To some extent, the RF model outperforms other deep learning models. He speculated that one of the reasons might be that the deep learning method requires a large dataset for training, and that the feature variables selected for the experiments are strongly correlated with wheat yields and cannot fully exploit the advantages of the deep learning method for automatic feature extraction. The choice of model approach depends on the problem, the data and the corresponding requirements [22]. Both machine learning and deep learning have been widely used in the field of crop yield prediction. Machine learning and deep learning have a powerful ability to fit nonlinear relationships [23]. These two methods have their own advantages. For significantly large datasets, deep learning methods are better able to take advantage of handling complex relationships and automatically extracting features, and for ordinary datasets, the prediction performance of traditional machine learning methods is no worse than that of deep learning methods. [PERSON] conducted comparative experiments based on CNN-LSTM hybrid models for NDVI, EVI, LST and soil moisture and found that EVI performed the best. The results also showed that the hybrid model had less prediction error than a single CNN or LSTM [24]. [PERSON] et al. investigated the ability of combining physical indicators with NDVI remote sensing parameters to predict yield. The results showed that the multivariate regression model using the phenological indicators and NDVI was better than the NDVI univariate regression model in predicting yield [25]. [PERSON] et al. proposed three model architectures: CNN-LSTM, ConvLSTM and 3D-CNN using drone sequences and weather data. The results showed that 3D-CNN performed the best among all model architectures [26]. [PERSON] et al. proposed a CNN and LSTM dual branch model: an LSTM network branch based on remote sensing and meteorological data; a static soil feature model branch constructed using CNN. The evaluation results performed well, and the results also showed that yield prediction could be achieved at least one month before harvest [27]. [PERSON] et al. used 3D-CNN to train the feature data based on Sentinel-2 satellite data, meteorological data and soil data. The results showed that the yield prediction performance of 3D-CNN was better than 2D-CNN [28]. [PERSON] et al. used convolutional neural networks (CNNs) to learn the feature factors associated with rice growth. The results showed that the neural network trained by RGB and multispectral datasets performed better than the vegetation-index-based regression model in yield estimation [29]. [PERSON] et al. proposed a model architecture based on a bidirectional ConvLSTM network. Spatial features were first extracted using a convolutional neural network, and then the extracted spatial features were analyzed in time using a recurrent neural network (RNN) based on LSTM. The early crop map was predicted based on satellite data from Nebraska [30]. [PERSON] et al. converted MODIS surface reflectance and surface temperature into histograms based on Google Earth Engine (GEE). Combined with weather data, CNN-LSTM was used as the model architecture to predict the annual soybean yield in the United States [31]. The remote sensing data were downloaded based on the GEE platform by [PERSON] et al. Multiple classifiers in GEE were compared to generate high-resolution crop classification maps over large areas [32]. [PERSON] et al. developed a modeling framework based on GEE to integrate remote sensing data, meteorological data and soil data. Eight typical machine learning models were tested [33]. With the continuous development of remote sensing technology, the resolution of satellite remote sensing images has been increasing, and more and more accurate information about crops can be observed. Crop yield prediction at large spatial and temporal scales has a great demand for downloading and preprocessing satellite remote sensing data with multiple time series.