As part of the BELSPO funded research project, CropWaves - fostering food security by leveraging satellite data and models - I work on implementing global crop heat stress effects in the land surface model, Community Land Model v5 (CLM5) in Fortran and on assessing driving climatic components (ocean, land, atmosphere) behind specific crop failure events.

After obtaining my PhD degree, I continued on my doctoral research for three months, during which I used satellite-derived crop phenology data to run the regional AquaCrop setup over Europe in growing-degree-days instead of fixed calendar days.

During my PhD I worked on spatializing a crop model, AquaCrop, and evaluated the model output over various regions in Europe. Later, I used microwave backscatter observations from Sentinel-1, to extract information of crop biomass and surface soil moisture, which I then used to correct for the regional model estimates by employing a data assimilation framework. During my PhD, I became proficient in high performance computing using Linux and postprocessing in Python. My research was part of the Horizon 2020 project 'SHui', which focused on managing water scarcity in European and Chinese cropping systems.

For the duration of three months, I worked on a project for the World Meteorological Organization (WMO), in which I developed a rain-based methodology to define the onset and offset of crop growing seasons for the different agro-ecological zones of Senegal. I later helped the WMO set up a workshop around this, which was given in other countries with strong rainfall-based crop seasons.

I spent six months in Tasmania to study burnt peatland sites using hyperspectral imagery from drones. The purpose of the study was to map hydrological networks using 3D generated maps from the images over damaged locations, and inform local government on hydrological restoration.