CrYP: An open-source Google earth engine tool for spatially explicit crop yield predictions

Accurate yield forecasts are essential for supporting stakeholders in making informed strategic and tactical decisions. Process-based models are widely used for predicting crop yields, as they reproduce plant phenology and physiology in response to environmental conditions and agricultural practices. However, most crop models are point-based and must be integrated into spatially explicit simulation environments to produce yield predictions over larger areas at the desired spatial resolution. Remote sensing can provide critical data to inform crop models, offering consistent and high-quality observations of actual vegetation dynamics with time and space continuity. This study presents the Crop Yield Prediction (CrYP) app, an open-source tool designed for pixel-level crop yield forecasting over large regions. CrYP runs on the Google Earth Engine platform, applying a simple crop model executed in real time across geographic areas. The app uses ERA5-Land weather data and MODIS-derived Normalized Difference Vegetation Index, and can be adapted to different crops by tuning a few physiologically meaningful parameters. As a proof of concept, CrYP was tested on maize in the U.S. Corn Belt and on wheat and barley in two Italian regions. Results show that CrYP accurately captured seasonal crop phenology and the effects of abiotic stresses in both case studies, producing yield predictions consistent with official statistics. CrYP introduces an innovative approach for yield forecasting by assimilating remotely sensed data and real-time observed phenology into a process-based crop model. It also holds significant potential for near-real-time crop monitoring at local and regional scales, facilitating the timely identification of food security hotspots where adaptation strategies can be timely implemented.