Identifying Winter Wheat Using Landsat Data Based on Deep Learning Algorithms in the North China Plain
Qixia Zhang, Guofu Wang, Guofu Wang, Guojie Wang, Guojie Wang, Weichen Song, Xikun Wei, Yifan Hu
Abstract
The North China Plain (NCP) represents a significant agricultural production region in China, with winter wheat serving as one of its main grain crops. Accurate identification of winter wheat through remote sensing technology holds significant importance in ensuring food security in the NCP. In this study, we have utilized Landsat 8 and Landsat 9 imagery to identify winter wheat in the NCP. Multiple convolutional neural networks (CNNs) and transformer networks, including ResNet, HRNet, MobileNet, Xception, Swin Transformer and SegFormer, are used in order to understand their uncertainties in identifying winter wheat. At the same time, these deep learning (DL) methods are also compared to the traditional random forest (RF) method. The results indicated that SegFormer outperformed all methods, of which the accuracy is 0.9252, the mean intersection over union (mIoU) is 0.8194 and the F1 score (F1) is 0.8459. These DL methods were then applied to monitor the winter wheat planting areas in the NCP from 2013 to 2022, and the results showed a decreasing trend.