Mapping the agricultural croplands of the world using Petabyte-scale big-data analytics from Landsat 30m satellite sensor data, multiple machine learning algorithms, and Google Earth Engine (GEE) cloud computing

Global food and water security are threatened by several events such as changing climate, ballooning populations, stress on land and water, demographic changes, pandemics, and wars. The need to grow sufficient food and nutrition to feed the populations of twenty-first century and beyond require us to carefully understand, model, map, and monitor cropland dynamics over time and space. To achieve this, we have proposed and established a global food security support analysis data (GFSAD) project to develop multiple high-resolution agricultural cropland products encompassing the entire world. In this presentation, we will demonstrate production of Landsat satellite derived 30m global cropland extent product as well as irrigated versus rainfed cropland product using petabyte-scale big-data analytics, and multiple machine learning algorithms by coding and computing on the Google Earth Engine (GEE) cloud. Accuracies, errors, and uncertainties of the products will also be discussed.