Early-season and refined mapping of winter wheat based on phenology algorithms - a case of Shandong, China

• Published in: Frontiers in plant science Vol. 14; p. 1016890
• Main Authors: Liu, Xiuyu, Li, Xuehua, Gao, Lixin, Zhang, Jinshui, Qin, Dapeng, Wang, Kun, Li, Zhenhai
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 24.07.2023
• Subjects: contemporaneous crops; early mapping; phenological information; Plant Science; Sentinel-2; winter wheat; contemporaneous crops; winter wheat; Sentinel-2; phenological information; early mapping
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2023.1016890

Winter wheat is one of the major food crops in China, and timely and effective early-season identification of winter wheat is crucial for crop yield estimation and food security. However, traditional winter wheat mapping is based on post-season identification, which has a lag and relies heavily on sample data. Early-season identification of winter wheat faces the main difficulties of weak remote sensing response of the vegetation signal at the early growth stage, difficulty of acquiring sample data on winter wheat in the current season in real time, interference of crops in the same period, and limited image resolution. In this study, an early-season refined mapping method with winter wheat phenology information as priori knowledge is developed based on the Google Earth Engine cloud platform by using Sentinel-2 time series data as the main data source; these data are automated and highly interpretable. The normalized differential phenology index (NDPI) is adopted to enhance the weak vegetation signal at the early growth stage of winter wheat, and two winter wheat phenology feature enhancement indices based on NDPI, namely, wheat phenology differential index (WPDI) and normalized differential wheat phenology index (NDWPI) are developed. To address the issue of “ different objects with the same spectra characteristics” between winter wheat and garlic, a plastic mulched index (PMI) is established through quantitative spectral analysis based on the differences in early planting patterns between winter wheat and garlic. The identification accuracy of the method is 82.64% and 88.76% in the early overwintering and regreening periods, respectively, These results were consistent with official statistics (R2 = 0.96 and 0.98, respectively). Generalization analysis demonstrated the spatiotemporal transferability of the method across different years and regions. In conclusion, the proposed methodology can obtain highly precise spatial distribution and planting area information of winter wheat 4_6 months before harvest. It provides theoretical and methodological guidance for early crop identification and has good scientific research and application value.