154588300001
来源期刊	REMOTE SENSING OF ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305973
作者单位	Chengdu University of Technology; Chengdu University of Technology; Curtin University; Curtin University; Ningxia University; University of Cape Town; Universidad Catolica de la Santisima Concepcion; Imperial College London; Helmholtz Association; Karlsruhe Institute of Technology; Seoul National University (SNU); University System Of New Hampshire; University of New Hampshire; Chinese Academy of Sciences; Wuhan Botanical Garden, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Western Australia; Tianfu Yongxing Laboratory
推荐引用方式 GB/T 7714	Wang, Xiang,Chen, Guo,Awange, Joseph,et al. Establishing the global isoscape of leaf carbon in C3 plants through the integrations of remote sensing, carbon, geographic, and physiological information[J],2024,302.
APA	Wang, Xiang.,Chen, Guo.,Awange, Joseph.,Song, Yongze.,Wu, Qi.,...&Wen, Bo.(2024).Establishing the global isoscape of leaf carbon in C3 plants through the integrations of remote sensing, carbon, geographic, and physiological information.REMOTE SENSING OF ENVIRONMENT,302.
MLA	Wang, Xiang,et al."Establishing the global isoscape of leaf carbon in C3 plants through the integrations of remote sensing, carbon, geographic, and physiological information".REMOTE SENSING OF ENVIRONMENT 302(2024).