Articles | Volume 15, issue 4
https://doi.org/10.5194/essd-15-1501-2023
© 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-15-1501-2023
© 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
| 
04 Apr 2023
Data description paper |
| 04 Apr 2023
| 04 Apr 2023
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
Chunling Sun
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 100049, China
Hong Zhang
CORRESPONDING AUTHOR
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 100049, China
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Ji Ge
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 100049, China
Jingling Jiang
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 100049, China
Lijun Zuo
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
Chao Wang
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of
Sciences, Beijing 100049, China
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Cited
17 citations as recorded by crossref.
- Integrating Sentinel-1 data and machine learning for effective paddy field monitoring in Cauvery Delta Zone, Tamil Nadu, India J. Niraimathi & S. Saravanan 10.1007/s10661-024-13487-0
- Decline in Planting Areas of Double-Season Rice by Half in Southern China over the Last Two Decades W. Zhu et al. 10.3390/rs16030440
- A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment H. Fang et al. 10.1016/j.srs.2024.100172
- ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China H. Li et al. 10.5194/essd-16-1689-2024
- Plant disease mapping in paddy growing stages using remotely sensed data M. Safari & A. Malian 10.1007/s12665-024-11991-7
- Large-scale and high-resolution paddy rice intensity mapping using downscaling and phenology-based algorithms on Google Earth Engine L. Meng et al. 10.1016/j.jag.2024.103725
- Challenges in the evaluation of earth observation products: Accuracy assessment case study using convolutional neural networks T. Prantl et al. 10.1016/j.rsase.2024.101420
- Mapping Ratoon Rice Fields Based on SAR Time Series and Phenology Data in Cloudy Regions Y. Li et al. 10.3390/rs16152703
- Large-scale rice mapping under spatiotemporal heterogeneity using multi-temporal SAR images and explainable deep learning J. Ge et al. 10.1016/j.isprsjprs.2024.12.021
- 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
- Sample-free automated mapping of double-season rice in China using Sentinel-1 SAR imagery X. Zhang et al. 10.3389/fenvs.2023.1207882
- Full cycle rice growth monitoring with dual-pol SAR data and interpretable deep learning J. Ge et al. 10.1080/17538947.2024.2445639
- Recognition of multi-season rice in a complex tropical agronomy zone using time-series SAR data: a case study of Hainan, China L. Xu et al. 10.1080/2150704X.2024.2313610
- An Optimized Semi-Supervised Generative Adversarial Network Rice Extraction Method Based on Time-Series Sentinel Images L. Du et al. 10.3390/agriculture14091505
- Spatial domain transfer: Cross-regional paddy rice mapping with a few samples based on Sentinel-1 and Sentinel-2 data on GEE L. Sun et al. 10.1016/j.jag.2024.103762
- Cropland Data Extraction in Mekong Delta Based on Time Series Sentinel-1 Dual-Polarized Data J. Jiang et al. 10.3390/rs15123050
- Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data C. Sun et al. 10.5194/essd-15-1501-2023
16 citations as recorded by crossref.
- Integrating Sentinel-1 data and machine learning for effective paddy field monitoring in Cauvery Delta Zone, Tamil Nadu, India J. Niraimathi & S. Saravanan 10.1007/s10661-024-13487-0
- Decline in Planting Areas of Double-Season Rice by Half in Southern China over the Last Two Decades W. Zhu et al. 10.3390/rs16030440
- A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment H. Fang et al. 10.1016/j.srs.2024.100172
- ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China H. Li et al. 10.5194/essd-16-1689-2024
- Plant disease mapping in paddy growing stages using remotely sensed data M. Safari & A. Malian 10.1007/s12665-024-11991-7
- Large-scale and high-resolution paddy rice intensity mapping using downscaling and phenology-based algorithms on Google Earth Engine L. Meng et al. 10.1016/j.jag.2024.103725
- Challenges in the evaluation of earth observation products: Accuracy assessment case study using convolutional neural networks T. Prantl et al. 10.1016/j.rsase.2024.101420
- Mapping Ratoon Rice Fields Based on SAR Time Series and Phenology Data in Cloudy Regions Y. Li et al. 10.3390/rs16152703
- Large-scale rice mapping under spatiotemporal heterogeneity using multi-temporal SAR images and explainable deep learning J. Ge et al. 10.1016/j.isprsjprs.2024.12.021
- 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
- Sample-free automated mapping of double-season rice in China using Sentinel-1 SAR imagery X. Zhang et al. 10.3389/fenvs.2023.1207882
- Full cycle rice growth monitoring with dual-pol SAR data and interpretable deep learning J. Ge et al. 10.1080/17538947.2024.2445639
- Recognition of multi-season rice in a complex tropical agronomy zone using time-series SAR data: a case study of Hainan, China L. Xu et al. 10.1080/2150704X.2024.2313610
- An Optimized Semi-Supervised Generative Adversarial Network Rice Extraction Method Based on Time-Series Sentinel Images L. Du et al. 10.3390/agriculture14091505
- Spatial domain transfer: Cross-regional paddy rice mapping with a few samples based on Sentinel-1 and Sentinel-2 data on GEE L. Sun et al. 10.1016/j.jag.2024.103762
- Cropland Data Extraction in Mekong Delta Based on Time Series Sentinel-1 Dual-Polarized Data J. Jiang et al. 10.3390/rs15123050
Latest update: 14 Jan 2025
Short summary
Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a...
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