{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T14:55:07Z","timestamp":1780584907822,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M662478"],"award-info":[{"award-number":["2019M662478"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs14051113","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4567-2313","authenticated-orcid":false,"given":"Haifeng","family":"Tian","sequence":"first","affiliation":[{"name":"International Joint Laboratory of Geospatial Technology of Henan Province\/College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"International Joint Laboratory of Geospatial Technology of Henan Province\/College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiangzi","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuyi","family":"Mei","sequence":"additional","affiliation":[{"name":"International Joint Laboratory of Geospatial Technology of Henan Province\/College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"International Joint Laboratory of Geospatial Technology of Henan Province\/College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengdan","family":"Yang","sequence":"additional","affiliation":[{"name":"International Joint Laboratory of Geospatial Technology of Henan Province\/College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjiu","family":"Wang","sequence":"additional","affiliation":[{"name":"International Joint Laboratory of Geospatial Technology of Henan Province\/College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6962-8838","authenticated-orcid":false,"given":"Yaochen","family":"Qin","sequence":"additional","affiliation":[{"name":"International Joint Laboratory of Geospatial Technology of Henan Province\/College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"},{"name":"Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2016.06.016","article-title":"Spectral considerations for modeling yield of canola","volume":"184","author":"Sulik","year":"2016","journal-title":"Remote Sens. 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