NextGenCarbon

The key target of the project is to improve our understanding of Europe's greenhouse gas budget with next generation modelling of the land-based carbon cycle using Earth Observation and Machine Learning methods. To mitigate the severity of climate change, the EU has adopted ambitious emission reduction targets that include land-based carbon sources and sinks. To achieve and monitor progress towards these targets, we need reliable data that can be updated frequently to promptly and efficiently adjust policies for land management and fossil fuel emission reduction ambitions. Understanding whether terrestrial ecosystems will continue to absorb carbon dioxide (CO2) from the atmosphere and identifying those that could become weaker sinks or even carbon (C) sources under changes in climate, land use and management remains a major scientific challenge. Remote sensing (RS) data for Earth Observation (EO) can monitor several essential climate variables (ECVs), like vegetation structure, phenology, or land surface temperature. Harnessing the potential of RS technologies can be important to promptly inform about the cycling of greenhouse gases (GHGs) and adjust targets under different climate change scenarios. However, not all GHG fluxes and C stock changes can be monitored solely with EO (for instance, soil C change or wood density); hence, coordination with in situ data campaigns is required to obtain a complete assessment. To effectively combine all these data sources, we must improve our capacity to model the terrestrial C cycle by developing data assimilation (DA) methods to incorporate the newest ground-based monitoring methods, RS data sources, and models (including land surface, bookkeeping and forest inventory models, all of which have specific strengths in this domain). The current state of the art in these fields is focused on including more detailed information on lateral C transfers, the age structure of ecosystems, hydrological cycles, anthropogenic disturbances and management decisions, and the effects of extreme events. NextGenCarbon proposes advances in all these aspects, allowing us to better monitor the present and model the future of C cycles.