A multitemporal index for the automatic identification of winter wheat based on Sentinel-2 imagery time series

Abstract

Timely and accurate monitoring of the spatial distribution of wheat is crucial for wheat field management, growth monitoring, yield estimation and prediction. In this study, a multitemporal index, termed the winter wheat mapping index (WWMI), was constructed for automatic winter wheat mapping on the basis of Sentinel-2 enhanced vegetation index (EVI) time series and wheat phenological features. Henan, an important winter wheat production province in China, was selected as the study area. Zhumadian, the primary wheat-growing city in Henan, was the test area. Both empirical and automatic threshold (Otsu) methods were adopted to calculate the optimal threshold of the WWMI. The performance of WWMI in winter wheat mapping was compared at object-oriented and pixel-based levels. The proposed WWMI separated winter wheat and nonwinter wheat areas well, thus achieving highly accurate winter wheat mapping. In Zhumadian, the empirical threshold method performed better than the Otsu method, but the former relied on official statistics to iteratively adjust the WWMI threshold. In Henan, the mapping accuracy achieved by the Otsu method was higher than that achieved by the empirical threshold method, with mean relative errors (MREs) of 6.78% and 19.87% at the municipal and county levels, respectively. This was because, compared with the empirical threshold method, the Otsu method did not rely on official statistics and adaptively determined the optimal threshold of the WWMI for each city in Henan, thus fully considering wheat growth state differences in different cities. In addition, the object-oriented WWMI performed better than the pixel-based WWMI in wheat mapping. The results further demonstrated the feasibility of combining the WWMI with the Otsu method for automatic winter wheat mapping at large extents, which will provide a theoretical basis for identifying other food crops.