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Data-Driven Estimation of Gross Primary Production |
Kun Zhang1(),Naiwen Liu2(),Shuai Gao3,Shuhui Zhao1 |
1.School of Information Science & Engineering,Shandong Normal University, Jinan 250358, China 2.Provincial Key Laboratory of Information Technology, School of Information Engineering, Shandong Management University, Jinan 250357, China 3.The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China |
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Abstract Gross Primary Production (GPP) of vegetation refers to the assimilation of all organic matter produced by green plants through photosynthesis and fixed carbon dioxide per unit time and unit area. Accurate estimation of GPP is helpful for the study of carbon cycle. In order to improve the estimation accuracy of GPP, this study combines machine learning technology and remote sensing technology. First, the remote sensing data under the GEE platform and the flux tower measurement data of the China Terrestrial Ecosystem Flux Observation Research Network are used to establish a data set. Then use random forest as the estimation model, and adjust the model according to the data characteristics after modeling. Finally, the prediction results of the model are obtained, the determination coefficient R2 is 0.87, and the root mean square error RMSE is 1.132 gC·m-2·d-1. This shows that the random forest model can estimate GPP more accurately.From the results of this study, we can see that the rapid development of computer technology represented by big data and artificial intelligence will inject new vitality into remote sensing technology and make remote sensing technology enter a more mature stage of development and application.
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Received: 30 June 2018
Published: 15 September 2020
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Corresponding Authors:
Naiwen Liu
E-mail: 329937012@qq.com;sdnwliu@126.com
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