Abstract
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.
Highlights
Canola (Brassica napus L.) is one of the major oilseed crops worldwide
To verify whether the optimal canola flower index (CFI) enhances the image features of the canola compared to Sentinel-2 raw images, canola was extracted based on the Sentinel-2 raw images and the optimal CFI image derived from the Sentinel-2 raw images
Because IsoData is hardly affected by subjective human factors, and classification is automatically done by computers based on the characteristics of the image itself, the classification results from IsoData could determine to some extent whether the optimal CFI images were better than the raw images
Summary
Canola (Brassica napus L.) is one of the major oilseed crops worldwide. It is the primary source of edible oil for human consumption and a biological feedstock for fuels [1]. Canola flowers are an important tourism resource, promoting the local development of tourism agriculture [2,3]. The production and consumption of canola are unbalanced in different world regions. China’s domestic canola production is far less than its increasing demand for canola [2]. Mapping and tracking the unbalance in canola planting areas is of great importance for agricultural management and food security
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