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Climate and the carbon cycle: identifying responses and impacts using satellite remote sensing and machine learning (part of the SENSE Centre for Doctoral Training)

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  • Full or part time
    Prof P Palmer
    Dr E Mitchard
    Prof T Keenan
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (UK Students Only)
    Competition Funded PhD Project (UK Students Only)

Project Description

Large-scale climate variations, e.g. El Nino, can have immediate effects on the terrestrial carbon cycle via anomalous periods of drought, fire, and storm damage to forests. These immediate effects have longer-term implications for plant functioning and mortality rates, which subsequently have implications for net primary production and carbon storage that are often more difficult to observe without in situ measurements.

Space-borne instruments now observe a wide range of land surface and vegetation properties and associated atmospheric variables that can help us to understand the lagged effects of climate on terrestrial vegetation. The challenge lies in developing an infrastructure that can integrate the corresponding volume of heterogeneous data.

In this project, you will use machine learning to explore relationships between environmental variables that are measured from satellite instruments (e.g., phenology, hydrology, evapotranspiration, photosynthetically active radiation, biomass burning, solar induced fluorescence and atmospheric CO2). These relationships will be interpreted within the context of current scientific knowledge and used to test and develop existing land surface models, e.g. the Joint UK Land Environment Simulator (JULES) community land surface model. The resulting model predictions will be evaluated using sparse but detailed decadal-scale measurements collected at plot sites around the world. The long history of El Nino events will be a particular focus, taking advantage of available ground-based ecological data and satellite measurements.

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org

Funding Notes

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org

References

* Clark, et al.: https://doi.org/10.5194/gmd-4-701-2011, 2011.
* Lary et al.: https://doi.org/10.1007/978-3-319-65633-5_8
* Xiao et al.: https://doi.org/10.1016/j.rse.2019.111383

How good is research at University of Edinburgh in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 104.98

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