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High-dimensional probabilistic dynamic models for global vegetation change and weather forecasting - SENSE CDT

   School of Mathematics

   Wednesday, January 03, 2024  Competition Funded PhD Project (UK Students Only)

About the Project

The Earth’s vegetation is changing as a result of both human activity and climate change. Large scale shifts in vegetation will fundamentally alter terrestrial ecosystems, with a range of potential consequences – from impacts on biodiversity to altered carbon and hydrological cycling. In northern high latitudes plants are growing more as the climate warms, resulting in a “greening” of the land surface. Within the next 50 years the tundra biome is expected to become climatically suitable for trees, the boreal treeline is already shifting northwards and woody shrub abundance in tundra is increasing. These changes will have a profound impact on ecosystem function and climate feedbacks; while CO2 uptake from the atmosphere through photosynthesis is likely to increase, taller denser plant canopies will decrease the reflectivity of the land surface, resulting in greater warming. To understand the implications of changing vegetation distributions, it is vital we can model important biophysical parameters from space over time. Moreover, interactions between land and atmosphere also affect the weather. Thus, understanding and modeling these interactions through statistical models is of utmost importance for weather forecasting.

These spatio-temporal problems can be described through statistical models that relate the sequential observed data to a dynamical hidden process through some unobserved static model parameters. These parameters are often interpretable and are essential for prediction purposes, also providing domain experts with further understanding. In the Bayesian framework, the probabilistic estimation of the unknowns is represented by the posterior distribution of these parameters. Learning those distributions is crucial not only for prediction/forecasting purposes but also for uncertainty quantification. Unfortunately, in most realistic models for earth observation problems, the posteriors of both static and dynamic parameters are intractable and must be approximated. Importance Sampling (IS)-based algorithms are Monte Carlo methods that have shown a satisfactory performance in many problems of Bayesian inference, both for inferring static parameters and the hidden states [1]. Particle filters (also called sequential Monte Carlo methods) are the de facto IS-based computational tools in this context. See [2] and [3] for two tutorials in adaptive IS and particle filtering, respectively.

In this project, we focus in developing inferential tools for probabilistic spatio-temporal models with applications in earth observation problems. We consider the challenging problem of estimating biophysical parameters and atmospheric entrainment parameters from remote sensing (satellite) observations acquired across time. Just as an example, let us focus in the aforementioned problem where the estimation of the evolving Leaf Area Index (LAI) is key for forecasting the change of Earth’s vegetation. It is important to track evolution of LAI through time in every spatial position on Earth because LAI plays an important role in vegetation processes such as photosynthesis and transpiration, and is connected to meteorological/climate and ecological land processes [4, 5]. We will propose novel computational methods in order to overcome current limitations of more traditional IS-based techniques in such a challenging context, including adaptive IS methods for learning static parameters in high dimensional spaces [6] and extensions of [7] to observational spaces with big amount of data. Many applications in earth observation can be benefited from the development of these methodologies. See [5] and [8] for the application of recent IS methodological advances in remote sensing problems.

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 and field training. All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See

More information for applicants can be found on our available ProjectsHow to Apply and dedicated FAQ webpages. You will also find additional resources such as How can a PhD help me with my career?

Computer Science (8) Environmental Sciences (13) Mathematics (25)

Funding Notes

This 3 year 9 month long NERC SENSE CDT award will provide tuition fees, tax-free stipend at the UK research council rate (£18,662 for 2023/24), and a research training and support grant to support national and international conference travel.


[1] C. P. Robert and G. Casella, Monte Carlo Statistical Methods. Springer, 2004.
[2] M. F. Bugallo, V. Elvira, L. Martino, D. Luengo, J. Miguez, and P. M. Djuric, “Adaptive importance sampling: the past, the present, and the future,” IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 60 79, 2017.
[3] A. Doucet and A. M. Johansen, “A tutorial on particle filtering and smoothing: Fifteen years later,” Handbook of nonlinear filtering, vol. 12, no. 656-704, p. 3, 2009.
[4] J. M. Chen and T. A. Black, “Defining leaf area index for non-flat leaves,” Plant, Cell & Environment, vol. 15, no. 4, pp. 421–429, 1992.
[5] L. Martino, V. Elvira, and G. Camps-Valls, “Group importance sampling for particle filtering and mcmc,” Digital Signal Processing, vol. 82, pp. 133–151, 2018.
[6] L. Martino, V. Elvira, D. Luengo, and J. Corander, “Layered adaptive importance sampling,” Stat. Comput., vol. 27,
no. 3, pp. 599–623, May 2017.
[7] V. Elvira, J. Miguez, and P. M. Djuric, “Adapting the Number of Particles in Sequential Monte Carlo Methods Through an Online Scheme for Convergence Assessment,” IEEE Trans. Sig. Proc., vol. 65, no. 7, pp. 1781–1794, 2017.
[8] L. Martino, V. Elvira, and G. Camps-Valls, “The recycling gibbs sampler for efficient learning,” Digital Signal Processing, vol. 74, pp. 1–13, 2018.

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