18.06.2025PhD Defense Jasmin Kesselring

In her thesis she explored how the inherent top-view perspective of satellite remote sensing can come with potential limitations in estimating evapotranspiration (ET) and gross primary production (GPP).  “Satellite observations provide important insights, but I wanted to raise awareness in the science community that there can be limitations coming from the top-view perspective. I wanted to show, quantitatively, how much uncertainty that introduces,” Jasmin explained.

For her dissertation, Jasmin collected data in the dense, evergreen coniferous forest in Davos and the more open, deciduous broadleaf forest in Lägern, which provided distinct canopy structures for comparison. Jasmin integrated multiple data sources, including in situ eddy flux tower data, GNSS-derived data of vegetation optical depth (VOD), and meteorological observations. To quantify the uncertainty in estimates of evapotranspiration and gross primary productivity introduced by the top-view remote sensing perspective, she used 3D radiative transfer modeling (DART), satellite data, and in situ eddy flux measurements. She created virtual scenes and evaluated how Sentinel-2 satellite observations align with actual conditions within the forest canopy.  

Jasmin’s findings demonstrate that accounting for vertical variation within the forest canopy significantly enhances the accuracy of satellite-based estimates of evapotranspiration (ET) and gross primary production (GPP). Her research shows that both canopy structure and the distribution of light within the have an impact on the accuracy of satellite-based estimates of evapotranspiration and gross primary production. To address the limitations of traditional top-of-canopy remote sensing, she emphasizes the potential of integrating complementary technologies such as LiDAR and radar. She also sees potential in scaling her methods through machine learning.

Congratulations Jasmin on this important contribution! We wish you all the best for your future!