How do ecosystems respond to changing weather patterns, rising temperatures and increasing carbon dioxide concentrations? Is the effect of precipitation more important than that of temperature? Or are ecosystem dynamics more strongly affected by nutrient availability? What is the role of extreme events in shaping biogeochemical cycles? To find out the answers we need to understand the interactions among three complex systems: climate, vegetation, and soil. Thus, we combine experiments and in-situ long-term observation with Earth Observations gathered by aircraft and satellites across a range of spatial scales, and embrace data-driven machine learning and theory-driven mechanistic modelling. With our research, we try to understand how the terrestrial biosphere reacts to and exerts feedbacks on ongoing environmental change and variation in atmospheric conditions.
Son, R.; Stratoulias, D.; Kim, H. C.; Yoon, J.-H.: Estimation of surface PM2.5 concentrations from atmospheric gas species retrieved from Tropomi using deep learning: Impacts of fire on air pollution over Thailand. Atmospheric Pollution Research 14 (10), 101875 (2023)
Katal, N.; Rzanny, M.; Mäder, P.; Römermann, C.; Wittich, H. C.; Boho, D.; Musavi, T.; Wäldchen, J.: Bridging the gap: How to adopt opportunistic plant observations for phenology monitoring. Frontiers in Plant Science (accepted)
Caldararu, S.; Rolo, V.; Stocker, B. D.; Gimeno, T. E.; Nair, R.: Ideas and perspectives: Beyond model evaluation – combining experiments and models to advance terrestrial ecosystem science. Biogeosciences 20 (17), pp. 3637 - 3649 (2023)