Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery

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dc.creatorFikriyah, Vidya Nahdhiyatul
dc.creatorDarvishzadeh, Roshanak
dc.creatorLaborte, Alice
dc.creatorNelson, Andrew
dc.date2025-04
dc.date2025-08-04T03:02:05Z
dc.date2025-08-04T03:02:05Z
dc.date.accessioned2026-06-27T04:07:26Z
dc.descriptionRice 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.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/175944
dc.identifier.urihttp://hdl.handle.net/123456789/22308
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceFikriyah, 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.
dc.subjectcrop yield
dc.subjectrice
dc.subjectratooning
dc.subjectremote sensing
dc.subjectsynthetic aperture radar
dc.subjectoptical sensors
dc.subjectclouds
dc.subjectmapping
dc.subjectcrop monitoring
dc.subjectprecision agriculture
dc.subjectfield experimentation
dc.titleRatoon rice mapping based on Sentinel-1 and Sentinel-2 imagery
dc.typeJournal Article

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Instituto Internacional de Investigación del Arroz (IRRI)