Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery
Rice ratooning has gained increasing interest in Asia as a way to boost rice production by allowing two rice harvests from a single growing season. Accurate mapping of this practice can improve rice production estimates. However, current efforts have mainly relied on optical sensors, which are limit...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Elsevier
2025
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| Acceso en línea: | https://hdl.handle.net/10568/175944 |
| _version_ | 1855514002546753536 |
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| author | Fikriyah, Vidya Nahdhiyatul Darvishzadeh, Roshanak Laborte, Alice Nelson, Andrew |
| author_browse | Darvishzadeh, Roshanak Fikriyah, Vidya Nahdhiyatul Laborte, Alice Nelson, Andrew |
| author_facet | Fikriyah, Vidya Nahdhiyatul Darvishzadeh, Roshanak Laborte, Alice Nelson, Andrew |
| author_sort | Fikriyah, Vidya Nahdhiyatul |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Rice ratooning has gained increasing interest in Asia as a way to boost rice production by allowing two rice harvests from a single growing season. Accurate mapping of this practice can improve rice production estimates. However, current efforts have mainly relied on optical sensors, which are limited by cloud cover, especially during the wet season when ratooning is common. This study systematically assessed the use of optical Sentinel-2, Synthetic Aperture Radar (SAR) Sentinel-1 data and their combination to map ratoon rice crops. Field data were collected in four provinces of the Philippines in 2018–19. Backscatter intensity from Sentinel-1, spectral information, and six commonly used vegetation indices (VIs) from Sentinel-2 were analysed using the Mann-Whitney Usignificance test to examine differences between the main and ratoon rice crops. Next, we compared the classification performance of decision tree (DT), support vector machine (SVM), and random forest (RF) classifiers. Results show that ratoon and main rice crop significantly differed in VV and VH polarisations, red edge and near-infrared bands, and all VIs. The highest accuracy was achieved with selected features in an RF classifier (overall accuracy of 92 %), compared to SVM (87 %) and DT (81 %). Classification using features from both Sentinel-1 and 2 consistently yielded higher accuracy than using features from one sensor alone. The total planting of ratoon rice was estimated at approximately 223 km2 (±4 % of the wet season rice area). This study demonstrates the value of combining SAR Sentinel-1 and optical Sentinel-2 for ratoon rice mapping. |
| format | Journal Article |
| id | CGSpace175944 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1759442025-12-08T10:06:44Z Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery Fikriyah, Vidya Nahdhiyatul Darvishzadeh, Roshanak Laborte, Alice Nelson, Andrew crop yield rice ratooning remote sensing synthetic aperture radar optical sensors clouds mapping crop monitoring precision agriculture field experimentation Rice ratooning has gained increasing interest in Asia as a way to boost rice production by allowing two rice harvests from a single growing season. Accurate mapping of this practice can improve rice production estimates. However, current efforts have mainly relied on optical sensors, which are limited by cloud cover, especially during the wet season when ratooning is common. This study systematically assessed the use of optical Sentinel-2, Synthetic Aperture Radar (SAR) Sentinel-1 data and their combination to map ratoon rice crops. Field data were collected in four provinces of the Philippines in 2018–19. Backscatter intensity from Sentinel-1, spectral information, and six commonly used vegetation indices (VIs) from Sentinel-2 were analysed using the Mann-Whitney Usignificance test to examine differences between the main and ratoon rice crops. Next, we compared the classification performance of decision tree (DT), support vector machine (SVM), and random forest (RF) classifiers. Results show that ratoon and main rice crop significantly differed in VV and VH polarisations, red edge and near-infrared bands, and all VIs. The highest accuracy was achieved with selected features in an RF classifier (overall accuracy of 92 %), compared to SVM (87 %) and DT (81 %). Classification using features from both Sentinel-1 and 2 consistently yielded higher accuracy than using features from one sensor alone. The total planting of ratoon rice was estimated at approximately 223 km2 (±4 % of the wet season rice area). This study demonstrates the value of combining SAR Sentinel-1 and optical Sentinel-2 for ratoon rice mapping. 2025-04 2025-08-04T03:02:05Z 2025-08-04T03:02:05Z Journal Article https://hdl.handle.net/10568/175944 en Open Access application/pdf Elsevier Fikriyah, Vidya Nahdhiyatul, Roshanak Darvishzadeh, Alice Laborte, and Andrew Nelson. "Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery." Remote Sensing Applications: Society and Environment (2025): 101592. |
| spellingShingle | crop yield rice ratooning remote sensing synthetic aperture radar optical sensors clouds mapping crop monitoring precision agriculture field experimentation Fikriyah, Vidya Nahdhiyatul Darvishzadeh, Roshanak Laborte, Alice Nelson, Andrew Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery |
| title | Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery |
| title_full | Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery |
| title_fullStr | Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery |
| title_full_unstemmed | Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery |
| title_short | Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery |
| title_sort | ratoon rice mapping based on sentinel 1 and sentinel 2 imagery |
| topic | crop yield rice ratooning remote sensing synthetic aperture radar optical sensors clouds mapping crop monitoring precision agriculture field experimentation |
| url | https://hdl.handle.net/10568/175944 |
| work_keys_str_mv | AT fikriyahvidyanahdhiyatul ratoonricemappingbasedonsentinel1andsentinel2imagery AT darvishzadehroshanak ratoonricemappingbasedonsentinel1andsentinel2imagery AT labortealice ratoonricemappingbasedonsentinel1andsentinel2imagery AT nelsonandrew ratoonricemappingbasedonsentinel1andsentinel2imagery |