WG4GEE: a GEE-integrated regional winter wheat growth model for efficient agronomic process and yield simulation

Spatial crop growth models are critical for agronomic decision-making, especially in assessing climate change impacts on productivity. However, regional applications of point-scale models are limited by insufficient input data and computational resources. This study aims to address these challenges by developing WG4GEE—a regional winter wheat growth model integrated with Google Earth Engine (GEE). The GEE platform provides high-resolution weather and soil data for the model, which runs on GEE's cloud computing environment. Validated using 2014–2019 winter wheat yield data from China, WG4GEE completed regional yield simulation in 7 h, achieving an R2 of 0.62 and an NRMSE of 15.57%. Simulated LAI time series accurately reflected wheat growth dynamics. Compared with traditional models, WG4GEE simplifies data acquisition, enhances efficiency via cloud computing, and reduces local resource reliance. This study confirms GEE's value for regional agronomic models, offering a tool for large-scale crop monitoring, yield prediction, and climate-adaptive management.