Near real time global cropland monitoring

Novel Coronavirus (COVID-19) pandemic disrupts global food supply chains, causes labor shortages, and raises potential food insecurity in 2020, especially for regions already have been experiencing hunger. The United Nations World Food Programme (WFP) estimated that approximately 265 million people in developing countries would suffer acute food hunger by the end of 2020. According to the recent assessment by FAO, the global food market remains stable and resilient so far but still brace for uncertainty in parallel with the desert locust swarm, drought, and flood in vast regions of the world. Therefore, near real-time monitoring of global food production is vital to provide early information, helping to alleviate food insecurity. In this study, we developed a publicly available global crop growth monitoring system to detect production anomaly with process-based model Breathing Earth System Simulator (BESS) driven by multi-satellite data. The system includes automatic data downloading, processing, and model execution. We mapped gross primary productivity (GPP) and evapotranspiration (ET) for global croplands with fine spatial (1-5 km) and temporal (daily) resolution. Estimates of GPP and ET has been served as key proxies for crop growth status and yield prediction. In conjunction with the daily global scale GPP and ET products for 2000-2018 as the reference, we detected crop production anomalies for 2020.