Mapping 20-years winter wheat dynamics in global primary planting areas using Gaussian mixture models with adaptive thresholds

Abstract. Understanding the spatiotemporal dynamics of winter wheat is essential for ensuring global food security. Currently, limited research has focused on the global dynamics of wheat over past decades. In this study, we propose a novel framework to map fractional winter wheat dynamics from 2001 to 2020 at 1 km resolution in key global planting areas from MODIS satellite data, utilizing a flexible Gaussian mixture model. We first created the stratified samples of winter wheat fractions at 1 km resolution from multiple public crop datasets, and then developed a robust random forest regression model using MODIS surface reflectance. Subsequently, we estimated the actual wheat cover fractions across different regions and years by analyzing crop mixtures within 1°×1° grids with multiple Gaussian models. The model parameters were utilized to determine optimal thresholds for winter wheat extraction. The performance of our proposed framework was evaluated spatially and temporally, revealing significant insights into global winter wheat dynamics. Results demonstrated that our mapping approach aligns closely with existing local winter wheat maps and statistical data, achieving a coefficient of determination (R²) of 0.81 with FAO statistics in primary planting regions and exceeding 0.72 at subnational scales. This study presents the first comprehensive effort to map global winter wheat distribution and dynamics from 2001 to 2020 at a near-global scale. The proposed framework is readily adaptable to other major crops and demonstrates strong agreement with existing maps and statistical records. The resulting high-resolution global winter wheat map series provides valuable inputs for global crop modeling and contributes to achieving the “Zero Hunger”. The product is publicly available at https://doi.org/10.6084/m9.figshare.32149033.