A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery
Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/5/1113 |
| _version_ | 1797473955560292352 |
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| author | Haifeng Tian Ting Chen Qiangzi Li Qiuyi Mei Shuai Wang Mengdan Yang Yongjiu Wang Yaochen Qin |
| author_facet | Haifeng Tian Ting Chen Qiangzi Li Qiuyi Mei Shuai Wang Mengdan Yang Yongjiu Wang Yaochen Qin |
| author_sort | Haifeng Tian |
| collection | DOAJ |
| description | Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction. |
| first_indexed | 2024-03-09T20:23:30Z |
| format | Article |
| id | doaj.art-a42e32d1ca27435c928819d3b89391b6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T20:23:30Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a42e32d1ca27435c928819d3b89391b62023-11-23T23:41:53ZengMDPI AGRemote Sensing2072-42922022-02-01145111310.3390/rs14051113A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 ImageryHaifeng Tian0Ting Chen1Qiangzi Li2Qiuyi Mei3Shuai Wang4Mengdan Yang5Yongjiu Wang6Yaochen Qin7International Joint Laboratory of Geospatial Technology of Henan Province/College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaInternational Joint Laboratory of Geospatial Technology of Henan Province/College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, ChinaInternational Joint Laboratory of Geospatial Technology of Henan Province/College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaInternational Joint Laboratory of Geospatial Technology of Henan Province/College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaInternational Joint Laboratory of Geospatial Technology of Henan Province/College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaInternational Joint Laboratory of Geospatial Technology of Henan Province/College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaInternational Joint Laboratory of Geospatial Technology of Henan Province/College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaBecause canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction.https://www.mdpi.com/2072-4292/14/5/1113automatic mappingcanola flower indexremote sensingSentinel-2winter wheat |
| spellingShingle | Haifeng Tian Ting Chen Qiangzi Li Qiuyi Mei Shuai Wang Mengdan Yang Yongjiu Wang Yaochen Qin A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery Remote Sensing automatic mapping canola flower index remote sensing Sentinel-2 winter wheat |
| title | A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery |
| title_full | A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery |
| title_fullStr | A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery |
| title_full_unstemmed | A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery |
| title_short | A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery |
| title_sort | novel spectral index for automatic canola mapping by using sentinel 2 imagery |
| topic | automatic mapping canola flower index remote sensing Sentinel-2 winter wheat |
| url | https://www.mdpi.com/2072-4292/14/5/1113 |
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