Articles | Volume 14, issue 6
https://doi.org/10.5194/essd-14-2851-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-2851-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 Jun 2022
Data description paper |
| 23 Jun 2022
| 23 Jun 2022
A 30 m annual maize phenology dataset from 1985 to 2020 in China
Quandi Niu
College of Land Science and Technology, China Agricultural
University, Beijing 100083, China
Xuecao Li
College of Land Science and Technology, China Agricultural
University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of
Agriculture and Rural Affairs, Beijing 100083, China
Jianxi Huang
CORRESPONDING AUTHOR
College of Land Science and Technology, China Agricultural
University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of
Agriculture and Rural Affairs, Beijing 100083, China
Hai Huang
College of Land Science and Technology, China Agricultural
University, Beijing 100083, China
Xianda Huang
College of Land Science and Technology, China Agricultural
University, Beijing 100083, China
Wei Su
College of Land Science and Technology, China Agricultural
University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of
Agriculture and Rural Affairs, Beijing 100083, China
Wenping Yuan
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510245, Guangdong, China
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Short summary
In this paper we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using Landsat images. The derived phenological indicators agree with in situ observations and provide more spatial details than moderate resolution phenology products. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future.
In this paper we generated the first national maize phenology product with a fine spatial...
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