The United States Geological Survey (USGS) in partnership with several US Universities and International Institutes produced the world’s first, Landsat Satellite-derived, global cropland extent product at 30m resolution (GCEP30) for the nominal year 2015, which is the highest resolution freely available global cropland dataset (Thenkabail et al., 2020). The product was a result of a “paradigm-shift” in cropland production involving satellite sensor big-data analytics, segmenting the world into 74 agro-ecological zones, use of massively large reference training and validation data, machine learning algorithms, and petabyte-scale cloud-computing on the Google Earth Engine (GEE). The GCEP30 had an overall map accuracy of 91.7%. For the global cropland class, the producer’s accuracy was 83.4% (errors of omission of 16.6%) and user’s accuracy 78.3% (errors of commission of 21.7%). For the year 2015, GCEP30-derived global net cropland area (GNCA) was 1.873 Bha (~12.6% of the terrestrial area), of which the continental distribution was: Asia 33%, Europe 25.5%, Africa 16.7%, North America 14.4%, South America 8.1%, and Australia and Oceania 2.4%. The 10 leading cropland area countries as a percentage of the GNCA were: India 179.8 Mha (9.6% of the 1.873 Bha of GNCA), USA 167.8 Mha (8.95%), China 165.2 Mha (8.82%), Russia 155.8 Mha (8.32%), Brazil 64 Mha (3.42%), Ukraine 43.4 Mha (2.32%), Canada 42.9 Mha (2.29%), Argentina 38.4 Mha (2.05%), Indonesia 37.4 Mha (2.0%), and Nigeria 35.7 Mha (1.91%). The 10 countries constitute 50% (937 Mha) of the GNCA. Only 4 countries (India, USA, China, and Russia) encompass 36% (670 Mha) of the GNCA. A comparison of the country-wise statistics on cropland areas reported in FAOSTAT with GCEP30-derived cropland data showed that the latter explained the 93% variability in country-wise cropland data reported by FAOSTAT. The slope of the predicting line was 0.89. The GCEP30 is downloadable at:; viewable at full-resolution: Reference: ***Source: Thenkabail, P.S., Teluguntla, P., Xiong, J., Oliphant, A., Congalton, R., Ozdogan, M., Gumma, M.K., Tilton, J., Giri, C., Milesi, C., Phalke, A., Massey, M., Yadav, K., Milesi, C., Sankey, T., Zhong, Y., Aneece, Y., Foley, D. 2020. Global Cropland Extent Product at 30m (GCEP30) derived using Landsat Satellite Time-series Data for the Year 2015 through Multiple Machine Learning Algorithms on Google Earth Engine (GEE) Cloud. Research Paper #, United States Geological Survey (USGS). IP-119164. In Press.