Mapping winter wheat in Kaifeng, China using Sentinel-1A time-series images

Crop planting area mapping is essential for crop phenology monitoring, yield prediction, and disaster prevention. In this study, a winter wheat identification method combining Markov Random Field and Spectral Similarity Measure (MRF-SSM) is proposed by using Sentinel-1 A time-series images. It is found that compared with VH polarization, the backscattering coefficient of winter wheat at VV polarization fluctuates more at all growth stages and is used for winter wheat mapping. The result shows that the precision of mapping winter wheat using the MRF-SSM is 89.62% which is higher than using the support vector machine (SVM) and random forest (RF) methods. Because winter wheat near towns can be accurately identified using MRF-SSM methods. Moreover, the MRF-SSM method has the advantages of fewer winter wheat samples and less computation time. Therefore, time-series Sentinel-1A images with MRF-SSM have great potential for mapping winter wheat or other crops.

Automated summary

This study proposes a winter wheat identification method combining Markov Random Field and Spectral Similarity Measure (MRF-SSM) using Sentinel-1A time-series images. The results show that the accuracy of mapping winter wheat using the MRF-SSM method is higher than using support vector machine and random forest methods, and it can accurately identify winter wheat near towns.