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

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.

Remote sensing identification of early planting information of rapeseed in mountainous areas

“…We used the ground truth data described in Section 2.3 as a validation sample to assess the accuracy of rapeseed mapping. For the validation sample, we followed the non-proportional sampling allocation suggested by Olofsson et al [28] to increase the confidence in the accuracy assessment. The performance of the rapeseed mapping method was validated using the confusion matrix accuracy validation method and the F1 score.…”

Evaluating the Accuracy and Spatial Agreement of Five Global Land Cover Datasets in the Ecologically Vulnerable South China Karst

“…In this paper, it was found that the accuracy of high-resolution LC datasets is better than low-resolution datasets. High-resolution image data can finely distinguish spectral features among ground objects, and more fine-scale information can be obtained [61]. Accordingly, high-resolution datasets are more advantageous than low-resolution datasets.…”

Winter Wheat Yield Estimation Based on Optimal Weighted Vegetation Index and BHT-ARIMA Model

“…Details of the bands used in this study can be found in Table 2. All bands were atmospherically corrected using the Sen2Cor processor tool, and the bands at 20 m resolution were rescaled to 10 m before being stacked [18,42]. In order to quickly and effectively select the optimal VIs suitable for winter wheat yield estimation and their importance ranking from the VI candidates, the RReliefF algorithm [43], as an extended variant of Relief [44], was adopted to screen the optimal VIs with high efficiency.…”