In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery
Sugarcane is a significant crop in terms of annual biomass in the world. Timely and accurate mapping of sugarcane planting is important for food security and sustainability. However, accurately remote-sensing-based mapping sugarcane remains challenging due to two reasons: 1) the scarcity of sugarcan...
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| Format: | Article |
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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/10752341/ |
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| author | Hui Li Liping Di Chen Zhang Li Lin Liying Guo Ruopu Li Haoteng Zhao |
| author_facet | Hui Li Liping Di Chen Zhang Li Lin Liying Guo Ruopu Li Haoteng Zhao |
| author_sort | Hui Li |
| collection | DOAJ |
| description | Sugarcane is a significant crop in terms of annual biomass in the world. Timely and accurate mapping of sugarcane planting is important for food security and sustainability. However, accurately remote-sensing-based mapping sugarcane remains challenging due to two reasons: 1) the scarcity of sugarcane training samples, and 2) the diverse sugarcane fields planting dates. This article proposed a novel transfer learning algorithm for accurate sugarcane planting mapping through space and temporary in the U.S. This algorithm only depended on one place's training data to mapping other places’ growing sugarcane. We distilled burning sugarcane fields as training labels from Sentinel-2 in Palm Beach County and neighboring area in 2021. The time-invariant phenology features were calculated from composite Sentinel-2 normalized difference vegetation index (NDVI) series using linear cosine regression (LCR). They integrated as training samples to construct a one-class support vector machine (OCSVM) classifier, generating Jun.–Nov. growing sugarcane maps in Plam Beach County and Lafourche County 2022. Meanwhile, a postprocess method was used to improve mapping quality. These sugarcane maps were validated using surveyed and observed field dataset and compared against the cropland data layer (CDL). As a result, the sugarcane maps achieved the best accuracy in its mature stage (Sep. 2022), which exhibited nearly complete phenological characteristics. The maps show low misclassification rates and higher accuracy compared to the CDL 2022. Moreover, the LCR-OCSVM method was confirmed to have superior transfer learning capability for sugarcane classification across different regions and periods compared to the Harmonic-OCSVM method. |
| format | Article |
| id | doaj-art-331aa4fc10fd4aa5be48b2e60b8f9f42 |
| institution | OA Journals |
| 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-331aa4fc10fd4aa5be48b2e60b8f9f422025-08-20T02:38:58ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01181410142110.1109/JSTARS.2024.349765310752341In-Season Mapping of Sugarcane Planting Based on Sentinel-2 ImageryHui Li0https://orcid.org/0000-0001-6547-888XLiping Di1https://orcid.org/0000-0002-3953-9965Chen Zhang2https://orcid.org/0000-0002-8990-2267Li Lin3https://orcid.org/0000-0002-7753-2270Liying Guo4https://orcid.org/0000-0001-9684-0204Ruopu Li5https://orcid.org/0000-0003-3500-0273Haoteng Zhao6Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USASchool of Earth Systems and Sustainability, South Illinois University, Carbondale, IL, USAHydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Beltsville, MD, USASugarcane is a significant crop in terms of annual biomass in the world. Timely and accurate mapping of sugarcane planting is important for food security and sustainability. However, accurately remote-sensing-based mapping sugarcane remains challenging due to two reasons: 1) the scarcity of sugarcane training samples, and 2) the diverse sugarcane fields planting dates. This article proposed a novel transfer learning algorithm for accurate sugarcane planting mapping through space and temporary in the U.S. This algorithm only depended on one place's training data to mapping other places’ growing sugarcane. We distilled burning sugarcane fields as training labels from Sentinel-2 in Palm Beach County and neighboring area in 2021. The time-invariant phenology features were calculated from composite Sentinel-2 normalized difference vegetation index (NDVI) series using linear cosine regression (LCR). They integrated as training samples to construct a one-class support vector machine (OCSVM) classifier, generating Jun.–Nov. growing sugarcane maps in Plam Beach County and Lafourche County 2022. Meanwhile, a postprocess method was used to improve mapping quality. These sugarcane maps were validated using surveyed and observed field dataset and compared against the cropland data layer (CDL). As a result, the sugarcane maps achieved the best accuracy in its mature stage (Sep. 2022), which exhibited nearly complete phenological characteristics. The maps show low misclassification rates and higher accuracy compared to the CDL 2022. Moreover, the LCR-OCSVM method was confirmed to have superior transfer learning capability for sugarcane classification across different regions and periods compared to the Harmonic-OCSVM method.https://ieeexplore.ieee.org/document/10752341/Burning sugarcanelinear cosine regressionone-class support vector machinesugarcane mapping |
| spellingShingle | Hui Li Liping Di Chen Zhang Li Lin Liying Guo Ruopu Li Haoteng Zhao In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Burning sugarcane linear cosine regression one-class support vector machine sugarcane mapping |
| title | In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery |
| title_full | In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery |
| title_fullStr | In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery |
| title_full_unstemmed | In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery |
| title_short | In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery |
| title_sort | in season mapping of sugarcane planting based on sentinel 2 imagery |
| topic | Burning sugarcane linear cosine regression one-class support vector machine sugarcane mapping |
| url | https://ieeexplore.ieee.org/document/10752341/ |
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