Articles | Volume 16, issue 4
https://doi.org/10.5194/essd-16-1689-2024
© Author(s) 2024. 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-16-1689-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China
Hui Li
Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Shaoqiang Wang
CORRESPONDING AUTHOR
Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Jinyuan Liu
Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Yuanyuan Liu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Zhenhai Liu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Shiliang Chen
Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Qinyi Wang
Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Tongtong Zhu
Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Lunche Wang
Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Lizhe Wang
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China
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Cited
9 citations as recorded by crossref.
- Rice recognition from Sentinel-1 SLC SAR data based on progressive feature screening and fusion S. Tian et al. https://doi.org/10.1016/j.jag.2024.104196
- Mapping Paddy Rice Cropping Intensity and Planting Dates in Monsoon Asia at 20 m Resolution during 2018–2021 from Multi-source Satellite Data Y. Chen et al. https://doi.org/10.34133/remotesensing.1045
- Net Primary Productivity Retrieval Based on ESTARFM Fusion and an Improved CASA Model Y. Cai et al. https://doi.org/10.3390/plants15101436
- A Phenology-Guided Multi-Source Framework for In-Season Rice Mapping in Cloud-Prone and Complex Agroecosystems W. Wang et al. https://doi.org/10.3390/rs18111828
- GTGRI: a Gaussian time-weighted growth rate index for multi-season paddy rice mapping in diverse climates with dense SAR time series D. Liu et al. https://doi.org/10.1080/15481603.2026.2629148
- Estimation of rice yield using multi-source remote sensing data combined with crop growth model and deep learning algorithm J. Lu et al. https://doi.org/10.1016/j.agrformet.2025.110600
- Semi-automatic paddy rice intensity mapping for southern China with multiple cropping systems L. Meng et al. https://doi.org/10.1016/j.jag.2025.104862
- Climate-Based Estimation of Multi-Cropping Rice Transplanting Dates Using a Geographical Random Convolutional Kernel Transform H. Zhuang et al. https://doi.org/10.3390/agriculture16080852
- Spatial-temporal assessment and optimization of ecological cropland utilization: A case study of China's Huang-Huai-Hai region L. Yang et al. https://doi.org/10.1016/j.eiar.2026.108383
9 citations as recorded by crossref.
- Rice recognition from Sentinel-1 SLC SAR data based on progressive feature screening and fusion S. Tian et al. https://doi.org/10.1016/j.jag.2024.104196
- Mapping Paddy Rice Cropping Intensity and Planting Dates in Monsoon Asia at 20 m Resolution during 2018–2021 from Multi-source Satellite Data Y. Chen et al. https://doi.org/10.34133/remotesensing.1045
- Net Primary Productivity Retrieval Based on ESTARFM Fusion and an Improved CASA Model Y. Cai et al. https://doi.org/10.3390/plants15101436
- A Phenology-Guided Multi-Source Framework for In-Season Rice Mapping in Cloud-Prone and Complex Agroecosystems W. Wang et al. https://doi.org/10.3390/rs18111828
- GTGRI: a Gaussian time-weighted growth rate index for multi-season paddy rice mapping in diverse climates with dense SAR time series D. Liu et al. https://doi.org/10.1080/15481603.2026.2629148
- Estimation of rice yield using multi-source remote sensing data combined with crop growth model and deep learning algorithm J. Lu et al. https://doi.org/10.1016/j.agrformet.2025.110600
- Semi-automatic paddy rice intensity mapping for southern China with multiple cropping systems L. Meng et al. https://doi.org/10.1016/j.jag.2025.104862
- Climate-Based Estimation of Multi-Cropping Rice Transplanting Dates Using a Geographical Random Convolutional Kernel Transform H. Zhuang et al. https://doi.org/10.3390/agriculture16080852
- Spatial-temporal assessment and optimization of ecological cropland utilization: A case study of China's Huang-Huai-Hai region L. Yang et al. https://doi.org/10.1016/j.eiar.2026.108383
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset...
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