Large-scale crop mapping using remote sensing data is of great significance for agricultural production, food security and the sustainable development of human societies. Winter rapeseed is an important oil crop in China that is mainly distributed in the Yangtze River Valley. Traditional winter rapeseed mapping practices are insufficient since they only use the spectral characteristics during the critical phenological period of winter rapeseed, which are usually limited to a small region and cannot meet the needs of large-scale applications. In this study, a novel phenology-based winter rapeseed index (PWRI) was proposed to map winter rapeseed in the Yangtze River Valley. PWRI expands the date window for distinguishing winter rapeseed and winter wheat, and it has good separability throughout the flowering period of winter rapeseed. PWRI also improves the separability of winter rapeseed and winter wheat, which traditionally have been two easily confused winter crops. A PWRI-based method was applied to the Middle Reaches of the Yangtze River Valley to map winter rapeseed on the Google Earth Engine platform. Time series composited Sentinel-2 data were used to map winter rapeseed with 10 m resolution. The mapping achieved a good result with overall accuracy and kappa coefficients exceeding 92% and 0.85, respectively. The PWRI-based method provides a new solution for high spatial resolution winter rapeseed mapping at a large scale.

By employing the unique phenological feature of winter wheat extracted from peak before winter (PBW) and the advantages of moderate resolution imaging spectroradiometer (MODIS) data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data.  First, a phenological window, PBW was drawn from time-series MODIS data.  Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information.  Finally, a regression model was built to model the relationship of the phenological feature and the sample data.  The amount of information of the PBW was evaluated and compared with that of the main peak (MP).  The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data.  These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale.  Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies.  This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.