Accurate mapping of winter wheat over a large area is of great significance for guiding policy formulation related to food security, farmland management, and the international food trade. Due to the complex phenological features of winter wheat, the cloud contamination in time-series imagery, and the influence of the soil/snow background on vegetation indices, there remains no effective method for mapping winter wheat at a medium spatial resolution (10–30 m). In this study, we proposed a novel method called phenology-time weighted dynamic time warping (PT-DTW) for identifying winter wheat based on Sentinel 2A/B time-series data. The main advantages of PT-DTW include (1) the use of phenological features in two periods, i.e., the greenness increase before winter and greenness decrease after heading, which are common to all winter wheat and are distinct from the features of other land cover types, and (2) the use of the normalized differential phenology index (NDPI) instead of traditional vegetation indices to provide more robust vegetation information and to suppress the adverse impacts of soil and snow cover, especially during the before-winter growth period. The proposed PT-DTW method was employed for winter wheat mapping based on Sentinel 2A/B data on the Huang-Huai Plain, China. Validation with visually interpreted samples showed that the produced winter wheat map achieved an overall classification accuracy of 89.98% and a kappa coefficient of 0.7978, outperforming previous winter wheat classification methods. Moreover, the planting area derived from PT-DTW agreed well with census data at the municipal level, with a coefficient of determination of 0.8638, indicating that the winter wheat map produced at 20 m resolution was reliable overall. Therefore, the PT-DTW method is recommended for winter wheat mapping over large areas.

Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yield. Thus, yellowness indices were previously used to detect the canola flower using aerial imagery or median-resolution satellite data like Sentinel-2. However, it remains challenging to map the canola planting area and to trace long-term canola yields in China due to the wide areal extent of cultivation, different flowering periods in different locations and years, and the lack of high spatial resolution data within a long-term period. In this study, a novel canola index, called the enhanced area yellowness index (EAYI), for mapping canola flowers and based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, was developed. There are two improvements in the EAYI compared with previous studies. First, a method for estimating flowering period, based on geolocation and normalized difference vegetation index (NDVI) time-series, was established, to estimate the flowering period at each place in each year. Second, the EAYI enhances the weak flower signal in coarse pixels by combining the peak of yellowness index time-series and the valley of NDVI time-series during the estimated flowering period. With the proposed EAYI, canola flowering was mapped in five typical canola planting areas in China, during 2003-2017. Three different canola indices proposed previously, the normalized difference yellowness index (NDYI), ratio yellowness index (RYI) and Ashourloo canola index (Ashourloo CI), were also calculated for a comparison. Validation using the samples interpreted through higher resolution images demonstrated that the EAYI is better correlated with the reference canola coverage with R² ranged from 0.31 to 0.70, compared to the previous indices with R² ranged from 0.02 to 0.43. Compared with census canola yield data, the total EAYI was well correlated with actual yield in Jingmen, Yili and Hulun Buir, and well correlated with meteorological yields in all five study areas. In contrast, previous canola indices show a very low or even a negative correlation with both actual and meteorological yields. These results indicate that the EAYI is a potential index for mapping and tracing the change in canola areas, or yields, with MODIS data.