Matthew McCabe

Nature based Solutions

Professor of Environmental Science and Engineering and Associate Director of Water Desalination and Reuse Center

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Research Interests

​Prof. McCabe’s research focuses on issues related to water and food security, climate change impacts, precision agriculture, water resources monitoring and modeling, and the novel use of technologies for enhanced Earth system observation. The research undertaken in his group combines models and observations to answer questions on the distribution, variability and exchanges of water at local, regional and global scales, as well as the interactions with vegetation. CubeSats, unmanned aerial vehicles (UAVs) and in-situ monitoring techniques are all employed to monitor terrestrial processes, while a range of modeling and statistical approaches are used to understand and predict system behavior. Improved description and understanding of the water-food nexus is a key objective of his research.​​​​

Selected Publications

  • Current practices in UAS-based environmental monitoring. Tmušić, G., Manfreda, S., Aasen, H., James, M. R., Gonçalves, G., Ben-Dor, E., ...McCabe, M. F. Remote Sensing, 12(6), (2020).
  • State of the climate in 2018. Ades, M., Adler, R., Aldeco, L. S., Alejandra, G., Alfaro, E. J., Aliaga-Nestares, V., ...Veasey, S. W. Bulletin of the American Meteorological Society, 100(9), SI-S305, (2019).
  • Using unmanned aerial vehicles to assess the rehabilitation performance of open cut coal mines. Johansen, K., Erskine, P. D., & McCabe, M. F. Journal of Cleaner Production, 209, 819-833, (2019).
  • A random forest machine learning approach for the retrieval of leaf chlorophyll content in wheat. Shah, S. H., Angel, Y., Houborg, R., Ali, S., & McCabe, M. F. Remote Sensing, 11(8), (2019).
  • On the use of unmanned aerial systems for environmental monitoring. Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Madrigal, V. P., Mallinis, G., ...Toth, B. Remote Sensing, 10(4), (2018).
  • A Cubesat enabled spatio-temporal enhancement method (CESTEM) utilizing planet, Landsat and MODIS data. Houborg, R., & McCabe, M. F. Remote Sensing of Environment, 209, 211-226, (2018).
  • A hybrid training approach for leaf area index estimation via cubist and random forests machine-learning. Houborg, R., & McCabe, M. F. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 173-188, (2018).

Education

  • ​​​​​​​​Post-doctoral Fellow, Los Alamos National Laboratory, 2006
  • Post-doctoral Fellow, Princeton University, 2003
  • Ph.D., University of Newcastle, Australia, 2003
  • B.E., University of Newcastle, Australia, 1998

Professional Profile

  • 2012-Present: Associate Professor, KAUST, Saudi Arabia
  • 2012: Associate Professor, University of New South Wales, Australia
  • 2008-2011: Senior Lecturer, University of New South Wales, Australia
  • 2006-2007: Postdoctoral Researcher, Los Alamos National Laboratory, ​New Mexico, USA
  • 2003-2005: Postdoctoral Researcher, Princet​​on University, New Jersey, USA

KAUST Affiliations

  • Water Desalination & Reuse Centre (WDRC)
  • Biological & Environmental Sciences & Engineering (BESE)
  • Environmental Science and Engineering Program