 The study aimed to produce three distinct cropland products using Landsat 30m data on the Google Earth Engine platform for South Asia's major crop types and irrigation methods. These products were designed to assess irrigated versus rain-fed crop lands, cropping intensity, and crop types. The results showed an overall accuracy of 79.8% for the irrigated versus rain-fed product, 85.3% for the cropping intensity product, and accuracy levels ranging from 72% to 97% for crop types. These products are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple satellite sensor big data and random forest machine learning algorithms on the GE cloud. This article was authored by Maroli Krishnagumar, Prasad S. Tenkabel, Pranay Panjala, and others.