I’m a Fellow of the Climate Change Initiative of the European Space Agency (ESA-CCI) at the Department of Remote Sensing of the Helmholtz Centre for Environmental Research - UFZ. My research consists in exploring the possibilities offered by remote sensing data and machine learning methods to estimate ground surface fluxes at the global scale. Remote sensing observations from satellite instruments provide near global coverage of the land surface at high spatial and temporal resolutions, potentially complementing the information provided by micrometeorological observatories and subsurface temperature profiles. Machine learning techniques, such as deep neural networks, are powerful and flexible frameworks allowing to consider different processes for estimating ground heat fluxes. My goal is to expand our understanding of the least known term of the surface energy balance to enhance the estimates of important phenomena occurring near the land surface, such as extreme temperature events, soil respiration, and permafrost stability. As part of my research, I also work with reanalysis products and global climate simulations from the CMIP projects, assembling a large collection of heterogeneous datasets.