Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

doi:10.3390/su12052099

CORC > 植物研究所 > 中国科学院植物研究所 > 植被与环境变化国家重点实验室

	Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison
	Zhu, Xiaobo 4,5,6; He, Honglin 5,6,7; Ma, Mingguo 4; Ren, Xiaoli 5,6; Zhang, Li 5,6,7,10; Zhang, Fawei 8; Li, Yingnian 8; Shi, Peili 5,7; Chen, Shiping 9; Wang, Yanfen 10
刊名	SUSTAINABILITY
	2020
卷号	12 期号:5
关键词	ecosystem respiration machine learning deep learning grasslands northern China
DOI	10.3390/su12052099
文献子类	Article
英文摘要	While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China's grasslands. The four models were trained with two strategies: training for all of northern China's grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China's grasslands fairly well, while the SAE model performed best (R-2 = 0.858, RMSE = 0.472 gC m(-2) d(-1), MAE = 0.304 gC m(-2) d(-1)). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.
学科主题	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
电子版国际标准刊号	2071-1050
出版地	BASEL
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
语种	英语
出版者	MDPI
WOS记录号	WOS:000522470900400
资助机构	National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41571424, 41830648, 41771453] ; Strategic Priority Research Program of the Chinese Academy of SciencesChinese Academy of Sciences [XDA19020301]
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/21840]
专题	植被与环境变化国家重点实验室
作者单位	1.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Tianjin Univ, Inst Surface Earth Syst Sci, Tianjin 300072, Peoples R China 4.Southwest Univ, Chongqing Jinfo Mt Field Sci Observat & Res Stn K, Sch Geog Sci, Minist Educ, Chongqing 400715, Peoples R China 5.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data Ap, Sch Geog Sci, Chongqing 400715, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Beijing 100101, Peoples R China 8.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 9.Chinese Acad Sci, Northwest Inst Plateau Biol, Xining 810001, Peoples R China 10.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Xiaobo,He, Honglin,Ma, Mingguo,et al. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison[J]. SUSTAINABILITY,2020,12(5).
APA	Zhu, Xiaobo.,He, Honglin.,Ma, Mingguo.,Ren, Xiaoli.,Zhang, Li.,...&Gu, Qing.(2020).Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison.SUSTAINABILITY,12(5).
MLA	Zhu, Xiaobo,et al."Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison".SUSTAINABILITY 12.5(2020).