Major grain crop mapping in Northeast China using sample generation method and ensemble learning

Northeast China (NE) is a vital grain production base, playing a crucial role in ensuring food security. Maize, rice, and soybean, the three major grain crops (MGC), account for 98.7 % of NE’s total grain output. However, high-resolution MGC maps for this region are lacking, which are essential for analyzing spatial and temporal changes in crop planting patterns and developing agricultural policies. This study aims to create a 10-meter resolution map of MGC in NE (NE-MGCM) from 2019 to 2023 with a method based on sample generation and ensemble learning strategies, combined with optical and synthetic aperture radar (SAR) images. NE-MGCM achieves high mapping accuracy, with a five-year average overall accuracy ($\mathrm{OA}$) of 94.96 % and an average $\mathrm{Kappa}$ coefficient of 0.9264. The producer accuracy ($\mathrm{PA}$) for maize, rice, and soybean ranged from 91.12 % to 94.55 %, 94.89–96.36 %, and 94.53–95.69 %, respectively. Compared to four public datasets, NE-MGCM demonstrates superior overall performance and high consistency with statistical data. NE-MGCM effectively tracks the spatial and temporal distribution changes of MGC in NE, providing robust data support for food security and agricultural management.