BACI: 2015-2019


WP4 New downstream data products

Lead: MPG
Main Contact: Martin Jung, WP4 Leader

The overall aim of this work package is to derive novel synergistic products of essential ecosystem variables (EEVs) and ecosystem functional properties by integrating ground measurements and EO with machine learning methods. More specifically, our objectives are:

  • Upscaling the diurnal cycle of carbon and energy fluxes from half-hourly FLUXNET data
  • Mapping key ecosystem functional properties related to e.g. radiation use efficiency and water use efficiency of gross primary production
  • Generating a spatially explicit reconstruction of tree-ring variability for Europe
  • Producing spatially and temporally explicit patterns of ecosystem scale plant traits
  • Providing an objective functional classification of the biosphere

Latent heat map (left): Eddy covariance towers can monitor carbon, water, and energy fluxes on the long-term and provide one measurement each half hour. Using modern machine learning methods and a wide array of additionally data, we can now estimate these fluxes quite accurately across large areas. Here, we show fluxes estimate over Europe for the time 17:30 19th July 2001

Essential Ecosystem Variables (EEVs): Fundamental interactions among ECVs and EBVs govern the states, processes and functions of ecosystems.

Image Licence: CC BY 4.0

Papers that were supported by this WP