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

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.

• 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).