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

ABSTRACT

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.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [42101386,61871175]; Plan of Science and Technology of Kaifeng City [2102005]; College Key Research Project of Henan Province [21A520004,22A520021]; Plan of Science and Technology of Henan Province [202102210175,212102210093,222102110439].