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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- An improved deep learning approach for detection of maize tassels using UAV-based RGB images J. Chen et al. 10.1016/j.jag.2024.103922
- Early mapping of winter wheat in Henan province of China using time series of Sentinel-2 data X. Huang et al. 10.1080/15481603.2022.2104999
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- The role of phenology in crop yield prediction: Comparison of ground-based phenology and remotely sensed phenology J. Pei et al. 10.1016/j.agrformet.2024.110340
- Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold Y. Ma et al. 10.3390/rs16050826
- High-resolution mapping of China’s flooded croplands J. Qiu et al. 10.1016/j.scib.2025.01.053
- The dataset of main grain land changes in China over 1985–2020 S. Liu et al. 10.1038/s41597-024-04292-y
- Refining landsat-based annual NDVImax estimation using shape model fitting and phenological metrics L. Zhang et al. 10.1016/j.ecoinf.2025.103107
- Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review M. Debbagh et al. 10.1016/j.plaphe.2025.100089
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Latest update: 28 Aug 2025
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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