Early-Season Mapping of Winter Wheat and Garlic in Huaihe Basin Using Sentinel-1/2 and Landsat-7/8 Imagery

Abstract

Early crop mapping is essential in predicting crop yield, assessing agricultural disasters, and responding to food price fluctuations. Winter wheat is a major food contributor in China. Existing early season maps of winter wheat strongly depend on the shape of the time series curve, which limits applicability on large scales. Besides, the effect of garlic on winter wheat mapping is often ignored. In this study, we determined how early we could identify winter crops (winter wheat and garlic) by examining time series of different lengths, and generated annual 30-m winter wheat and garlic map of the Huaihe basin using the random forest classifier, Sentinel-1/2, and Landsat-7/8 time-series imagery. The results showed that garlic could be identified at the end of November by using four composite images with an overall accuracy (OA) of 0.88, followed by winter wheat recognizable at the end of January by using eight composite images with an OA of 0.91. The proposed framework can also be implemented in other regions and crops to generate early season distribution maps of different crops.

Keywords

• Garlic
• image integration
• mapping
• random forest
• winter wheat