Starting point / motivation
When it comes to monitoring the health status of ecosystems, satellite-based remote sensing approaches hit a sweet spot in terms of global spatial coverage and temporal resolution. Conventional optical remote sensing approaches, however, 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 from the reflectance in the visible and near-infrared range of the energy spectrum.
Satellite-based remote sensing of sun-induced chlorophyll fluorescence (SIF) offers much greater potential to that end. 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 wavelength. 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.
Contents and goals
Our previous work, however, has shown that in order to correctly diagnose whether or not plants are exposed to stress, SIF needs to be quantified jointly with the energy that is dissipated as heat and that this process can be accurately quantified on the basis of reflectance changes around the green peak, exploited by the so-called photochemical reflectance index (PRI).
SIF data have become available from a few satellite platforms during the past couple of years, however their spatio-temporal resolution and signal-to-noise ratio is still unsatisfactory. A major step forward in data quality is expected from the upcoming ESA Earth Explorer mission FLEX, scheduled to launch in mid-2023.
The overarching goal of the proposed project is to make present and future satellite-based sun-induced chlorophyll fluorescence measurements a sensitive and reliable means for the early detection of ecosystem stress by combining remotely sensed SIF and PRI. To that end we propose to simulate satellite measurements using ground-based, proximal sensing of active and passive chlorophyll fluorescence and hyperspectral reflectance. These measurements will be conducted in the field covering a wide range of ecosystems typical for Austria.
Available satellite products will be used to test this approach at larger spatial and temporal scales. Process-based models will be used to disentangle the underlying drivers and will then be used together with the experimental data to derive best practise guidelines for diagnosing early ecosystem stress on the basis of combined SIF and PRI data.
University of Innsbruck
University of Innsbruck