A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery

被引:41
|
作者
Tian, Haifeng [1 ,2 ,3 ]
Chen, Ting [1 ]
Li, Qiangzi [4 ]
Mei, Qiuyi [1 ]
Wang, Shuai [1 ]
Yang, Mengdan [1 ]
Wang, Yongjiu [1 ]
Qin, Yaochen [1 ,5 ,6 ]
机构
[1] Henan Univ, Int Joint Lab Geospatial Technol Henan Prov, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China
[2] Henan Univ, Key Lab Geospatial Technol Middle & Lower Yellow, Minist Educ, Kaifeng 475004, Peoples R China
[3] Henan Univ, Henan Key Lab Earth Syst Observat & Modeling, Kaifeng 475004, Peoples R China
[4] Beijing Normal Univ, Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China
[5] Henan Univ, Key Res Inst Yellow River Civilizat & Sustainable, Henan Prov & Minist Educ, Kaifeng 475001, Peoples R China
[6] Henan Univ, Collaborat Innovat Ctr Yellow River Civilizat Joi, Henan Prov & Minist Educ, Kaifeng 475001, Peoples R China
基金
中国博士后科学基金;
关键词
automatic mapping; canola flower index; remote sensing; Sentinel-2; winter wheat; TIME-SERIES; SPATIAL-RESOLUTION; LAND; CROP; CLASSIFICATION; RAPESEED; WHEAT; LEAF;
D O I
10.3390/rs14051113
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:18
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