Improved ecosystem productivity modeling by innovative algorithms and remotely sensed phenology indicators (ECOPROPHET). (ECOPROPHET)

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 as described by in-situ observations, they still fail to capture the existing huge variability within biomes. The ECOPROPHET project aims to improve this situation 1) by testing to what degree the multitude of new Earth Observation data (e.g. from Sentinel 2, Proba-V) are able to be exploited as better proxies of ecosystem functional phenology (photosynthetic activity) and can be used to improve the phenology modules of Land Surface models, 2) by exploring the potential of these new remote sensing data 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, and 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 when greenness and photosynthesis become uncoupled. We propose to use novel remote sensing-based indicators, more closely related to photosynthetic processes than to greenness, to parameterize phenology modules of Land Surface models and thereby improve their estimates for present time and projections under future climate. The novel developed indicator will be used to produce a new generation remote sensing-based GPP and NPP product.

Keywords:ECOSYSTEMS

Disciplines:Ecology, Environmental science and management, Other environmental sciences

Project type:Collaboration project