Global cropping intensity at 30m resolution for 2022 was generated using multi-source satellite imageries during 2021-2023 based on a phenophase detection algorithm.Globally, 110 AEZs were identified, taking into account differences in climatic zones, topographic conditions, cropping systems, and cropland distributions. According to the phenological information of the AEZs, the starting dates and ending dates of monitoring were determined based on time series MODIS NDVI data at 250 m. Within each monitoring unit, all available images of top-of-atmosphere (TOA) reflectance from Landsat series, Sentinel-2 (global), and GaoFen (GF-1, China) satellites during the monitoring period were used for CI mapping. These images were used to compose the 16-day TOA reflectance time series based on the maximum composition method. In order to ensure data continuity, this study used MODIS NDVI data at 250 m to fill temporal gaps and the Whittaker smoothing algorithm was applied to smooth the NDVI time series. We included two phenology metrics, mid-greenup and mid-greendown, which were derived as the day of year (DOY) at the transition points in the greenup and greendown periods when the smoothed NDVI time series passes 50% of the NDVI amplitude. An interval starting from mid-greenup and ending at mid-greendown was defined as a growing phenophase, and an interval moving from mid-greendown to mid-greenup was defined as a non-growing phenophase (Liu C et al., 2020; Zhang et al., 2021). We set a lower limit of the growing-harvesting cycle length to 48 days for removing the false cycles. Finally, global CI was mapped at 30 m resolution for 2022.