Remote sensing of plant stress with hyperspectral-fluorescence imaging to track crop response to environmental and biotic sources

 

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Applications are no longer being accepted for this project

SIF is an optical signal allowing remote sensing of plant photosynthetic efficiency and to detect plant physiological reactions (e.g. stress) to environmental changes (Mohammed et al. 2019, Porcar-Castell et al. 2021). The most advanced SIF measuring techniques, applied at leaf or canopy levels, use ground-based sensors and are, therefore, of small spatial extent (local) and time-consuming.

Automated measurements from instruments installed on towers provide frequent observations over longer intervals but their spatial extent is also locally constrained (Aasen et al. 2019). To observe larger areas, airborne and satellite measurements are necessary, but they are too coarse to investigate local phenomena and they are not always available when needed (Wen et al. 2020).

The overall research project aims to close this gap by establishing a methodology for efficient measurements of photosynthetic efficiency in crops with two perspective drone-based and aircraft-based approaches. Although drone remote sensing is already delivering exciting data for agricultural applications (Quiros et al. 2020), in this project, we aim to use newly developed drone sensors for measuring SIF and airborne high-performance airborne sensors to reveal spatial heterogeneity of canopy SIF in the field and deliver diurnal measurements throughout a crop’s growing cycle.

The University of Melbourne-based project focuses on developing new indicators of pre-visual plant stress using airborne imaging spectroscopy and thermal imaging in the context of biotic (plant diseases) and abiotic (water and nutrient) stress. In particular, the proposal targets remote sensing methods based on narrowband and sub-nanometer hyperspectral imaging and high-resolution thermal imaging acquired onboard manned and unmanned aerial vehicles. This imaging will be used to develop physiological indicators related to photosynthetic functioning through chlorophyll fluorescence quantification, pigment alterations and degradation, and investigating the blue spectral region for the potential assessment of blue fluorescence and pigment changes associated with early symptoms of stress.

The Jülich-based project aims to develop a solid protocol for measuring SIF from drones. So far, some measurements provide data that cover larger areas occasionally or data that is limited to one static location but delivering a detailed time series. Drones enable us to get data more often from a reasonably sized area, which means that spatial patterns can be investigated as well as temporal developments e.g. the growing cycle of a crop. Some technical challenges arise when sensing SIF with a drone, which led to two promising sensor prototypes that will both be tested and applied on agricultural test sites within this project. This project enables high temporal resolution time series of solar-induced chlorophyll fluorescence (SIF) measurements in agricultural fields.

Project goals

These projects aim to:

  • Establish robust drone-based sensing of solar-induced fluorescence (SIF) (FZJ).
  • Develop protocols for systematic assessment of spatio-temporal dynamics of SIF (FZJ).
  • Combine SIF, thermal and visible and near-infrared remote sensing techniques to assess biotic and abiotic sources of stress (UoM).
  • To develop new indicators for pre-visual plant stress using physically-based and machine learning models (UoM).

Supervision team

The University of Melbourne: Professor Pablo J Zarco-Tejada

Forschungszentrum Jülich: Professor Uwe Rascher

*Click on the researcher's name above to learn more about their publication and grant successes.

Who we are looking for

We are seeking PhD candidates with the following skills:

  • A masters qualification in Remote Sensing, Geography, Spatial Sciences, Ecology, Agronomy, Plant Physiology, Biology or related disciplines.
  • Demonstrated experience in the field of optical remote sensing and spatial data analysis.
  • Demonstrated quantitative skills, including programming for data analysis.
  • Ability to perform physical work outside.
  • A driver’s licence is advantageous.
  • Demonstrated ability to work independently and as part of a team.
  • Demonstrated time and project management skills.
  • Demonstrated ability to write research reports or other publications to a publishable standard (even if not published to date).
  • Excellent written and oral communication skills.
  • Demonstrated organisational skills, time management and ability to work to priorities.
  • Demonstrated problem-solving abilities.

Further details

  • Two PhD projects are available. One candidate will be based at University of Melbourne with a minimum twelve-month stay at Forschungszentrum Jülich. The FZJ candidate will be based in Jülich, and will spend a minimum of 12 months at UoM.
  • The PhD candidate will benefit from the combined expertise of the project supervisors, and the embedding into two research environments.
  • The candidate will be enrolled in the PhD program at the University of Bonn, and in the PhD program at the School of Veterinary and Agricultural Sciences University of Melbourne.

Applications are no longer being accepted for this project

First published on 14 June 2022.

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