Kolloquium Geoinformatik From Clouds to Crops: a journey through various data assimilation showcases in environmental science

Data assimilation, or more generally phrased machine learning, has been gaining popularity in academia and industry in recent years. Today, affordable computing power and the immense availability of accessible data are fueling a data science boom. With the start of the EU's Copernicus programme, highly resolved satellite data will further push this development in the geosciences. Such recently emerging sources of spatial data provide new challenges and opportunities in geographic information technology. In this talk, I will summarize my own research activities in applied data science or assimilation throughout the last 10 years. To quantify the carbon balance of croplands, I have assimilated carbon flux and remotely sensed NDVI data (MODIS) into an ecosystem model. I trained the same model for an improved simulation of plant hydraulics and drought resistance through corroboration with transpiration data, and investigated statistical relationships between variables of forest inventory data. I finally applied an artificial neural network and a variational data assimilation algorithm within a cloud model to retrieve climatologies of cloud parameters from space (AVHRR, MODIS). These attempts are a mixture of success stories and failures, and both of which shall be given attention in this presentation. I will conclude with a brief outlook on the potential of the Copernicus programme to spark research activities and business ideas around the globe.