Training on Remote Sensing for Ecosystem modElling (TRuStEE)
Training on Remote Sensing for Ecosystem modElling
Start date: Oct 1, 2016,
End date: Sep 30, 2020
Understanding and predicting ecosystem functions remains a major challenge in evaluating ecosystem services and biophysical controls on biosphere-atmosphere interactions, as current dynamic vegetation models are still not capable of grasping the spatial and temporal variability in ecosystem processes. Remote sensing (RS) data at a range of scales from proximal observations to global extent sampling can detect essential changes in plant traits (PTs), biodiversity and ecosystem functioning, providing a method for scaling-up. However there are still methodological and technical constraints that hamper a systematic incorporation of RS in ecosystem models, including scalability and multi-source data integration issues. TRuStEE will train a new generation of scientists with complementary and interdisciplinary skills in ecosystem modelling, plant physiology, RS technologies and big data analysis, addressing the specific objectives: 1) to identify essential biodiversity variables (EBVs) and the link with PTs and ecosystem functional properties (EFPs), inferable from RS, 2) to investigate a completely new avenue for assessing vegetation photosynthetic efficiency from RS measurements of canopy fluorescence, 3) to assimilate diverse RS data streams with varying spatial and temporal resolution in dynamic ecosystem models and 4) to exploit new satellite missions (e.g. ESA-FLEX, ESA-Sentinels, NASA-GEDI) and EO products for the upscaling of PTs, EBVs and EFPs. The early stage researchers (ESRs) involved will strongly benefit from the network of internationally recognized scientists and private companies with relevant expertise in these topics. The cooperation program proposed will link academic and non-academic participants to allow the circulation of ESRs giving them the opportunity to become new research and innovation leaders in the most cutting edge sophisticated technologies in the field, increasing their employability in both academic and private sectors.
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