Efficient water usage is one of the greatest challenge of 21st century especially in agriculture that consumes more than 70% of fresh water. Irrigation methods, which are based on scientific models (such as Penman-Monteith, Sebal, and Metric models) have the potential to improve on current irrigation practices. Generally, such approaches rely on combining two data sources; satellite data that provide information about the vegetation/biomass and weather that can be used to derive the evapo-transpiration. The challenge to run satellite and weather based models operationally on continental/global scale is due to enormous volume of data that needs to be processed in real time for daily updates for irrigation recommendations. Towards that end, we developed a big data platform called Physical Analytics Integrated Repository and Services (PAIRS) that curates satellite, soil, topography, land use, and high resolution weather model forecast and combines them to enable real time irrigation forecast to run on continentals scale. The platform automatically ingest new datasets as they become available like the Landsat tiles and the models are seamlessly updated with new data. Here we demonstrate a scalable modeling technique that provides 10 days ahead irrigation forecast at 30 m spatial resolution. We discuss also the integration of the irrigation forecast with variable rate irrigation control system. Variable rate irrigation can reduce up to 20% the water usage compared with uniform irrigation. The accuracy of the irrigation forecast was tested and demonstrated on various crops situated in Israel, India and USA that proves the applicability of the variable rate irrigation.
