Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-791-2023
© Author(s) 2023. 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-15-791-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
14 Feb 2023
Data description paper |
| 14 Feb 2023
| 14 Feb 2023
AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015
Huaqing Wu
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Jing Zhang
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
Faculty of Geographical Science, Beijing Normal University, Beijing
100875, PR China
Zhao Zhang
CORRESPONDING AUTHOR
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Jichong Han
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Juan Cao
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Liangliang Zhang
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Yuchuan Luo
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Qinghang Mei
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Jialu Xu
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Fulu Tao
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographical Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
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Cited
9 citations as recorded by crossref.
- Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method L. Li et al. 10.1109/JSTARS.2024.3357141
- Compound impact of COVID-19, economy and climate on the spatial distribution of global agriculture and food security P. Yao et al. 10.1016/j.scitotenv.2023.163105
- Effects of Age Group, Gender, and Consumption Frequency on Texture Perception and Liking of Cooked Rice or Bread W. Choi & H. Seo 10.3390/foods12091793
- Downscaling Administrative-Level Crop Yield Statistics to 1 km Grids Using Multisource Remote Sensing Data and Ensemble Machine Learning J. Pei et al. 10.1109/JSTARS.2024.3441252
- Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy J. Silva et al. 10.1016/j.fcr.2023.109063
- Reservoir dominated spatio-temporal changes of the surface water area in the Yangtze River Basin during past three decades L. Chang et al. 10.1016/j.ejrh.2024.101948
- Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain H. Zhuang et al. 10.1016/j.agrformet.2024.109909
- Rice yield prediction using radar vegetation indices from Sentinel-1 data and multiscale one-dimensional convolutional long- and short-term memory network model C. Sun et al. 10.1117/1.JRS.18.024505
- AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015 H. Wu et al. 10.5194/essd-15-791-2023
8 citations as recorded by crossref.
- Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method L. Li et al. 10.1109/JSTARS.2024.3357141
- Compound impact of COVID-19, economy and climate on the spatial distribution of global agriculture and food security P. Yao et al. 10.1016/j.scitotenv.2023.163105
- Effects of Age Group, Gender, and Consumption Frequency on Texture Perception and Liking of Cooked Rice or Bread W. Choi & H. Seo 10.3390/foods12091793
- Downscaling Administrative-Level Crop Yield Statistics to 1 km Grids Using Multisource Remote Sensing Data and Ensemble Machine Learning J. Pei et al. 10.1109/JSTARS.2024.3441252
- Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy J. Silva et al. 10.1016/j.fcr.2023.109063
- Reservoir dominated spatio-temporal changes of the surface water area in the Yangtze River Basin during past three decades L. Chang et al. 10.1016/j.ejrh.2024.101948
- Integrating data assimilation, crop model, and machine learning for winter wheat yield forecasting in the North China Plain H. Zhuang et al. 10.1016/j.agrformet.2024.109909
- Rice yield prediction using radar vegetation indices from Sentinel-1 data and multiscale one-dimensional convolutional long- and short-term memory network model C. Sun et al. 10.1117/1.JRS.18.024505
1 citations as recorded by crossref.
Latest update: 15 Nov 2024
Short summary
High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed...
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