Satellite terrestrial ecosystem large-scale analysis
(Stella)
Satellite measurements offer indirect but large-scale information about the status and health of ecosystems, also in inaccessible areas. Observations in different spectral regions give complementary information on different aspects such as the greenness or water content of the canopy. The vegetation in turn strongly drives the exchange processes of CO2, water and energy between the land surface and the atmosphere. In the case of CO2, the land ecosystems also compensate a large part of anthropogenic emissions. Our main goal is therefore the accurate quantification, and possibly a better understanding, of these exchange processes. For this we use satellite observations and largely follow an established data-driven approach that combines them with local measurements of the fluxes from eddy-covariance stations and machine learning to eventually scale the in-situ flux measurements to regional and global scales. We are part of the Fluxcom team.
- Quantifying the biogenic CO2 fluxes and their uncertainties in Europe in high spatiotemporal detail. With this we are contributing to the project ITMS in which many scientific partners and public institutions work together towards building an operational monitoring system for greenhouse gases in Germany. Challenges include:
- the highly fragmented landscape in central Europe and the limited life time of satellites. How to accommodate and account for this in the flux estimates from the machine learning side?
- creating meaningful estimates of the uncertainty of the flux estimates that represent as many methodological choices as possible
- creating highly automated processing pipelines for an operational monitoring system
New observational constraints on stress conditions. How can we improve the flux estimates under stress conditions by combining complementary satellite observations, including geostationary ones, and other observational constraints?
Spotlight on the Arctic: The Arctic-Boreal region is particularly scarse in observations and also brings particular challenges in satellite observation. At the same time, the region has highly unique ecosystems which undergo rapid change. We want to understand the opportunities and limitations in understanding and quantifying land-atmosphere fluxes there better.
Fluxes in highly managed areas - Crops: Although most of the ecosystems on the land surface are affected by human presence to some extent, cropping areas are the most. They are also very productive and important for food security. Including information on management in the machine learning approach is limited by the availability of relevant information at large scales. We work towards better understanding and quantifying terrestrial ecosystem fluxes over cropping areas at large spatial scales.
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