Croplayer-China: A 2-Meter Resolution Cropland Map of China Based on Active Learning of Segmentation with Mapbox and Google Satellite Imagery

Accurate cropland distribution data is essential for crop yield estimation. Currently, seven publicly available datasets, such as CLCD, ESA, ESRI, FCS30, FROM, Globeland30, and SinoLC-1, show high accuracy in flat regions of China but significant errors in rugged and fragmented areas like the south and northwest China. To address this, our study uses Mapbox and Google remote sensing imagery and the deep learning Segformer model with active learning to capture detailed cropland shapes across diverse terrains. We produced 2-meter resolution cropland maps for all 34 provincial-level regions in China for 2022. Our method achieves an overall cropland identification accuracy of 86%. Additionally, 14 provincial regions have an area error of less than 10%, compared to a maximum of 8 regions in other datasets. Detailed comparisons show our data accurately reflects cropland details, especially in mountainous and arid areas. Overall, Croplayer-China will significantly enhance high-precision crop yield estimation, disaster monitoring, and related research.