Food security remains a global challenge. ITC contributes to local and global food security initiatives by combining the latest EO technology with field, lab, numerical and artificial intelligence methods to better understand "where" certain crops are and "when", and "how" their production is affected by various biotic and abiotic stressors.

You have good experience in processing remote sensing data (preferably hyperspectral data) of vegetation covers. You will analyse various remote sensing data and deep learning to map crop boundaries by performing image analysis methods such as segmentation. You will also evaluate the stress in cereal crops by utilising physically based models (RTMs), empirical models, hyperspectral EO and field measurements to understand the crop stress effects on crop traits and remote sensing signatures.

You will also develop and maintain a strong presence in EO community involved in crop mapping and monitoring, perform field/lab spectral and physiological measurements and identify relevant research and capacity-building opportunities related to the use of EO in crop mapping and monitoring. Based on identified opportunities, you will pursue and attract funding that builds on ITC's geospatial expertise, develop scientific articles and publish them in ISI journals and develop material for case studies related to project works which could be brought into challenge-based components of ITC's M.Sc. programmes.