Identifying Paddy Rice Fields in the U.S. from the Operational VIIRS Flood Products.
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| Title: | Identifying Paddy Rice Fields in the U.S. from the Operational VIIRS Flood Products. |
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| Authors: | Yang, Tianshu1 (AUTHOR), Kalluri, Satya2 (AUTHOR), Lomax, Andrew1,2 (AUTHOR), Sun, Donglian1,2 (AUTHOR) dsun@gmu.edu |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 4, p587. 20p. |
| Subjects: | Paddy fields, Trend analysis, Remote-sensing images, Plant phenology, Agricultural statistics |
| Geographic Terms: | United States |
| Abstract: | Highlights: What are the main findings? Extensive analyses are conducted on time series VIIRS flood data to identify paddy rice fields based on the duration of water presence, and combined with rice calendar data. For the first time, the Mann–Kendall analysis is applied to the VIIRS flood product time series to identify paddy rice fields and is compared with methods based on water presence and regression analysis. What is the implication of the main finding? It is expected that this study can help reduce the false positives in optical sensor-based flood products to improve real hazardous flood detection. Operational satellite-based flood products are generated by comparing water classification maps from satellite imagery with permanent or normal water masks. This approach may misclassify some water bodies—such as irrigated paddy rice fields—as floodwaters because they are not masked as permanent or normal water sources. Due to the importance of paddy fields for food security, in this study, methodologies based on the long-time duration of water presence combined with paddy rice phenological algorithms and change detection analysis are developed to extract paddy rice fields from the operational VIIRS (Visible Infrared Imaging Radiometer Suite) flood products. This method is also compared with the regression analysis and the Mann–Kendall analysis. Evaluations are performed through confusion matrix analysis by comparing with the USDA rice data. The three paddy rice extraction algorithms show good agreement and can achieve an accuracy of 93% with an F1-score exceeding 80%. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Extensive analyses are conducted on time series VIIRS flood data to identify paddy rice fields based on the duration of water presence, and combined with rice calendar data. For the first time, the Mann–Kendall analysis is applied to the VIIRS flood product time series to identify paddy rice fields and is compared with methods based on water presence and regression analysis. What is the implication of the main finding? It is expected that this study can help reduce the false positives in optical sensor-based flood products to improve real hazardous flood detection. Operational satellite-based flood products are generated by comparing water classification maps from satellite imagery with permanent or normal water masks. This approach may misclassify some water bodies—such as irrigated paddy rice fields—as floodwaters because they are not masked as permanent or normal water sources. Due to the importance of paddy fields for food security, in this study, methodologies based on the long-time duration of water presence combined with paddy rice phenological algorithms and change detection analysis are developed to extract paddy rice fields from the operational VIIRS (Visible Infrared Imaging Radiometer Suite) flood products. This method is also compared with the regression analysis and the Mann–Kendall analysis. Evaluations are performed through confusion matrix analysis by comparing with the USDA rice data. The three paddy rice extraction algorithms show good agreement and can achieve an accuracy of 93% with an F1-score exceeding 80%. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18040587 |
