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Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison
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; Xin, Xiaoping9; Ma, Yaoming8,10,11; Zhang, Yu12; Du, Mingyuan13; Ge, Rong3,4,8; Zeng, Na3,4,8; Li, Pan14; Niu, Zhongen3,4,8; Zhang, Liyun3,4,8; Lv, Yan3,4,8; Song, Zengjing1,2; Gu, Qing1,2
2020-03-01
Source PublicationSUSTAINABILITY
Volume12Issue:5Pages:17
Corresponding AuthorHe, Honglin(hehl@igsnrr.ac.cn) ; Ma, Mingguo(mmg@swu.edu.cn)
AbstractWhile 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.
Keywordecosystem respiration machine learning deep learning grasslands northern China
DOI10.3390/su12052099
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS Research AreaScience & Technology - Other Topics ; Environmental Sciences & Ecology
WOS SubjectGreen & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS IDWOS:000522470900400
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/133755
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHe, Honglin; Ma, Mingguo
Affiliation1.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
11.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China
12.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China
13.Natl Agr & Food Res Org, Inst Agroenvironm Sci, Tsukuba, Ibaraki 3058604, Japan
14.Tianjin Univ, Inst Surface Earth Syst Sci, Tianjin 300072, Peoples R China
Recommended Citation
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.
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