## 2 Materials and Methods
### Preprocessing in GEE  
As for deep-learning-based prediction processing, most methods prefer to select the mean or VI of regions as features because these methods have low computational complexity [31]. The minimum outsourcing rectangle size varies from county to county, and the large amount of raw remote sensing data fed directly into the deep learning model requires enormous computational power. Thus, we converted the remote sensing images into histograms by referring to others' research in order to improve the computational efficiency. In this study, remote sensing data were downloaded based on the GEE platform, and the raw images were preprocessed. Figure 2 shows the flow of data processing based on the GEE platform. The preprocessing steps are as follows. 1. Download data. Download remote sensing data from 2000 to 2019, April to October. A 16-day synthesis of 8 days of MODIS GPP data and daily air temperature data from ERA5 was performed. Alignment with MODIS EVI and MODIS SAVI;  
Figure 2: GEE processing flow. Rice masks and county boundary layers. The rice mask layer is used to process the remote sensing data and eliminate the interference from other vegetation on the ground. The county boundary data of Hubei province are imported into GEE to extract remote sensing data of each county;
3. Convert the histogram. GEE provides a convenient and fast API to convert image collections into county-level 32-bin normalized histograms;
4. A dummy variable. After numbering each county in Hubei Province, the output is a 32-bin histogram in a uniform format, which is added as a factor to the feature;
5. In this study, annual time steps of 14, with 5 bands per time step, were converted to histograms and then input into the model. The format of the input variables is 32 \(\times\) 5 \(\times\) 14. The corresponding county-level yield was assigned to each input variable based on the obtained rice yield statistics in Hubei Province. Model Architecture
1. CNN. Convolutional neural network (CNN) is a neural network with convolutional structure. The basic components are input layer, convolutional layer, pooling layer, fully connected layer and output layer. Convolutional layers are linked to the input layer using local weighting and weight sharing. The features of the input data are extracted by convolutional kernels. The pooling layer reduces the amount of data for convolution operations. After the convolution and pooling layers, one or more fully connected layers are usually connected. Fully connected layers can integrate local information with category differentiation in the convolutional or pooling layers [41]. The output value of the last fully connected layer is passed to the output layer. The main component of the CNN model is the convolutional operation. CNN uses a convolutional kernel applied to the input variables to produce a set of spatial features of the input data by convolutional operations. In this paper, we set up two convolutional layers, Conv2D. The first layer was set up with 32 filters and the convolutional kernel size was 3 \(\times\) 3. The second layer was set up with 64 filters and the convolutional kernel size was 3 \(\times\) 3. The pooling layer used the maximum pooling method. 2. ConvLSTM. The long short-term memory network (LSTM) is a modified version of the recurrent neural network (RNN). Unlike CNNs, the neurons in RNNs have a feedback structure. This feedback structure enables the previous data to receive the influence of the later data. Therefore, recurrent neural networks have better performance when dealing with temporally correlated sequential data. LSTM effectively improves the problem of gradient explosion, which exists in recurrent neural networks and makes it difficult to learn the relationship between long interval data, by filtering the information obtained through the gate function. Convolutional LSTM (ConvLSTM) [42] is a model that combines features of convolutional and sequential models into a single architecture. Use the convolutional layer as the gate function of the LSTM [26].