Thesis subject
MSc thesis topic: Unmixing of satellite-based sun-induced fluorescence at scale
Sun-Induced chlorophyll Fluorescence (SIF) is an important indicator of vegetation health and photosynthesis in plants, as it can be used as an early warning sign of plant stress. However, it is difficult to retrieve from remote sensing imagery and to upscale to large areas. Global products have been derived largely from atmospheric monitoring satellites, leading to a 5 km spatial resolution at best. The upcoming FLEX satellite will be a major improvement, as it will provide 300 m SIF data. Nevertheless, even at 300 m, the product will be of limited use at field scales. A promising technique to unmix SIF data is to make use of finer spatial resolution data, such as land cover fractions or Sentinel-2 surface reflectance.
Background
FLEX will fly in tandem with Sentinel-3 satellites and will share the grid and resolution. The Sentinel-3 constellation has been collecting data since 2016, including bands at the oxygen absorption bands that are needed to retrieve SIF at 300 m scale. Nevertheless, Sentinel-3 remains underutilised for SIF research. Using Google Earth Engine (GEE), SIF can be retrieved from Sentinel-3 imagery. By combining it with finer resolution products available in GEE, such as land cover fractions from the Copernicus Global Land Cover 100 m product and 10-20 m Sentinel-2 surface reflectance products, using machine learning, the 300 m SIF signal can be further unmixed. A radiative transfer modelling approach can also be used. The 300 m and derived finer resolution imagery can then be compared with the prior 5 km product, as well as with aerial imagery, to determine the model accuracy.
Relevance to research/projects at GRS or other groups
The research topic is part on an ongoing field of research into measuring and modelling SIF within the GRS group, led by prof. Lammert Kooistra.
Objectives and Research questions
- How accurate is SIF retrieved from Sentinel-3 OLCI imagery?
- How can 300 m SIF be disaggregated to a finer spatial resolution?
- How much does the modelled SIF accuracy differ across spatial scales?
- How useful is it to include external data into the model, such as land cover fractions and finer spatial resolution imagery?
Requirements
- Required: Geoscripting, Remote Sensing
- Optional: Advanced Earth Observation, Machine Learning
Literature and information
- Di Cicco, A., Gupana, R., Damm, A., Colella, S., Angelini, F., Fiorani, L., ... & De Vincenzi, M. (2020). “Flex 2018” Cruise: an opportunity to assess phytoplankton chlorophyll fluorescence retrieval at different observative scales. Proceedings e report, 688-697.
- Taveira, G., Rivas, R., & Beninato, S. (2023, November). Qualitative Estimation of Sun-Induced Chlorophyll Fluorescence (SIF) Using Sentinel-3 OLCI Sensor Data. In 2023 XX Workshop on Information Processing and Control (RPIC) (pp. 1-6). IEEE.
Expected reading list before starting the thesis research
- Jantol, N., Prikaziuk, E., Celesti, M., Hernandez-Sequeira, I., Tomelleri, E., Pacheco-Labrador, J., ... & Cendrero-Mateo, M. P. (2023). Using Sentinel-2-based metrics to characterize the spatial heterogeneity of FLEX sun-induced chlorophyll fluorescence on sub-pixel scale. Remote sensing, 15(19), 4835.
- Masiliūnas, D., Tsendbazar, N.-E., Herold, M., Lesiv, M., Buchhorn, M., & Verbesselt, J. (2021). Global land characterisation using land cover fractions at 100 m resolution. Remote Sensing of Environment, 259, 112409.
- Mohammed et al (2019). Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress.
- N Wang, B Siegmann, U Rascher, JGPW Clevers, O Muller, ..., 2022. Comparison of a UAV-and an airborne-based system to acquire far-red sun-induced chlorophyll fluorescence measurements over structurally different crops. Agricultural and Forest Meteorology 323, 109081.
Theme(s): Sensing & measuring; Integrated Land Monitoring