An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on Cloud Coverage
Timely and accurate mapping of paddy rice cultivation is crucial for estimating rice production and optimizing land utilization. Optical images are essential data source for paddy rice mapping, but it is susceptible to cloud contamination. Existing methods struggle to effectively utilize clear-sky p...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10836781/ |
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| author | Xueqin Jiang Song Gao Huaqiang Du Shenghui Fang Yan Gong Ning Han Yirong Wang |
| author_facet | Xueqin Jiang Song Gao Huaqiang Du Shenghui Fang Yan Gong Ning Han Yirong Wang |
| author_sort | Xueqin Jiang |
| collection | DOAJ |
| description | Timely and accurate mapping of paddy rice cultivation is crucial for estimating rice production and optimizing land utilization. Optical images are essential data source for paddy rice mapping, but it is susceptible to cloud contamination. Existing methods struggle to effectively utilize clear-sky pixel information in optical images containing clouds, which impacts the accuracy of paddy rice mapping under cloudy conditions. To address the abovementioned problems, we propose an automatic decision-level fusion rice mapping method of optical and synthetic aperture radar (SAR) images based on cloud coverage (the Auto-OSDF method). The method effectively utilizes clear-sky pixels in images containing clouds and leverages the advantages of SAR features in heavily clouded regions. We tested and validated the Auto-OSDF method in Xiangyin County, Hunan Province, and analyzed the impact of different cloud coverage levels (10%–50%) on the accuracy of rice mapping based on this method. The results indicate that, as cloud coverage increases, the rice mapping accuracy of the Auto-OSDF method is not significantly affected, with overall accuracy and Kappa coefficients both above 93% and 0.90, respectively. To show the value of the proposed method in large-scale applications, we further mapped paddy rice in the entire Hunan Province, and the overall accuracy and Kappa coefficient were 92.47% and 0.87, respectively. The results obtained by the Auto-OSDF method show an average R<sup>2</sup> of 0.926 compared to municipal-level statistical planting areas. The abovementioned study demonstrates that the Auto-OSDF method is capable of achieving stable and high-precision rice mapping under cloud contamination interference. |
| format | Article |
| id | doaj-art-55d3d2ec2c0d4ef5ae8376e169e057eb |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-55d3d2ec2c0d4ef5ae8376e169e057eb2025-02-11T00:00:29ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01185018503210.1109/JSTARS.2025.352812410836781An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on Cloud CoverageXueqin Jiang0https://orcid.org/0009-0001-3863-0580Song Gao1https://orcid.org/0009-0001-2516-5632Huaqiang Du2https://orcid.org/0009-0003-8999-4290Shenghui Fang3https://orcid.org/0000-0002-2646-6090Yan Gong4https://orcid.org/0000-0002-8605-6683Ning Han5Yirong Wang6https://orcid.org/0009-0009-5435-7252State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou, ChinaCollege of Civil Engineering, Hunan University, Changsha, ChinaState Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaState Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou, ChinaState Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou, ChinaTimely and accurate mapping of paddy rice cultivation is crucial for estimating rice production and optimizing land utilization. Optical images are essential data source for paddy rice mapping, but it is susceptible to cloud contamination. Existing methods struggle to effectively utilize clear-sky pixel information in optical images containing clouds, which impacts the accuracy of paddy rice mapping under cloudy conditions. To address the abovementioned problems, we propose an automatic decision-level fusion rice mapping method of optical and synthetic aperture radar (SAR) images based on cloud coverage (the Auto-OSDF method). The method effectively utilizes clear-sky pixels in images containing clouds and leverages the advantages of SAR features in heavily clouded regions. We tested and validated the Auto-OSDF method in Xiangyin County, Hunan Province, and analyzed the impact of different cloud coverage levels (10%–50%) on the accuracy of rice mapping based on this method. The results indicate that, as cloud coverage increases, the rice mapping accuracy of the Auto-OSDF method is not significantly affected, with overall accuracy and Kappa coefficients both above 93% and 0.90, respectively. To show the value of the proposed method in large-scale applications, we further mapped paddy rice in the entire Hunan Province, and the overall accuracy and Kappa coefficient were 92.47% and 0.87, respectively. The results obtained by the Auto-OSDF method show an average R<sup>2</sup> of 0.926 compared to municipal-level statistical planting areas. The abovementioned study demonstrates that the Auto-OSDF method is capable of achieving stable and high-precision rice mapping under cloud contamination interference.https://ieeexplore.ieee.org/document/10836781/Cloud coveragedecision-level fusionoptical and synthetic aperture radar (SAR) imagespaddy rice mappingtime series |
| spellingShingle | Xueqin Jiang Song Gao Huaqiang Du Shenghui Fang Yan Gong Ning Han Yirong Wang An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on Cloud Coverage IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Cloud coverage decision-level fusion optical and synthetic aperture radar (SAR) images paddy rice mapping time series |
| title | An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on Cloud Coverage |
| title_full | An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on Cloud Coverage |
| title_fullStr | An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on Cloud Coverage |
| title_full_unstemmed | An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on Cloud Coverage |
| title_short | An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on Cloud Coverage |
| title_sort | automatic decision level fusion rice mapping method of optical and sar images based on cloud coverage |
| topic | Cloud coverage decision-level fusion optical and synthetic aperture radar (SAR) images paddy rice mapping time series |
| url | https://ieeexplore.ieee.org/document/10836781/ |
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