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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19 citations as recorded by crossref.
- Drought risk assessment for early maize growth in Northeast China based on a reconstructed phenological dataset X. Wang et al. 10.1111/jac.12702
- Coupling GEDI LiDAR and Optical Satellite for Revealing Large‐Scale Maize Lodging in Northeast China Q. Zhang et al. 10.1029/2023EF003590
- The 500-meter long-term winter wheat grain protein content dataset for China from multi-source data X. Xu et al. 10.1038/s41597-024-03866-0
- A twenty-year dataset of high-resolution maize distribution in China Q. Peng et al. 10.1038/s41597-023-02573-6
- 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
- Opposite effect on soil organic carbon between grain and non-grain crops: Evidence from Main Grain Land, China S. Liu et al. 10.1016/j.agee.2024.109364
- 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
- Time series sUAV data reveal moderate accuracy and large uncertainties in spring phenology metric of deciduous broadleaf forest as estimated by vegetation index-based phenological models L. Pan et al. 10.1016/j.isprsjprs.2024.09.023
- A Comprehensive Evaluation of Flooding’s Effect on Crops Using Satellite Time Series Data S. Miao et al. 10.3390/rs15051305
- Large-Scale Crop Mapping Based on Multisource Remote Sensing Intelligent Interpretation: A Spatiotemporal Data Cubes Approach J. Sun et al. 10.1109/JSTARS.2024.3428627
- Combining shape and crop models to detect soybean growth stages Z. Lou et al. 10.1016/j.rse.2023.113827
- From satellite-based phenological metrics to crop planting dates: Deriving field-level planting dates for corn and soybean in the U.S. Midwest Q. Zhou et al. 10.1016/j.isprsjprs.2024.07.031
- 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
- 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
- Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method Z. Zhang et al. 10.3390/rs16132342
- 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 30 m annual maize phenology dataset from 1985 to 2020 in China Q. Niu et al. 10.5194/essd-14-2851-2022
18 citations as recorded by crossref.
- Drought risk assessment for early maize growth in Northeast China based on a reconstructed phenological dataset X. Wang et al. 10.1111/jac.12702
- Coupling GEDI LiDAR and Optical Satellite for Revealing Large‐Scale Maize Lodging in Northeast China Q. Zhang et al. 10.1029/2023EF003590
- The 500-meter long-term winter wheat grain protein content dataset for China from multi-source data X. Xu et al. 10.1038/s41597-024-03866-0
- A twenty-year dataset of high-resolution maize distribution in China Q. Peng et al. 10.1038/s41597-023-02573-6
- 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
- Opposite effect on soil organic carbon between grain and non-grain crops: Evidence from Main Grain Land, China S. Liu et al. 10.1016/j.agee.2024.109364
- 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
- Time series sUAV data reveal moderate accuracy and large uncertainties in spring phenology metric of deciduous broadleaf forest as estimated by vegetation index-based phenological models L. Pan et al. 10.1016/j.isprsjprs.2024.09.023
- A Comprehensive Evaluation of Flooding’s Effect on Crops Using Satellite Time Series Data S. Miao et al. 10.3390/rs15051305
- Large-Scale Crop Mapping Based on Multisource Remote Sensing Intelligent Interpretation: A Spatiotemporal Data Cubes Approach J. Sun et al. 10.1109/JSTARS.2024.3428627
- Combining shape and crop models to detect soybean growth stages Z. Lou et al. 10.1016/j.rse.2023.113827
- From satellite-based phenological metrics to crop planting dates: Deriving field-level planting dates for corn and soybean in the U.S. Midwest Q. Zhou et al. 10.1016/j.isprsjprs.2024.07.031
- 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
- 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
- Estimating Global Wheat Yields at 4 km Resolution during 1982–2020 by a Spatiotemporal Transferable Method Z. Zhang et al. 10.3390/rs16132342
- 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
1 citations as recorded by crossref.
Latest update: 22 Nov 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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