PhD project offered by the IMPRS-gBGC in Jan 2024

Detecting ecosystem stress from space using synergistic remote sensing and fluorescence

Gregory Duveiller

Project description

Ecosystems are under increasing stress as a result of changing climate. Detecting and monitoring ecosystem health efficiently is becoming increasingly necessary. Satellite-based remote sensing provides a valuable tool for this task, as such technology can provide global spatial coverage with high temporal revisit. However, conventional optical remote sensing approaches offer limited potential for the early detection of ecosystem stress, as changes in ecosystem structure and function often need to be substantial in order to be detectable when using reflectance in the visible and near-infrared range of the energy spectrum. In the past decade, the promise of a direct measurement of photosynthetic activity from space has appeared: spaceborne sun-induced chlorophyll fluorescence (SIF)¹.
Chlorophyll fluorescence is generated when solar energy absorbed by chlorophyll inside plant leaves is not used for photosynthesis or dissipated as heat, but is instead emitted at a slightly higher wavelength². Chlorophyll fluorescence thus results from fine-tuned changes in chlorophyll energy partitioning and SIF provides a highly sensitive optical signal, which allows the early detection of plant stress before symptoms become apparent in classical optical remote sensing indices. However, to correctly diagnose whether or not plants are exposed to such stress, SIF needs to be quantified jointly with the energy that is dissipated as heat, which is not easy to do. Furthermore, the SIF signal itself is very weak and difficult to retrieve. In practice, currently available spaceborne SIF comes at a coarse spatial resolution with a signal contained confounding information from both physiology (expressing stress) and structure, undermining the potential to quantify ecosystem stress.
This PhD will explore novel approaches to tackle this problem. The hypothesis is that by combining together different synergistic remote sensing data streams using hybrid modelling approaches, whereby process-based understanding is interwoven with machine learning or deep learning techniques³, an effective signal of ecosystem stress can be generated and contribute towards the general goal of monitoring ecosystem stress.

Working group

The PhD candidate will work in the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry with Gregory Duveiller in the group Ecosystem Function from Earth Observation (EFEO).

Requirements for the PhD project are

Applications to the IMPRS-gBGC are open to well-motivated and highly-qualified students from all countries. Prerequisites for this PhD project are:

either Master in Earth System Science, Plant Physiology, Ecology, Agronomy, Forestry or Geography
programming experience in a modern computing language (R, Python, Julia)
experience in satellite remote sensing
good knowledge and interest in plant physiology and ecosystem functioning
experience in machine learning and deep learning is beneficial but not mandatory
interest in working in an interdisciplinary team of ecologists and remote sensing scientists
very good oral and written communication skills in English

The Max Planck Society (MPS) strives for gender equality and diversity. The MPS aims to increase the proportion of women in areas where they are underrepresented. Women are therefore explicitly encouraged to apply. We welcome applications from all fields. The Max Planck Society has set itself the goal of employing more severely disabled people. Applications from severely disabled persons are expressly encouraged.

References

1. Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. Geophysical Research Letters 38, (2011).
2. Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of Experimental Botany 65, 4065–4095 (2014).
3. Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).