Timely and accurately estimating regional winter wheat yield is critical for maintaining national food secu-rity and sustainable agricultural development. In this study, we used remote sensing, meteorological data, and county yields from 2001 to 2018 in China’s main winter wheat producing areas to build a yield estima-tion model based on the Long Short Term Memory Networks(LSTM), compare the yield estimation perfor-mance of different models with Random Forest (RF), Support Vector Machine(SVR), and Decision Tree(DT) models, analyze the effects of different feature combinations on model accuracy, and evaluate the advance prediction ability of the models. The results show that: 1) the model based on all data has the highest accuracy with an average R2 of 0.853 and an average NRMSE of 7.22%. When compared with the DT, RF and SVR models, the LSTM model improves R2 by 0.324, 0.088 and 0.028; 2) Photosynthe-sis-related surface downward longwave radiation (lrad) (R2, 0.737), nstantaneous near surface air tempera-ture(temp) (R2, 0.747), surface downward shortwave radiation (srad) (R2, 0.735) and precipitation rate (prec) (R2, 0.681) surpass other single features in yield estimation. Adding more features to a single feature would increase yield estimation accuracy. The contribution of meteorological data, band reflectance, and vegeta-tion index to yield estimation decrease in order. Using three data sets, the accuracy of yield estimation is highest (R2 0.866, NRMSE 7.00%). 3) Winter wheat fertility data lasts from October 8 to June 10 of the following year, with one time-phase every eight days and a total of 32 time-phase data. The ability of the LSTM model to predict yield increases in time phases 1~6, plateaus in time phases 7~19, increases again in time phases 20~29 and remains steady without further increase in time phases 30~32. The LSTM model based on three data sources achieves the highest yield forecast accuracy (R2 0.873, NRMSE 6.90%) 24 days earlier using data from the first 29 time phases. The method in this study has high yield estimation accuracy and can achieve early yield prediction, which can provide an efficient and reliable way to estimate winter wheat yield in large areas for agricultural management and economic activities.