Fusion of EO data for crop yield forecast in Benin and Morocco (FudCast)
Fachgebiet: Fernerkundung, Klimatologie, Modellierung
Gefördert durch: ESA
Projektleitung: Prof. Dr. Lukas Lehnert, Dr. Youssef Brouziyne
Laufzeit: 03/2022 - 02/2023
Rainfed agriculture is playing a major socioeconomic role in Africa by being the major food supplier, a significant labour market, and a key pillar in most national economies of the continent. Nevertheless, yields are low due to high dependence on heterogeneous spatiotemporal rainfall and poor growing standards. The combination of earth observation data and machine learning can provide promising toolboxes e.g., for real-time prediction of yields to proactively mitigate potential crop losses and humanitarian and economic consequences. FudCast aims at mapping and predicting yields of two strategic rainfed crops (wheat and maize) in pilot areas in Benin and Morocco. This will be achieved through the combination of earth-observation datasets based on: Sentinel 1, Sentinel 2 and Sentinel 5 data, crop models, ground observation. The developed open-source solution will help in predicting yields with six weeks of lead time, will be transferable to other areas on the continent and will be a basis for capacity building in courses at the master and PhD level.