Abstract. Accurate, detailed, and up-to-date information on cropland extent is crucial for provisioning food security and environmental sustainability. However, because of the complexity of agricultural landscapes and lack of sufficient training samples, it remains challenging to monitor cropland dynamics at high spatial and temporal resolutions across large geographical extents, especially for places where agricultural land use is changing dramatically. Here we developed a novel cost-effective annual cropland mapping framework that integrated time-series Landsat imagery, automated training sample generation, and machine learning and change detection techniques. We implemented the proposed scheme to a cloud computing platform of Google Earth Engine and generated China’s annual cropland dataset (CACD) at a 30 m spatial resolution for the first time. Results demonstrated that our approach was capable of tracking dynamic cropland changes in different agricultural zones. The pixel-wise F1 scores for annual maps and change maps of CACD were 0.79±0.02 and 0.81, respectively. A further cross-product comparison in terms of accuracy assessment, correlations with statistics, and spatial details indicated the precision and robustness of CACD than other datasets. According to our estimation, from 1986 to 2021, China’s total cropland area expanded by 30,300 km2 (1.79 %), which underwent an increase before 2000 but a general decline between 2000–2015 and a slight recovery afterward. Cropland expansion was concentrated in the northwest while the eastern coastal region experienced substantial cropland loss. In addition, we observed 419,342 km2 (17.57 %) of croplands that were abandoned at least once during the study period. The consistent, high-resolution data of CACD can support progress toward sustainable agricultural use and food production in various research applications. The full archive of CACD is freely available at https://doi.org/10.5281/zenodo.7936885 (Tu et al., 2023a).