AbstractsAs the major maize-cultivated areas, the one-season cropland of China is increasingly threatened by rapid urbanization and soybean rejuvenation. Quantifying the area changes of maize cropland is crucial for both food and energy security. Nonetheless, due to the lack of survey data related to planting types, long-term and fine-grained maize cropland maps in China dominated by small-scale farmlands are still unavailable. In this paper, we collect 75,657 samples based on field surveys and propose a deep learning-based method according to the phenology information of maize. With the generalization capability, the proposed method produces maize cropland maps with a resolution of 30 m from 2013 to 2021 in the one-season planting areas of China. The maize-cultivated areas derived from the maps are highly consistent with the data recorded by statistical yearbooks (R2= 0.85 on average), which indicates that the produced maps are reliable to facilitate the research on food and energy security.