To explore the feasibility of using remote sensing technology and deep learning methods to achieve early and accurate prediction of winter wheat yield on a large scale, the northern Henan region of China was taken as the research area. Using the normalized difference vegetation index, temperature, sunshine hours, precipitation and county-level yield during the period from re-greening to maturity of winter wheat from 2013 to 2022 as characteristic parameters. The Long Short-term memory (LSTM)-Convolutional Neural Networks (CNN) was used to estimate the winter wheat yield in the northern Henan region. By training the CNN-LSTM yield estimation models at different growth stages one by one, their early yield estimation ability was compared. The results showed that the CNN-LSTM yield estimation model in re-greening-maturity stages had relatively strong robustness. The average coefficient of determination (R²) in the five-fold cross-validation was 0.86, and the average root mean square error (RMSE) was 402.76 kg·hm^-2. There was a significant improvement in the yield estimation accuracy compared with the corresponding LSTM model, with the average R² increased by 0.18, and the average RMSE decreased by 188.05 kg·hm^-2. The CNN-LSTM yield estimation model in re-greening-maturity stages was a model with high stability and strong generalization ability. The R², RMSE, and mean absolute percentage error (MAPE) for yield estimation of the 2022 test set were 0.91, 337.25 kg·hm^-2, and 4.25%, respectively. Among the 32 counties (districts) in northern Henan, 75% of them had a relative error less than 5% in yield prediction, of which 21.875% had a relative error of 5% to 10% in yield prediction, and only one county (district) in Linzhou City had a relative error of 15% to 20% in yield prediction. The CNN-LSTM yield estimation model had early yield estimation ability. The R² and RMSE of the CNN-LSTM yield estimation model during the heading-anthesis stage were 0.83 and 431.98 kg·hm^-2, respectively. This model had the ability to predict winter wheat yield one month in advance. Therefore, the CNN-LSTM could be used to establish a stable and accurate winter wheat yield estimation model, thus providing strong support for agricultural decision-making.