## 4 Discussion
[PERSON] also obtained the worst results for ConvLSTM prediction when testing the prediction performance of ConvLSTM, 3D-CNN and CNN-LSTM model architectures [26]. He thought, based on [PERSON]'s experiments, that it was likely that ConvLSTM would also require a pretrained CNN similar to the CNN-LSTM hybrid model. On the other hand, the CNN-LSTM model is more complex compared to the ConvLSTM model and is more advantageous for training a large number of datasets. We conducted comparison experiments of three models and found that the models based on CNN have better prediction performance in the rice yield prediction problem. Compared with LSTM, CNN utilizes convolution kernel for convolution operation. Preemptive spatial features before processing the data of time series can produce more accurate yield prediction results. However, the single CNN model is not as good as the model combining CNN and LSTM in terms of the final prediction results. This is because the data input to our experiments is a long time series. Moreover, the time interval chosen is the growing period of double-season rice, and the input data have some correlation in time. A single CNN does not handle the data of long time series well. Due to the existence of the feedback structure of LSTM and the use of the gate function mechanism, it deals well with the problem of data gradient explosion. It is more effective in dealing with large number of time series. In our experiments, CNN and LSTM are combined to predict rice yield. It can be found that the hybrid CNN-LSTM model has better prediction performance. The hybrid model uses CNN to pre-extract the spatial features of the input variables and adds LSTM to make up for the deficiency of CNN, which finds it difficult to handle long time series data. Compared with CNN, ConvLSTM also incorporates convolutional structure, but the convolutional effect is not as good as CNN, so the performance in the accuracy of yield prediction is also not as good as CNN. In addition, ConvLSTM has a more homogeneous structure compared with the CNN-LSTM hybrid model. There is a disadvantage when dealing with a large amount of rice feature data. The CNN-LSTM model performs better when processing and predicting data with a large dataset of long time series. Our experimental results also argued this point. In the field of crop yield prediction, the effect of spatial heterogeneity has been less considered in existing studies. Spatial heterogeneity is a field of study in ecology. In the field of ecology, many studies have shown that spatial heterogeneity has an effect on ecological environmental variables and soil variables, among others. Since ecology and soil are very important for crop growth, these effects can indirectly affect crop yield. In his study on the influence of spatial heterogeneity on ecosystem resilience in the Yangtze River basin, [PERSON] found that heterogeneities such as rainfall, topography (elevation, slope) and human activities all have different degrees of influence with ecosystems [50]. China has diverse climate types, and farmland field management practices are diverse and complex in all regions. This resulted in a large spatial variation in soil respiration in agricultural fields at the regional scale. [PERSON] studied this phenomenon. It was found that field management such as tillage, fertilization, drainage and irrigation can directly or indirectly influence the spatial and temporal distribution of crop growth and environmental factors, thus affecting the spatial heterogeneity of soil respiration [51]. [PERSON] proposed the concept of dynamic STR-NVI space based on high spatial and temporal resolution Sentinel-2 images to quantify the spatial heterogeneity of soil moisture and vegetation conditions. The results showed that dynamic STR-NVI space can improve the interpretation of soil moisture spatial heterogeneity at the farm scale and support irrigation and crop growth monitoring efforts in precision agriculture [52].