# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 4 Results
### Comparison of the Importance of Different Variables  
Overall, the contribution of the agrometeorological indexes to improving the modeling accuracy in the two provinces was greater than that of the remote sensing parameters (Figure 10). The contribution of the temperature indexes (AAT variables and EDD variables) to improving the modeling accuracy in the two provinces was greater than that of the SPI variables (Figure 10). The contribution of the GPPa and NDVIIa variables to the modeling accuracy in the two provinces was inconsistent. In Jilin Province, the GPPa variables performed  
Figure 9: Correlation coefficients of the candidate input variables in Jilin (**a**) and Liaoning (**b**) Provinces.  
better than the NDVIia variables, but in Liaoning Province, the opposite was true (Figure 10). In general, under the same method, the modeling accuracy difference between the GPP variable group and the NDVI variable group was not large, the R\({}^{2}\) difference was within 0.04, the CCC difference was within 0.04, and the MARE difference was within 0.02 (Table 1). NDVI is a basic remote sensing variable, which can be calculated only by using the reflectance of near infrared and red bands. However, the GPP calculation process is complex and requires a large amount of input data; the calculation itself introduces a certain amount of error [71]. Therefore, we recommend the use of NDVI in the establishment of a yield estimation model.  
There were 31 candidate variables in this study. For a given candidate variable in each province, we counted the cumulative number of times that it was selected as one of the input variables for building models 1-4. The maximum cumulative number of times was four for the ATT, EDD, and SPI variables, while the maximum cumulative number of times was two for the GPPa and NDVIa variables. The results are shown in Figure 11. From the figure, we can see that EDD7, AAT4-9, EDD8, AAT8, AAT9, and SPI2-7 were the most important variables in Jilin Province. ATT4, ATT6, ATT7, SPI2-9, and SPI2-7 were the most important variables in Liaoning Province. Among the 11 most important variables in two provinces, the ATT variables accounted for 6, constituting for the highest proportion (54.54%). SPI2-7 was used as the input variable in all models, which indicates that drought in the middle period of maize growth (June and July) has a great impact on maize yield in both Jilin and Liaoning Provinces. From June to July, maize is in the transitional period between three leaves and silking, and its water demand is high [72; 73; 74]. The critical period of water supply lasts from 10 days before tasseling to 20 days after tasseling. The ear differentiation and flowering stages are sensitive to water deficiencies [75]. Drought conditions lasting for more than half a month will cause \"neck drought\" in maize, which results in poor development of young ears, small ears, and few grains. In addition, GPPa and NDVIa in August were important modeling variables for both provinces. This is probably because the GPP and NDVI values usually peak in August. A large number of studies have shown that the peak NDVI value is highly correlated with crop yield, so it is often selected as an input variable for remote sensing statistical yield estimation models [76; 77]. NDVIa (GPPa) represents NDVI (GPP) anomaly; thus, the NDVIa observed in August can well reflect the yield fluctuation [69; 78; 79].  
Figure 10: The resulting modeling accuracy using only the technical yield and by adding different variable groups on the basis of the technical yield in Jilin (**a**) and Liaoning (**b**) Provinces.