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BG4.12/GI2.26 Media

Global Earth observation and in-situ data for improved understanding of terrestrial ecosystem dynamics (co-organized)
Convener: Wouter Dorigo  | Co-Conveners: Jean-Christophe Calvet , Nuno Carvalhais , Matthias Forkel , Mariette Vreugdenhil , Martin Thurner , Thomas Pugh , Jean-François Exbrayat , A. Anthony Bloom , Natasha MacBean , Matthias Cuntz , Thomas Smallman , Mathew Williams , Wenping Yuan , Gitta Lasslop 
 / Thu, 12 Apr, 13:30–17:00
 / Attendance Thu, 12 Apr, 17:30–19:00

Monitoring and modeling of vegetation and ecosystem dynamics is fundamental in diagnosing and forecasting Earth system states and feedbacks. However, the underlying ecosystem processes are still relatively poorly described by Earth system models. Confronting terrestrial biogeochemical models at multiple temporal and spatial scales with an ever-increasing amount and diversity of Earth observation data is therefore needed.

To this end, the rapidly growing amount of satellite data has fostered the development of novel global satellite products of vegetation and ecosystem properties (such as fluorescence, microwave vegetation optical depth, biomass, multi-sensor climate data records, new high resolution products), which complement more traditional products, such as NDVI, LAI or fAPAR. In this session, we present the most recent advances in:

(1) the production of global land surface biophysical and biochemical variables from satellite and in-situ observations;

(2) assessment of plausibility, validation and intercomparisons of these products;

(3) their use in studying global ecosystem dynamics related to, e.g., climate variability and change;

(4) developments of terrestrial biogeochemical models to allow for the integration of new observational datasets:

(5) benchmarking and improvement of these models through statistical and model-data integration techniques.

The latter may consider methodological foci or include applications related to the monitoring and modeling of terrestrial vegetation and ecosystem dynamics for timescales from days to decades, also including multiple data streams.