Digital Twin for Fertilization – D2D

The D2D project develops a digital twin for fertilization management to reduce the carbon footprint of arable farming. It combines hyperspectral Earth Observation (EO) data with the PROMET land surface model to dynamically simulate nitrogen processes and crop growth. By integrating satellite-derived information and hybrid retrieval methods (physics-based + machine learning), field conditions are monitored at high spatial detail. The system enables site-specific, model-driven fertilization decisions, improving nitrogen use efficiency and reducing emissions. D2D thus provides a forward-looking decision-support tool for sustainable agricultural management.