EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Time-scale dependent relations of vegetation productivity with Earth Observation based proxies and with climate drivers

Nora Linscheid1, Nuno Carvalhais1,2, Miguel Mahecha1,3, Anja Rammig4, and Markus Reichstein1,3,5
Nora Linscheid et al.
  • 1Max Planck Institute for Biogeochemistry, Hans–Knoell–Str. 10, 07745 Jena, Germany
  • 2Departamento de Ciencias e Engenharia do Ambiente, DCEA, Faculdade de Ciencias e Tecnologia, FCT Universidade Nova de Lisboa, Caparica, Portugal
  • 3German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany
  • 4Technical University of Munich, TUM School of Life Sciences Weihenstephan, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany
  • 5Michael Stifel Center Jena for Data–Driven & Simulation Science, Ernst–Abbe–Platz 2, 07743 Jena, Germany

New satellite products hold promise to improve our understanding of terrestrial ecosystem functioning, yet it remains a key challenge to measure global gross primary productivity (GPP) and its climate-induced fluctuations. While global estimates of GPP exist and several new satellite products hold potential for better GPP estimation, the best proxy of GPP may depend on the temporal and spatial scale considered, because available satellite products may differentially represent different time scales of vegetation dynamics. For example, vegetation indices such as NDVI and EVI may capture seasonal phenology well, while sun-induced fluorescence (SIF) may be more sensitive to short-term fluctuations of photosynthesis, and vegetation optical depth (VOD) may best represent slower changes in aboveground biomass. SIF in particular is proposed as a promising proxy for GPP as they show linear relationships with ecosystem-dependent slopes, but this may not be the case at all time scales.

In this study, we compare different Earth Observation vegetation proxies to FLUXCOM GPP in order to understand which vegetation proxy best represents GPP at sub-annual, annual and long-term scales with the aim to enable more accurate short- and long-term prediction of GPP and its drivers. We further assess the dominant climatic drivers of vegetation productivity and the vegetation’s sensitivity from sub-annual to inter-annual time scales using a multiple linear regression approach. We find the dominant drivers of vegetation productivity to differ across time scales in relation to land cover and climate.

In summary, depending on the time-scale, different satellite products best represent GPP and its climatic drivers. Considering this may help improve GPP estimates and predictions of long-term land carbon sink dynamics in the future.

How to cite: Linscheid, N., Carvalhais, N., Mahecha, M., Rammig, A., and Reichstein, M.: Time-scale dependent relations of vegetation productivity with Earth Observation based proxies and with climate drivers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18660,, 2020

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