Crop green water footprint estimation using earth observation, physics model and AI

Abstract

Without a doubt, water shortages will contribute to the future global crisis of available resources and climate change exacerbating water scarcity. Therefore, increasing water use efficiency and better management of existing resources have become substantially important. The agricultural sector is responsible for around 80% of global freshwater withdrawal. The water footprint (WF) of any agricultural product has three components: green, blue and grey water and is generally determined by the yield and the volume of water used during the crop-growing period. Estimation of crop water footprint is first step towards water sustainability under changing climate and regional crop management practices. In this talk, we present new technique to estimate crop green WF using earth observations and ML to address WF variations for a specific crop for multiple climate zones.