Accurate monitoring of crop yield is important for ensuring food security. Current yield estimation methods, such as machine learning models or the assimilation of remotely sensed biophysical variables into crop growth models, depend heavily on ground observations and involve significant computational costs. To solve these problems, a hybrid framework coupling the World Food Studies Simulation Model (WOFOST) and the Gated Recurrent Unit model (GRU) was proposed for soybean yield estimation in Northeast China from 2019 to 2023. This dataset provides 20 m annual soybena yield in Northeast China from 2019 to 2023. *** The data file is in “.tif" format *** Temporal Resolution: annually *** Temporal coverage: 2019-2023 *** Pixel size: 20 m *** Projection information: EPSG: 4326