Winter wheat yield prediction based on convolutional neural network–bidirectional long short-term memory–attention model using time series synthetic aperture radar images

Timely and accurate prediction of winter wheat yield is crucial for national food security. In the field of crop yield prediction, deep learning techniques are playing an increasingly important role. However, many existing methods mainly utilize convolutional neural network (CNN) or long short-term memory (LSTM) network, failing to fully exploit the spatiotemporal information in remote sensing data. To address this issue, a CNN–bidirectional long short-term memory (BiLSTM)–attention model for winter wheat yield prediction was proposed using time series Sentinel-1A synthetic aperture radar images. The histogram dimension reduction technique was employed to generate the samples. The CNN was used to extract the spatial–spectral features from the image samples, and the BiLSTM network was adopted to learn the temporal features of winter wheat growth stages from the time series samples. Furthermore, an attention mechanism was introduced to make the networks learn important features more efficiently to improve the accuracy of yield prediction. The time series Sentinel-1A synthetic aperture radar images covering Weishi County, Kaifeng city, Henan province, China, were used for model training and validation. The experimental results demonstrated that the proposed model exhibited good accuracy in yield prediction for the study area, with a coefficient of determination of 0.79, a root mean square error of 583.53 kg/ha, and a mean absolute error of 458.41 kg/ha. The proposed method has a promising application in crop yield prediction and provides a useful reference for similar crop yield prediction.