Rapid and Accurate Mapping of Winter Wheat Distribution in Jiaozuo City Using Sentinel-2 and Google Earth Engine

Rapid and accurate mapping using remote sensing and cloud computing is crucial for food security. This study employs Google Earth Engine (GEE) and Sentinel-2A multispectral imagery to analyze the distribution of winter wheat in Jiaozuo City from 2019 to 2021 using the one-class support vector machine (OCSVM) classification method. The results show high classification accuracy, with an overall accuracy of 0.952, a Kappa coefficient of 0.952, a producer's accuracy of 0.953, and a consumer's accuracy of 0.978, validating the classification effectiveness. This method is efficient, easy to implement, and suitable for large-scale crop mapping, providing valuable support for sustainable agriculture and policy-making.