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By providing food, animal-feed, fibre and energy, biomass production is possibly the most important ecosystem service made to society.

While global products of biomass production from Remote Sensing (MOD17) and Land Surface models do capture the global patterns, they still fail to capture the existing huge variability within biomes.

This project aims to improve this situation


  1.  by testing to what degree the multitude of new Earth Observation products (e.g. from Sentinel 2, Proba-V, MODIS) are better proxies of ecosystem functional phenology (photosynthetic activity) and can be used to re-parameterize the phenology modules of Land Surface models,
  2.  by exploring the potential of these new remote sensing products to produce a new gross primary productivity (GPP) product
  3. by developing an entirely new algorithm to convert remote sensing-based GPP products to biomass production
  4. by using a large database of quality-controlled in situ measurements of biomass production, all accompanied by a standardized uncertainty estimate, and the FLUXNET 2015 and ICOS databases (for in situ GPP estimates and functional phenology data) to assess whether our efforts did in fact reduce the currently large unexplained variation in ecosystem gross primary productivity and biomass production.

A major focus of this project is on functional phenology as a key determinant of ecosystem carbon, water and energy balances. Current phenological observations are all based on differences in the Normalized Difference Vegetation Index (NDVI), which is a good proxy for canopy leaf area and light absorption, but is not an ideal proxy for canopy photosynthesis, especially during drought periods and during autumn [1] when greenness and photosynthesis become uncoupled.