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Fabian Schneider

Photo of Fabian Schneider


4800 Oak Grove Drive
M/S 233-300

Pasadena, CA 91109



Curriculum Vitae:

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Member of:

Carbon Cycle And Ecosystems


I am a research scientist in the Carbon Cycle and Ecosystems group at the Jet Propulsion Laboratory. I’m interested in characterizing vegetation through the measurement of plant functional traits and trait diversity to study the impact of global change on plant health as well as ecosystem productivity and stability. To measure, monitor and predict vegetation change, my main focus lies on combining imaging spectroscopy and laser scanning measurements at different scales (terrestrial, airborne, spaceborne measurements), and integrating it with physically based models (e.g. ED2 for vegetation dynamics, DART for radiative transfer).


  • PhD (2018), Remote Sensing Laboratories, URPP GCB, University of Zurich, Switzerland.
  • MSc (2013), Department of Geography, University of Zurich, Switzerland.
  • BSc (2011), Department of Geography, University of Zurich, Switzerland.

Professional Experience

  • JPL Research Scientist, NASA Jet Propulsion Laboratory (2021 - present)
  • JPL Postdoc, NASA Jet Propulsion Laboratory (2018 - 2021)
  • Teaching assistant, Department of Geography, University of Zurich, Switzerland, 2013-2018

Research Interests

  • Remote sensing of forest ecosystems, plant functional traits and functional diversity
  • Fusion of imaging spectroscopy and laser scanning
  • Forest ecology and ecosystem dynamics
  • Biosphere-atmosphere interactions, including the interaction of radiation with the vegetation canopy

Selected Awards

  • Distinction of the Natural Science Faculty of the University of Zurich for the PhD thesis (2018)
  • Distinction of the Natural Science Faculty of the University of Zurich for the MSc thesis (2013)

Selected Publications

  1. Cavender-Bares, J., Schneider, F.D., Joao Santos, M., Armstrong, A., Carnaval, A., Dahlin, K., Fatoyinbo, L., Hurtt, G.C., Schimel, D.S., Townsend, P.A., Ustin, S.L., Wang, Z., & Wilson, A.M. (2022). Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nat. Ecol. Evol.
  2. Helfenstein I.S., Schneider F.D., Schaepman M.E., Morsdorf F. (2022). Assessing biodiversity from space: Impact of spatial and spectral resolution on trait-based functional diversity. Remote Sens. Environ. 275(March):113024
  3. Queally, N., Ye, Z., Zheng, T., Chlus, A., Schneider, F.D., Pavlick, R. P., & Townsend, P. A. (2022). FlexBRDF: A Flexible BRDF Correction for Grouped Processing of Airborne Imaging Spectroscopy Flightlines. Journal of Geophysical Research: Biogeosciences, 127(1).
  4. Cawse-Nicholson, K., et al. (2021). A compilation of surface imaging algorithms: NASA’s Surface Biology and Geology Designated Observable. Remote Sensing of Environment, 257, 112349.
  5. Zheng, Z., Zeng, Y., Schneider, F.D., Zhao, Y., Zhao, D., Schmid, B., Schaepman, M.E., & Morsdorf, F. (2021). Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest. Remote Sensing of Environment, 252, 112170.
  6. Schneider, F.D., Ferraz, A., Hancock, S., Duncanson, L.I., Dubayah, R.P., Pavlick, R.P., & Schimel, D.S. (2020). Towards mapping the diversity of canopy structure from space with GEDI. Environmental Research Letters.
  7. Morsdorf, F., Schneider, F.D.., Guillén-Escribà, C., Kükenbrink, D., Leiterer, R., & Schaepman, M.E. (2020). The Laegeren Site: An Augmented Forest Laboratory. In J. Cavender-Bares, J.A. Gamon & P.A. Townsend (Eds.). Remote Sensing of Plant Biodiversity (pp. 83-104).
  8. Czyż, E.A., Guillen Escriba, C., Wulf, H., Tedder, A., Schuman, M.C.,Schneider, F.D., & Schaepman, M.E. (2020). Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series. Ecology and Evolution, 10 (14), 7419-7430.
  9. Paul-Limoges, E., Wolf, S., Schneider, F.D., Longo, M., Moorcroft, P., Gharun, M., & Damm, A. (2020). Partitioning evapotranspiration with concurrent eddy covariance measurements in a mixed forest. Agricultural and Forest Meteorology, 280, 107786.
  10. Thonicke, K., Billing, M., Bloh, W., Sakschewski, B., Niinemets, Ü., Peñuelas, J., Cornelissen, J.H.C., Onoda, Y., van Bodegom, P., Schaepman, M.E., Schneider, F.D., & Walz, A. (2020). Simulating functional diversity of European natural forests along climatic gradients. Journal of Biogeography, 47 (5), 1069-1085.
  11. Schneider, F.D., A. Ferraz, & Schimel, D. (2019). Watching Earth's interconnected systems at work, EOS, 100.
  12. Schneider, F.D., Kükenbrink, D., Schaepman, M. E., Schimel, D. S., & Morsdorf, F. (2019). Quantifying 3D structure and occlusion in dense tropical and temperate forests using close-range LiDAR. Agricultural and Forest Meteorology, 268, 249-257.
  13. Schimel, D., Schneider, F.D., & JPL Carbon and Ecosystem Participants (2019). Flux towers in the sky: global ecology from space. New Phytologist, 224 (2), 570-584.
  14. Kükenbrink, D., Hueni, A., Schneider, F.D., Damm, A., Gastellu-Etchegorry, J.-P., Schaepman, M. E., & Morsdorf, F. (2019). Mapping the Irradiance Field of a Single Tree: Quantifying Vegetation-Induced Adjacency Effects. IEEE Transactions on Geoscience and Remote Sensing, 57 (7), 4994-5011.
  15. Damm, A., Paul-Limoges, E., Haghighi, E., Simmer, C., Morsdorf, F.,Schneider, F.D., van der Tol, C., Migliavacca, M., & Rascher, U. (2018). Remote sensing of plant-water relations: An overview and future perspectives. Journal of Plant Physiology, 227, 3-19.
  16. Fawcett, D., Verhoef, W., Schläpfer, D.,Schneider, F.D., Schaepman, M. E., & Damm, A. (2018). Advancing retrievals of surface reflectance and vegetation indices over forest ecosystems by combining imaging spectroscopy, digital object models, and 3D canopy modelling. Remote Sensing of Environment, 204, 583-595.
  17. Morsdorf, F., Kükenbrink, D., Schneider, F.D., Abegg, M., & Schaepman, M. E. (2018). Close-range laser scanning in forests: towards physically based semantics across scales. Interface Focus, 8(2), 20170046.
  18. Schneider, F.D., Morsdorf, F., Schmid, B., Petchey, O. L., Hueni, A., Schimel, D. S., & Schaepman, M. E. (2017). Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nature Communications, 8 (1), 1441.
  19. Yamasaki, E., Altermatt, F., Cavender-Bares, J., Schuman, M. C., Zuppinger-Dingley, D., Garonna, I.,Schneider, F.D., Guillén Escribà, C., van Moorsel, S.J., Hahl, T., Schmid, B., Schaepman-Strub, G., Schaepman, M.E., & Shimizu, K. K. (2017). Genomics meets remote sensing in global change studies: monitoring and predicting phenology, evolution and biodiversity. Current Opinion in Environmental Sustainability, 29, 177-186.
  20. Morsdorf, F., Eck, C., Zgraggen, C., Imbach, B.,Schneider, F.D., & Kükenbrink, D. (2017). UAV-based LiDAR acquisition for the derivation of high-resolution forest and ground information. Leading Edge, 36 (7), 566-570.
  21. Kükenbrink, D.,Schneider, F.D., Leiterer, R., Schaepman, M. E., & Morsdorf, F. (2017). Quantification of hidden canopy volume of airborne laser scanning data using a voxel traversal algorithm. Remote Sensing of Environment, 194, 424-436.
  22. Jetz, W., Cavender-Bares, J., Pavlick, R., Schimel, D., Davis, F. W., Asner, G. P., Guralnick, R., Kattge, J., Latimer, A.M., Moorcroft, P., Schaepman, M.E., Schildhauer, P.,Schneider, F.D., Schrodt, F., Stahl, U. & Ustin, S. L. (2016). Monitoring plant functional diversity from space. Nature Plants, 2 (3), 16024.
  23. Schaepman, M.E., Jehle, M., Hueni, A., D'Odorico, P., Damma, A., Weyermann, J., Schneider, F.D., Laurent, V., Popp, C., Seidel, F.C., Lenhard, K., Gege, P., Küchler, C., Brazile, J., Kohler, P., De Vos, L., Meuleman, K., Meynart, R., Schläpfer, D., Kneubühler, M. & Itten, K.I. (2015). Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX). Remote Sensing of Environment, 158 (1), 207-219.
  24. Schneider, F.D., Leiterer, R., Morsdorf, F., Gastellu-Etchegorry, J.-P., Lauret, N., Pfeifer, N., & Schaepman, M. E. (2014). Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sensing of Environment, 152, 235-250.