Assessing and improving the high uncertainty of global gross primary productivity products based on deep learning under extreme climatic conditions

被引：0
|
作者：
Qian, Long [1 ,2 ,3 ,4 ]
Yu, Xingjiao [1 ,2 ]
Zhang, Zhitao [1 ,2 ]
Wu, Lifeng [1 ,3 ,4 ]
Fan, Junliang [1 ,2 ]
Xiang, Youzhen [1 ,2 ]
Chen, Junying [1 ,2 ]
Liu, Xiaogang [4 ]
机构：
[1] College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling,712100, China
[2] Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling,712100, China
[3] School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang,330099, China
[4] Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming,650500, China
来源：
Science of the Total Environment | 2024年 / 957卷
关键词：
Compendex;
D O I：
10.1016/j.scitotenv.2024.177344
中图分类号：
学科分类号：
摘要：
Convolutional neural networks
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