PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping Efficiency
Paddy rice mapping is critical for food security and environmental management, yet existing methods face challenges such as cloud obstruction in optical data and speckle noise in synthetic aperture radar (SAR). To address these limitations, this study introduces PRICOS, a novel paddy rice index that...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/4/692 |
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| author | Yifeng Lou Gang Yang Weiwei Sun Ke Huang Jingfeng Huang Lihua Wang Weiwei Liu |
| author_facet | Yifeng Lou Gang Yang Weiwei Sun Ke Huang Jingfeng Huang Lihua Wang Weiwei Liu |
| author_sort | Yifeng Lou |
| collection | DOAJ |
| description | Paddy rice mapping is critical for food security and environmental management, yet existing methods face challenges such as cloud obstruction in optical data and speckle noise in synthetic aperture radar (SAR). To address these limitations, this study introduces PRICOS, a novel paddy rice index that systematically combines time series Sentinel-2 optical features (NDVI for bare soil/peak growth, MNDWI for the submerged stages) and Sentinel-1 SAR backscatter (VH polarization for structural dynamics). PRICOS automates key phenological stage detection through harmonic fitting and dynamic thresholding, requiring only 10–20 samples per region to define rice growth cycles. Validated across six agroclimatic regions, PRICOS achieved overall accuracy (OA) and F1 scores of 0.90–0.98, outperforming existing indices like SPRI (OA: 0.79–0.95) and TWDTW (OA: 0.85–0.92). By integrating multi-sensor data with minimal sample dependency, PRICOS provides a robust, adaptable solution for large-scale paddy rice mapping, advancing precision agriculture and climate change mitigation efforts. |
| format | Article |
| id | doaj-art-ec720aa8d9bd47c9bfb0b77dfa9307a3 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ec720aa8d9bd47c9bfb0b77dfa9307a32025-08-20T02:44:47ZengMDPI AGRemote Sensing2072-42922025-02-0117469210.3390/rs17040692PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping EfficiencyYifeng Lou0Gang Yang1Weiwei Sun2Ke Huang3Jingfeng Huang4Lihua Wang5Weiwei Liu6Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaInstitute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaPaddy rice mapping is critical for food security and environmental management, yet existing methods face challenges such as cloud obstruction in optical data and speckle noise in synthetic aperture radar (SAR). To address these limitations, this study introduces PRICOS, a novel paddy rice index that systematically combines time series Sentinel-2 optical features (NDVI for bare soil/peak growth, MNDWI for the submerged stages) and Sentinel-1 SAR backscatter (VH polarization for structural dynamics). PRICOS automates key phenological stage detection through harmonic fitting and dynamic thresholding, requiring only 10–20 samples per region to define rice growth cycles. Validated across six agroclimatic regions, PRICOS achieved overall accuracy (OA) and F1 scores of 0.90–0.98, outperforming existing indices like SPRI (OA: 0.79–0.95) and TWDTW (OA: 0.85–0.92). By integrating multi-sensor data with minimal sample dependency, PRICOS provides a robust, adaptable solution for large-scale paddy rice mapping, advancing precision agriculture and climate change mitigation efforts.https://www.mdpi.com/2072-4292/17/4/692paddy ricesentinel seriestime seriesfeature fusionpaddy rice indexSAR |
| spellingShingle | Yifeng Lou Gang Yang Weiwei Sun Ke Huang Jingfeng Huang Lihua Wang Weiwei Liu PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping Efficiency Remote Sensing paddy rice sentinel series time series feature fusion paddy rice index SAR |
| title | PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping Efficiency |
| title_full | PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping Efficiency |
| title_fullStr | PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping Efficiency |
| title_full_unstemmed | PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping Efficiency |
| title_short | PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping Efficiency |
| title_sort | pricos a robust paddy rice index combining optical and synthetic aperture radar features for improved mapping efficiency |
| topic | paddy rice sentinel series time series feature fusion paddy rice index SAR |
| url | https://www.mdpi.com/2072-4292/17/4/692 |
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