# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 4 Results
### Yield Estimations Models Built in Jilin and Liaoning Provinces
#### 4.2.3 Determination and Verification of the Final Yield Estimation Model  
Table 2 summarizes the input variables and accuracy levels of models 1 to 4 established by the three methods in the two provinces. Based on the table, we finally chose Yield Model 3 for Jilin Province and Yield Model 4 for Liaoning Province as their best yield estimation models. According to agricultural disaster statistics from 1997 to 2016 in China, Liaoning and Jilin suffered severe agricultural disasters in 1997, 2000, and 2001 (Figure 7). For the disaster year of 1997 in Jilin Province, we rebuilt a yield estimation model with data on the input variables of Yield Model 3 by removing data from 1997. We then estimated the maize yield in 1997 using the newly built model and evaluated the accuracy of the yield estimation for that disaster year. Using the same method, the model performance was also evaluated in the other two disaster years in Jilin Province and the three disaster years in Liaoning Province. The accuracy evaluation is shown in Figure 8. The MARE values for all municipal administrative units in Jilin Province was 0.1379 in 1997, 0.1163 in 2000, and 0.0919 in 2001. The MARE value for all municipal administrative units in Liaoning Province was 0.1508 in 1997, 0.1170 in 2000, and 0.1186 in 2001. The model accuracy of Jilin Province is higher than that of Liaoning Province.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Province** & **Model** &
\begin{tabular}{c} **The Number of** \\ **Input Variables** \\ \end{tabular} & **Input Variables** & **R2** & **CCC** & **MARE** \\ \hline \multirow{8}{*}{Jilin} & \multirow{8}{*}{Yield Model 1} & & SPI2-7, AAT9, GPPa8, AAT8, EDD8, & & & & \\  & & & GPpa7, AAT4-9, SPI2-5, AAT5, GPPa4-9, & & & & \\  & & & GPpa6, EDD6, EDD4-9, GPPa5, AAT7, & 0.7703 & 0.9093 & 0.0933 \\  & & & SPI2-9, EDD7, AAT4, GPPa4, AAT6, & & & & \\  & & & EDD5, GPpa5, EDD9, EDD4 & & & & \\  & & & SPI2-7, AAT9, NDVIa4, NDVIa8, AAT8, & & & & \\  & & & EDD8, AAT4-9, SPI2-5, NDVIa7, AAT5, & & & & \\  & & & EDD6, EDD4-9, NDVIa5, AAT7, SPI2-9, & 0.7680 & 0.9119 & 0.0925 \\  & & & EDD7, AAT4, NDVIa6, AAT6, EDD5, & & & & \\  & & & NDVIa4-9, NDVIa9, EDD9, EDD4 & & & & \\  & Yield Model 3 & 8 & SPI2-7, AAT9, GPPs, EDD8, AAT4-9, & 0.8201 & 0.9761 & 0.0943 \\  & & & SPI2-7, AAT9, EDDs, AAT4-9, & NDVIa8, & 0.8019 & 0.9599 & 0.0961 \\  & & & AAT8, EDD7 & & & & \\ \hline \multirow{8}{*}{Liaoning} & \multirow{8}{*}{Yield Model 2} & & GPR2-7, SPI2-5, SPI2-7, GPP4-9, GPPa8, & & & & \\  & & & AAT7, SPI2-9, GPPa9, EDD5, AAT6, & & & & \\  & & & AAT4, GPPa6, EDD6, AAT5, EDD7, & & & & \\  & & & ANJTA8, SPI2-5, SPI2-7, AAT7, SPI2-9, & & & & \\  & & & NDVIa9, NDVIa5, EDD5, AAT6, AAT4, & & & & \\  & & & EDD6, NDVIa6, AAT5, EDD7, NDVIa4, & & & & \\  & & NDVIa7, AAT4-9 & & & & & \\  & Yield Model 3 & 8 & GPPa7, AAT6, SPI2-7, AAT7, GPPa4-9, & & & & \\  & & & GPpa8, SPI2-9, AAT4 & & & & \\  & & & SDVIa8, SPI2-7, AAT7, AAT4, SPI2-9, & & & & \\  & & & NDVIa9, AAT6 & & & & \\ \hline \end{tabular}
\end{table}
Table 2: The input variables and accuracy of models 1 to 4 established by two methods in two provinces.