MAX PLANCK INSTITUTE FOR BIOGEOCHEMISTRY (DE)
Variations of water availability drive plant growth and vegetation carbon uptake from the atmosphere. At present, this induces substantial fluctuations in the global carbon balance and in year-to-year accumulation of atmospheric carbon. Under climate change, water stress is likely to be amplified by more frequent and intense droughts.
The integration of global land surface remote sensing and in-situ measured ecosystem carbon fluxes through machine learning offers a unique data-driven perspective to diagnose the carbon cycle response to climate change. However, current approaches like `FLUXCOM’ cannot capture drought effects reliably which strongly limits our capability of assessing interactions between global change and biogeochemical cycles.
This limitation is due to insufficient information in the traditionally used Earth observation data on (a) the moisture status, and (b) the individual and highly complex responses of ecosystems to dryness. The adequate representation of these two aspects are key challenges that have been hampering breakthroughs in our ability to model and monitor the biosphere with data-driven and process-based approaches.
The above limitations will be tackled by integrating data streams of the sun-induced chlorophyll fluorescence, land surface temperature and vegetation optical depth into data-driven flux models for a better diagnosis vegetative stress reactions as well as completementary information on soil moisture.