Yield Prediction and Estimation from Earth Observation (YIPEEO)
Crop yield forecasting is a vital tool to support stakeholders and decision-makers in preparing for potential yield deficiencies. Most crop yield forecasts so far have been implemented on a regional to national scale. Field-scale forecasts can add vital information for farmers and insurers but still have much potential to improve. Especially the increasing availability of high-resolution climate data from sources such as Copernicus Sentinel-1, Sentinel-2, Sentinel-3 data, and Proba-V can significantly improve such forecasts.
The goal of the YIPEEO project is to improve field-scale crop yield forecasts by using these datasets in combination with novel machine-learning techniques or crop growth models. For this purpose, we are working with various field datasets distributed over Europe (Ukraine, Finland, Netherlands, Denmark, Italy, and several in central Europe). In addition, we will explore the impact of droughts on crop yields, assess the impact of the war on Ukraine’s crop production, and develop an irrigation timing and fertilizer advisory tool.
March 2023 – February 2025
Field-scale crop yield forecasting using Earth Observation, airborne, and in-situ data and different machine learning and crop growth models
Detailed error analysis and accuracy estimation of developed crop yield models at different test sites
Assess the value of irrigation for enhancing crop production and mitigating yield losses
Impact of the war in Ukraine on crop production and yields is assessed, by quantifying natural variability of yield in Ukraine at NUTS3 level and deviations from that per region.
CLIMERS members involved