000821710 001__ 821710
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000821710 0247_ $$2CORDIS$$aG:(EU-Grant)721995$$d721995
000821710 0247_ $$2CORDIS$$aG:(EU-Call)H2020-MSCA-ITN-2016$$dH2020-MSCA-ITN-2016
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000821710 035__ $$aG:(EU-Grant)721995
000821710 150__ $$aTraining on Remote Sensing for Ecosystem modElling$$y2016-10-01 - 2020-09-30
000821710 371__ $$aUniversity of Exeter$$bUniversity of Exeter$$dUnited Kingdom$$ehttp://www.exeter.ac.uk/$$vCORDIS
000821710 371__ $$aMax Planck Society$$bMPG$$dGermany$$ehttp://www.mpg.de/en$$vCORDIS
000821710 371__ $$aForschungszentrum Jülich$$bForschungszentrum Jülich$$dGermany$$ehttps://www.ptj.de/$$vCORDIS
000821710 371__ $$aAGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS$$bCSIC$$dSpain$$ehttp://www.csic.es$$vCORDIS
000821710 371__ $$aFONDAZIONE EDMUND MACH$$bFEM$$dItaly$$ehttp://cri.fmach.eu; www.fmach.eu$$vCORDIS
000821710 371__ $$aUniversity of Milano-Bicocca$$bUNIMIB$$dItaly$$ehttp://www.unimib.it/go/102/Home/English$$vCORDIS
000821710 371__ $$aAEROVISION BV$$dNetherlands$$ehttp://www.aerovision.nl$$vCORDIS
000821710 371__ $$aQUESTUAV LTD$$bQuestUAV Ltd$$dUnited Kingdom$$ehttp://www.questuav.com$$vCORDIS
000821710 371__ $$aUniversity of Twente$$bUniversity of Twente$$dNetherlands$$ehttp://www.utwente.nl/en/$$vCORDIS
000821710 371__ $$aFlemish Institute for Technological Research$$bVITO$$dBelgium$$ehttps://vito.be/en$$vCORDIS
000821710 372__ $$aH2020-MSCA-ITN-2016$$s2016-10-01$$t2020-09-30
000821710 450__ $$aTRuStEE$$wd$$y2016-10-01 - 2020-09-30
000821710 5101_ $$0I:(DE-588b)5098525-5$$2CORDIS$$aEuropean Union
000821710 680__ $$aUnderstanding 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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