## 3 Results
### Accuracy of CNN-LSTM Hybrid Model  
The percentage error PE was used as a comparison indicator. The first row is the rice production data from 2017 to 2018 in Hubei Province, the second row is the predicted county-level rice production based on the CNN-LSTM hybrid model, and the last row is the county-level error percentage calculated based on the predicted and observed yields. The dark color means low production and percentage, and vice versa.  
In addition to RMSE and PE, we used the coefficient of determination R2 to evaluate the predicted and observed yields, as shown in the scatter plot in Figure 6.  
According to Figure 5, it can be seen that there is high consistency between the predicted production and the statistical data on rice production in Hubei Province. Rice production in Hubei Province is mainly concentrated in the central and eastern parts of the country. Rice production is lower in the northwestern mountainous areas. Most predictions have an error percentage of less than 30%. However, some extremely high forecast errors occur mainly in the northwestern mountainous areas, such as the Shennongjia Forest area, and parts of Yichang and Enshi, which appear bright yellow. The occurrence of these higher prediction errors may be related to the topographic relief and local climatic environment.  
Figure 5: Percentage error map of the forecasts for 2019 for the three models.  
Figure 6: Map of rice production in Hubei Province, projected production and percentage error (PE) from 2017 to 2018.  
Rice growth is influenced by the natural climate. Temperature and precipitation, among others, directly affect the growth and distribution of rice [45]. The spatial and temporal distribution of precipitation in Hubei Province is uneven, with a nearly three-fold difference between the north and south of the province. The Jianghan basin is rich in water. However, the northern part of Hubei Province is more drought-prone. There were consecutive droughts in the winter of 2010 and the spring of 2011, which are rare in history [46]. The Shennongjia forest area is at a higher elevation than the central-eastern Jianghan Plain area, and the climate is very different from that of the plain areas. The area under rice cultivation is relatively small and the rice yield is also at a low level in the province. There are also differences in rice irrigation efficiency in different regions of Hubei Province [46]. In addition to the amount of precipitation that affects rice irrigation efficiency, there are also effects of education level, sown area and population size in different regions on rice irrigation efficiency. Rice irrigation in the Jianghan Plain is more efficient, and the rice cultivation area is more extensive. However, in the northwest and southwest of Hubei Province, rice irrigation is less efficient and rice cultivation is sparse, resulting in lower rice yields. Rice needs to be in the right temperature range to grow properly. If rice is exposed to a high temperature above 35 \({}^{\circ}\)C during the growing period, it will be prone to heat damage [47]. Heat damage in the Jianghan Plain is low and short-lived, and most of the heat damage is mild. There is more heat damage in the Three Gorges area and western Hubei Province and most of this heat damage is severe. These reasons contribute to yield differences and forecast errors between the central-eastern region and the northwestern mountainous region.  
From 2017 to 2019, the R2 shows that the forecasted yield can explain 86%, 88% and 87% of the variance in the observed yield. It is demonstrated that the CNN-LSTM hybrid model can indeed effectively predict county-level rice yield.  
You can see in Figure 7. scatter plots of predicted and observed yield from 2017 to 2019.  
Figure 7: Scatter plots of predicted and observed yield from 2017 to 2019.