Within CHILL-Y, representative datasets from three upcoming Sentinel Expansion missions (LSTM, CHIME, and ROSE-L) will be generated and used to demonstrate a novel service for improved assessment of yield quantity and quality. To achieve this, agricultural parameters will be derived from these thermal, hyperspectral, and L-band radar datasets and assimilated into a physical crop growth model, which will then be combined with an AI model.
The novel datasets and methods will be tested and evaluated for wheat and sugar beet in several AOIs across Europe in cooperation with stakeholders and so-called Champion Users, providing validation data. This ensures knowledge transfer and future benefits from the developed solution for the users by incorporating their needs in the co-development process.
CHILL-Y Home Project Results Repository
