气候变化领域集成服务门户(CCPortal): Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

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DOI	10.3390/su12052099
	Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison
	Zhu, Xiaobo; He, Honglin; Ma, Mingguo; Ren, Xiaoli; Zhang, Li; Zhang, Fawei; Li, Yingnian; Shi, Peili; Chen, Shiping; Wang, Yanfen; Xin, Xiaoping; Ma, Yaoming; Zhang, Yu; Du, Mingyuan; Ge, Rong; Zeng, Na; Li, Pan; Niu, Zhongen; Zhang, Liyun; Lv, Yan; Song, Zengjing; Gu, Qing
通讯作者	Ma, MG (通讯作者)
发表日期	2020
EISSN	2071-1050
卷号	12 期号:5
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.
英文关键词	ecosystem respiration; machine learning; deep learning; grasslands; northern China
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS记录号	WOS:000522470900400
来源期刊	SUSTAINABILITY
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/260072
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]. 中国科学院青藏高原研究所,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).