The black soil region of Northeast China is a key grain-production area where cropland water erosion threatens soil fertility and sustainability. We diagnosed cropland soil loss across six diagnostic years/time slices (2001, 2005, 2010, 2015, 2020, and 2024) using a Revised Universal Soil Loss Equation (RUSLE)-based remote-sensing workflow implemented in Google Earth Engine (GEE). Rainfall erosivity was derived from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily precipitation, soil erodibility from SoilGrids, topography from the Shuttle Radar Topography Mission digital elevation model (SRTM DEM), vegetation cover from the Landsat normalized difference vegetation index (NDVI), and cropland extent from ESA WorldCover; alternative rainfall sources, cropland masks, and P-factor settings were used for sensitivity analyses. Under the slope-graded P-factor scenario, mean annual soil loss ranged from 1.60 to 3.07 t ha⁻¹ yr⁻¹, and the proportion of cropland exceeding T = 2 t ha⁻¹ yr⁻¹ ranged from 25.2% to 52.9%. Soil loss fluctuated among years because rainfall erosivity and cover-management effects partly counteracted each other. Risk was concentrated in sloping piedmont and hilly cropland, whereas broad plains were dominated by very slight and slight erosion. P-factor parameterization represented the largest structural uncertainty. The workflow provides regional screening evidence for field verification and conservation-practice assessment, rather than direct site-specific engineering prescriptions.