Jouni Susiluoto

Biographical Sketch

Jouni Susiluoto joined JPL in 2021 after finishing a JPL postdoctoral fellowship focused on retrieval algorithms for imaging spectroscopy missions. He continues in this work, additionally collaborating with Prof. Houman Owhadi at Caltech in developing fast emulators for radiative transfer forward models. Jouni's previous work includes a wide range of data science and modeling applications in geosciences and remote sensing, such as spatial data fusion and Bayesian model selection. Prior to JPL, he spent two years in the Uncertainty Quantification group of Department of Aeronautics and Astronautics at MIT, studying spatio-temporal statistical modeling of massive remote sensing data. Susiluoto has a doctorate in mathematics from University of Helsinki, Finland, where he was a member of the Inverse Problems group at the Department of Mathematics and Statistics. His research work and interests are in uncertainty quantification, inverse problems, Bayesian methods, MCMC, parameter estimation, climate models, data assimilation, and carbon cycle modeling.

Projects

• Uncertainty quantification for imaging spectroscopy
• Gaussian process emulators for radiative transfer and related problems

Selected Publications

David R. Thompson, Niklas Bohn, Amy Braverman, Philip G. Brodrick, Nimrod Carmon, Michael L. Eastwood, Jay E. Fahlen, Robert O. Green, Margaret C. Johnson, Dar A. Roberts, and Jouni Susiluoto (2021). “Scene invariants for quantifying radiative transfer uncertainty,” Remote Sensing of Environment, 260, p. 112432. Download.

Niklas Bohn, Thomas H. Painter, David R. Thompson, Nimrod Carmon, Jouni Susiluoto, Michael J. Turmon, Mark C. Helmlinger, Robert O. Green, Joseph M. Cook, and Luis Guanter (2021). “Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements,” Remote Sensing of Environment, 264, p. 112613. Download.

Susiluoto, J., Bohn, Niklas, Braverman, Amy, Brodrick, Philip, Carmon, Nimrod, Nguyen, Hai, Gunson, Michael, Turmon, Michael, and Thompson, David R. (2021). “Accelerated Optimal Estimation: Meeting the surface reflectance retrieval performance and quality requirements of future remote imaging spectroscopy missions,” Submitted.

Springer, S., Haario, H., Susiluoto, J., Bibov, A., Davis, A., and Marzouk, Y. (2021). “Efficient Bayesian inference for large chaotic dynamical systems,” Geoscientific Model Development, 14(7), pp. 4319–4333. Download.

David R. Thompson, Philip G. Brodrick, Niklas Bohn, Amy Braverman, Nimrod Carmon, David Connelly, Jay Fahlen, Robert O. Green, Robert L. Herman, Jonathan Hobbs, Margaret Johnson, Natalie Mahowald, Gregory S. Okin, Benjamin Poulter, Shawn Serbin, Alexey N. Shiklomonov, Jouni Susiluoto, and Michael Turmon (2020). “Toward comprehensive uncertainty predictions for remote imaging spectroscopy,” Imaging Spectrometry XXIV: Applications, Sensors, and Processing, vol. 11504, ed. E. Ientilucci and P. Mouroulis, pp. 56 – 62, SPIE. Download.

David R. Thompson, Amy Braverman, Philip G. Brodrick, Alberto Candela, Nimrod Carmon, Roger N. Clark, David Connelly, Robert O. Green, Raymond F. Kokaly, Longlei Li, Natalie Mahowald, Ronald L. Miller, Gregory S. Okin, Thomas H. Painter, Gregg A. Swayze, Michael Turmon, Jouni Susilouto, and David S. Wettergreen (2020). “Quantifying uncertainty for remote spectroscopy of surface composition,” Remote Sensing of Environment, 247, p. 111898. Download.

Nimrod Carmon, David R. Thompson, Niklas Bohn, Jouni Susiluoto, Michael Turmon, Philip G. Brodrick, David S. Connelly, Amy Braverman, Kerry Cawse-Nicholson, Robert O. Green, and Michael Gunson (2020). “Uncertainty quantification for a global imaging spectroscopy surface composition investigation,” Remote Sensing of Environment, 251, p. 112038. Download.

Susiluoto, J., Spantini, A., Haario, H., Härkönen, T., and Marzouk, Y. (2020). “Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2,” Geoscientific Model Development, 13(7), pp. 3439–3463. Download.

Mäkelä, J., Knauer, J., Aurela, M., Black, A., Heimann, M., Kobayashi, H., Lohila, A., Mammarella, I., Margolis, H., Markkanen, T., Susiluoto, J., Thum, T., Viskari, T., Zaehle, S., and Aalto, T. (2019). “Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH,” Geoscientific Model Development, 12(9), pp. 4075–4098. Download.

Susiluoto, J., Raivonen, M., Backman, L., Laine, M., Makela, J., Peltola, O., Vesala, T., and Aalto, T. (2018). “Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC,” Geoscientific Model Development, 11(3), pp. 1199–1228. Download.

Raivonen, M., Smolander, S., Backman, L., Susiluoto, J., Aalto, T., Markkanen, T., Mäkelä, J., Rinne, J., Peltola, O., Aurela, M., Lohila, A., Tomasic, M., Li, X., Larmola, T., Juutinen, S., Tuittila, E.-S., Heimann, M., Sevanto, S., Kleinen, T., Brovkin, V., and Vesala, T. (2017). “HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands,” Geoscientific Model Development, 10(12), pp. 4665–4691. Download.

Pulliainen, J., Aurela, M., Laurila, T., Aalto, T., Takala, M., Salminen, M., Kulmala, M., Barr, A., Heimann, M., Lindroth, A., Laaksonen, A., Derksen, C., Mäkelä, A., Markkanen, T., Lemmetyinen, J., Susiluoto, J., Dengel, S., Mammarella, I., Tuovinen, J-P, and Vesala, T. (2017). “Early snowmelt significantly enhances boreal springtime carbon uptake,” Proceedings of the National Academy of Sciences, 114(42), pp. 11081–11086, National Academy of Sciences. Download.

Mäkelä, J., Susiluoto, J., Markkanen, T., Aurela, M., Järvinen, H., Mammarella, I., Hagemann, S., and Aalto, T. (2016). “Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements,” Nonlinear Processes in Geophysics, 23(6), pp. 447–465. Download.