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] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 191972672 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Identifying Paddy Rice Fields in the U.S. from the Operational VIIRS Flood Products. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Tianshu%22">Yang, Tianshu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kalluri%2C+Satya%22">Kalluri, Satya</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lomax%2C+Andrew%22">Lomax, Andrew</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Donglian%22">Sun, Donglian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> dsun@gmu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2026, Vol. 18 Issue 4, p587. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Paddy+fields%22">Paddy fields</searchLink><br /><searchLink fieldCode="DE" term="%22Trend+analysis%22">Trend analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+phenology%22">Plant phenology</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+statistics%22">Agricultural statistics</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18040587 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 587 Subjects: – SubjectFull: Paddy fields Type: general – SubjectFull: Trend analysis Type: general – SubjectFull: Remote-sensing images Type: general – SubjectFull: Plant phenology Type: general – SubjectFull: Agricultural statistics Type: general – SubjectFull: United States Type: general Titles: – TitleFull: Identifying Paddy Rice Fields in the U.S. from the Operational VIIRS Flood Products. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Tianshu – PersonEntity: Name: NameFull: Kalluri, Satya – PersonEntity: Name: NameFull: Lomax, Andrew – PersonEntity: Name: NameFull: Sun, Donglian IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 4 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
