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Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison release_72jxazf6gncfdmqt3ggmnysqtq

by Xiaobo Zhu, Honglin He, Mingguo Ma, Xiaoli Ren, Li Zhang, Fawei Zhang, Yingnian Li, Peili Shi, 世苹 陈, Yanfen Wang, Xiaoping Xin, Yaoming Ma (+10 others)

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abstracts[] {'sha1': '024c68662ab27bcba446bc4dd691c319ee1f37de', 'content': "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 (R2 = 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.", 'mimetype': 'application/xml+jats', 'lang': None}
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release_date 2020-03-09
release_stage published
release_type article-journal
release_year 2020
subtitle
title Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison
version
volume 12
webcaptures []
withdrawn_date
withdrawn_status
withdrawn_year
work_id eas3mlefl5dkbovrw54f72leti
As JSON via API

Extra Metadata (raw JSON)

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crossref.subject ['Renewable Energy, Sustainability and the Environment', 'Geography, Planning and Development', 'Management, Monitoring, Policy and Law']
crossref.type journal-article