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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Cited
12 citations as recorded by crossref.
- Combining shape and crop models to detect soybean growth stages Z. Lou et al. 10.1016/j.rse.2023.113827
- Drought risk assessment for early maize growth in Northeast China based on a reconstructed phenological dataset X. Wang et al. 10.1111/jac.12702
- A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery J. Yang et al. 10.1016/j.isprsjprs.2023.07.017
- Coupling GEDI LiDAR and Optical Satellite for Revealing Large‐Scale Maize Lodging in Northeast China Q. Zhang et al. 10.1029/2023EF003590
- A twenty-year dataset of high-resolution maize distribution in China Q. Peng et al. 10.1038/s41597-023-02573-6
- 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
- The Improved Winter Wheat Yield Estimation by Assimilating GLASS LAI Into a Crop Growth Model With the Proposed Bayesian Posterior-Based Ensemble Kalman Filter H. Huang et al. 10.1109/TGRS.2023.3259742
- 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
- An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning H. Guan et al. 10.1016/j.jag.2022.102992
- Estimating the Legacy Effect of Post-Cutting Shelterbelt on Crop Yield Using Google Earth and Sentinel-2 Data Y. Liu et al. 10.3390/rs14195005
- A Comprehensive Evaluation of Flooding’s Effect on Crops Using Satellite Time Series Data S. Miao et al. 10.3390/rs15051305
- A 30 m annual maize phenology dataset from 1985 to 2020 in China Q. Niu et al. 10.5194/essd-14-2851-2022
11 citations as recorded by crossref.
- Combining shape and crop models to detect soybean growth stages Z. Lou et al. 10.1016/j.rse.2023.113827
- Drought risk assessment for early maize growth in Northeast China based on a reconstructed phenological dataset X. Wang et al. 10.1111/jac.12702
- A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery J. Yang et al. 10.1016/j.isprsjprs.2023.07.017
- Coupling GEDI LiDAR and Optical Satellite for Revealing Large‐Scale Maize Lodging in Northeast China Q. Zhang et al. 10.1029/2023EF003590
- A twenty-year dataset of high-resolution maize distribution in China Q. Peng et al. 10.1038/s41597-023-02573-6
- 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
- The Improved Winter Wheat Yield Estimation by Assimilating GLASS LAI Into a Crop Growth Model With the Proposed Bayesian Posterior-Based Ensemble Kalman Filter H. Huang et al. 10.1109/TGRS.2023.3259742
- 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
- An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning H. Guan et al. 10.1016/j.jag.2022.102992
- Estimating the Legacy Effect of Post-Cutting Shelterbelt on Crop Yield Using Google Earth and Sentinel-2 Data Y. Liu et al. 10.3390/rs14195005
- A Comprehensive Evaluation of Flooding’s Effect on Crops Using Satellite Time Series Data S. Miao et al. 10.3390/rs15051305
1 citations as recorded by crossref.
Latest update: 19 Apr 2024
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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