Early mapping of winter wheat in Henan province of China using time series of Sentinel-2 data

Accurate mapping of winter wheat in its early stages is crucial for crop growth monitoring and crop yield forecasting. However, early mapping of winter wheat using remotely sensed data is challenging because remote sensing observations can only be used for a part of the growth period. In this study, a framework was proposed for early season mapping of winter wheat using spectral and temporal information of Sentinel-2 images. First, time series of temporal and spectral features were derived using Whittaker smoothing. Subsequently, sensitivities of different parameters (i.e. input features, time interval, and length of time-series data) to early mapping were analyzed. Finally, early maps of winter wheat were generated based on optimal parameters. Results show that the earliest identifiable timing was delayed as the time interval of the time series increased. Winter wheat can be mapped in the early overwintering period (5 months before harvest) with an overall accuracy of 0.91, which is comparable to that of post-season mapping (0.94). In addition, the misclassification in early mapping was caused by uneven sample spatial patterns, natural conditions, and planting management; however, most errors can be gradually amended during the green-up and jointing periods, and the overall accuracy remained stable after the jointing stage. This study demonstrates that it is feasible to implement large-scale early mapping of winter wheat using satellite observations. The proposed approach potentially provides a reference for early mapping of other crop types in agricultural regions worldwide.

Automated summary

Accurate mapping of winter wheat in its early stages is crucial for crop growth monitoring and yield forecasting. This study proposes a framework using spectral and temporal information of Sentinel-2 images for early season mapping of winter wheat. Results show that winter wheat can be mapped accurately in the early overwintering period, with overall accuracy comparable to post-season mapping.