ExtremeEarth: Managing Water Availability for Crops Using Earth Observation and Machine Learning

Food security, especially in a changing Earth environment, is one of the most challenging issues of this century. Population growth, increased food consumption and the challenges of climate change will extend over the next decades. To deal with these, both regional and global measures are necessary. Biomass production and thus yield will need to be increased in a sustainable way. It is important to minimize the risks of yield loss even under more extreme environmental conditions, while making sure not to deplete or damage the available resources. Two measures are most important for this: irrigation and fertilization. While fertilization relies mainly on industrial goods, irrigation requires reliable water resources in the area that is being farmed, either from groundwater or surface water. Regarding surface water, a large portion of the world’s freshwater is linked to snowfall, snow storage and seasonal release of the water. All these components are subject to increased variability due to climate change and the resulting increase in extreme events. In ExtremeEarth we designed and implemented a work-fow that combines Earth Observation data with Deep Learning models to detect water demand and water availability to produce irrigation recommendations for the Danube basin.