A Deep Learning-Driven Spatio-Temporal Framework for Timely Corn Yield Estimation Across Multiple Remote Sensing Scenarios

Crop yield estimation, particularly early-season yield prediction, is highly important for global food security and disaster mitigation. In this study, we utilized deep learning models combined with remote sensing data to develop in-season crop yield estimation models, enabling immediate yield prediction. We employed a convolutional neural network (CNN) for spatial feature extraction and a long short-term memory network (LSTM) for temporal patterns, complemented by Gaussian process regression (GP) that introduced geographical coordinates. Three groups of in-season yield prediction experiments were designed, utilizing four-phase, two-phase, and single-phase data, respectively. The results indicated that under the two-phase training scheme, the LSTMGP model achieved the highest performance in the sixth period, with an R2 value of 0. 61 and a root mean square error (RMSE) value of 983. 38 kg/ha. When trained on single-phase data at the twelfth phase (approximately mid-to-late July), the LSTMGP model also performed best, attaining an R2 value of 0. 62 and an RMSE value of 969. 06 kg/ha. The single-phase prediction model outperformed time-series models in yield prediction accuracy. The periods from mid-to-late July to early-to-mid August represent critical crop growth stages were essential for accurate yield prediction. From our research, we found that adding GP can improve the prediction accuracy, especially for LSTM. Moreover, the proposed single-phase prediction model realized reliable crop yield prediction as well as the silking to early grain-filling stage (mid-to-late July), providing a critical lead time of approximately 2–2. 5 months before harvest to support pre-harvest agricultural decision-making.