Mariana Belgiu has a PhD in Remote Sensing from the University of Salzburg (Austria) and a MSc degree in Applied Geoinformatics from the same university. Her PhD research focused on land use and land cover classification from remote sensing data by combining expert knowledge and machine learning algorithms. After her PhD, she continued her postdoctoral research at the University of Salzburg with a focus on integrating expert knowledge into remote sensing data retrieval and analysis. Currently, she is an Assistant Professor (tenure track) at the University of Twente, Earth Observation Science department. Her research focuses on combining machine learning methods and multi-temporal satellite images for various food security applications such as agriculture mapping and monitoring or assessing the nutritional quality of crop grains. The results of her work are relevant to food security programs dedicated to integrating nutrition-related interventions into agricultural management plans and, thus, to addressing the nutrients malnutrition problems prevalent in developing countries. Due to her scientific activities, she became an Associate Editor of the ISPRS Journal of Photogrammetry and Remote Sensing and is a member of several commissions of trust and scientific societies: member of the scientific committee of IGARSS 2021, panel member of the Belgian Earth Observation Research Program STEREO III, committee member for evaluating Dutch Research Council (NWO) ENW PPS-fonds proposals, member of the Romanian National Research Council etc.. She is enthusiastically sharing her knowledge on remote sensing and machine learning concepts, theories, and methods in several courses taught at the University of Twente and is actively involved in capacity-building projects funded by the Nuffic Orange Knowledge Program.

HyNutri Project

In this research, we aim to evaluate the potential of European Space Agency (ESA) Sentinel-2 multispectral broadband (MSBB) and recently available Italian Space Agency (Agenzia Spaziale Italiana - ASI) PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral narrowband (HNB) data to estimate and predict the abundance of macro- and micro-nutrients in crops critical to evaluating nutritional status at the agricultural field level.

Image Analysis Course

Advanced Image Analysis (course coordinator)