MAX PLANCK INSTITUTE FOR BIOGEOCHEMISTRY (DE)
The EOLINCS promotes a community effort towards an enhanced multi-mission assessment of the terrestrial carbon cycle at resolutions in space and time compatible with decision making by improving the access to the Earth Observation (EO) data for the wider carbon scientific community so that key questions related to scale, representativeness, consistency, reliability, as well as the applicability of the multivariate EO data and how they affect our understanding of the carbon cycle processes across spatial and temporal scales can be addressed. This will be demonstrated through 4 Scientific Examples:
SCS1: Explanatory power of novel EO data streams for predicting net carbon fluxes
Exploration of novel data streams to constrain net ecosystem exchange estimates at flux towers and analysis of EO product added value via explainable machine learning, specifically incorporating Sentinel 3 data into the FLUXCOM-X framework a way that is updatable and expandable to all sites and other Sentinel data products.
SCS2: Forest recovery post disturbance
Quantification and understanding the temporal dynamics of forest biomass during disturbance and recovery uisng high-resolution height/biomass maps that are expected to enable the monitoring of biomass at finer scales, in particular the impact of fine scale forest disturbances due to management practices such as thinning and the impact of natural disturbances (insects attacks, droughts, fires and windthrown in regions of interest).
SCS3: Model-Data Fusion for Understanding Carbon State-Flux Relationships Across Space
Use of EO data to constrain and understand the carbon state-flux relationships across spatial gradients using a terrestrial carbon model to understand carbon state-flux relationships across space by leveraging and cross-comparing EO data of biomass and vegetation states
SCS4: EO enhanced benchmarking of GCB DGVMs
Better constraints on component processes (productivity and turnover; particularly in response to disturbances and land management) that determine the European land carbon sink, and the partitioning into vegetation and soil carbon pools using EO observational constraints to evaluate the suite of Dynamic Global Vegetation Models (DGVMs) that contribute to the Global Carbon Budget (GCB) through an enhanced ILAMB evaluation tool.
