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

文献类型：期刊论文

作者Zhu, Xiaobo1,2,3,4; He, Honglin3,4,5; Ma, Mingguo1,2; Ren, Xiaoli3,4; Zhang, Li3,4,5; Zhang, Fawei6; Li, Yingnian6; Shi, Peili3,5; Chen, Shiping7; Wang, Yanfen8
刊名SUSTAINABILITY
出版日期2020-03-01
卷号12期号:5页码:17
关键词ecosystem respiration machine learning deep learning grasslands northern China
DOI10.3390/su12052099
通讯作者He, Honglin(hehl@igsnrr.ac.cn) ; Ma, Mingguo(mmg@swu.edu.cn)
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.
资助项目National Natural Science Foundation of China[41571424] ; National Natural Science Foundation of China[41830648] ; National Natural Science Foundation of China[41771453] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19020301]
WOS研究方向Science & Technology - Other Topics ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000522470900400
出版者MDPI
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/133754]  
专题中国科学院地理科学与资源研究所
通讯作者He, Honglin; Ma, Mingguo
作者单位1.Southwest Univ, Chongqing Jinfo Mt Field Sci Observat & Res Stn K, Sch Geog Sci, Minist Educ, Chongqing 400715, Peoples R China
2.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data Ap, Sch Geog Sci, Chongqing 400715, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Beijing 100101, Peoples R China
5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
6.Chinese Acad Sci, Northwest Inst Plateau Biol, Xining 810001, Peoples R China
7.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
8.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
9.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
10.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100101, 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):17.
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),17.
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):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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