Winter Wheat Extraction Using Time-Series Sentinel-2 Data Based on Enhanced TWDTW in Henan Province, China

Abstract

As a major world crop, the accurate spatial distribution of winter wheat is important for improving planting strategy and ensuring food security. Due to big data management and processing requirements, winter wheat mapping based on remote-sensing data cannot ensure a good balance between the spatial scale and map details. This study proposes a rapid and robust phenology-based method named “enhanced time-weighted dynamic time warping” (E-TWDTW), based on the Google Earth Engine, to map winter wheat in a finer spatial resolution, and efficiently complete the map of winter wheat at a 10-m resolution in Henan Province, China. The overall accuracy and Kappa coefficient of the resulting map are 97.98% and 0.9469, respectively, demonstrating its great applicability for winter wheat mapping. This research indicates that the proposed approach is effective for mapping large-scale planting patterns. Furthermore, based on comparative experiments, the E-TWDTW method has shown excellent robustness across lower quantities of training data and early season extraction ability. Therefore, it can provide early data preparation for winter wheat planting management in the early stage.