## 3 Results and Analyses
### Growth Test Results  
The random forest classification approach was employed to accurately and reliably extract the maize plantation area in Huinong District. To do this, the NDVI difference model was utilized to compare the NDVI values of the year 2021 with those of previous years during the same time (refer to Figure 7). From the beginning of May to the middle \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Extraction Area** & **Statistical Area** & **Absolute Error** & **Relative Error** \\  & **(hm2)** & **(hm2)** & **(hm2)** & **(\%)** \\ \hline
2017 & 8452 & 8426 & 26 & 0.3 \\
2018 & 8689 & 8989 & 300 & 3.3 \\
2019 & 9209 & 9331 & 122 & 1.3 \\
2020 & 9801 & 9573 & 484 & 2.4 \\
2021 & 11,574 & 11,253 & 321 & 2.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of remotely sensed extracted sown area and statistical sown area of corn in Huinong District. Figure 6: Distribution of maize sown area in Huinong District, 2017-2021. **(a–e)** shows the distribution of maize sown area in Huinong District for each year from 2017 to 2021, with the green part of the graph representing maize-growing areas and the white part representing non-maize-growing areas. of August, the study found that the percentage of NDVI differences above 0 was more statistically significant than the percentage of NDVI differences below 0. This finding suggests that the majority of areas within the maize-growing region exhibited higher NDVI values compared to the same period in previous years, indicating improved maize-growing conditions. Conversely, there was minimal disparity observed between the areas with significant NDVI increments above 0 and those with NDVI increments below 0 from mid-August to the end of September. To better track the growth of maize during this time period, the NDVI difference model was used to divide the growth patterns into three groups: worse than previous years, about the same as previous years, and better than previous years. The area allocation for each type of maize cultivation in 2021 was determined based on predetermined values and compared to prior years (Figure 8). The geographical range of each winter wheat variety's growth throughout the same time frame as prior years is delineated and visualized in Figure 9. The findings indicate that the growth of maize during the months of May and July showed improvement compared to the corresponding time in the preceding years. However, from July to September, there was a progressive decline in growth in more favorable regions, while poorer regions had an increase. Nevertheless, the overall growth in the better areas remained higher than that in the poorer areas. Figure 8: Maize growth in the study area in May–September 2021 compared to previous years. Figure 7: NDVI changes in maize-growing areas in 2021 compared to previous years. Some academics have researched the data to indicate that climatic change strongly influences maize development, and the temperature factor (maximum temperature, minimum temperature, and average temperature) significantly influences maize growth, followed by the light component (sunshine hours). Therefore, in this paper, the daily value dataset (V3.0) of China's surface climate data ([[https://data.cma.cn/date](https://data.cma.cn/date)]([https://data.cma.cn/date](https://data.cma.cn/date)), accessed on 22 November 2023) released by the National Meteorological Science Data Sharing Platform, the temperature and sunshine hours meteorological data of one national meteorological station (Huinong) in Shizuishan City from 2017 to 2020 were selected to be statistically collected (Figure 10), and then, according to the statistical data, the growth trend of maize in Huinong District was selected to make an analysis. The comparison reveals that from early May to late July 2021, the temperature was higher than in the same period of the previous year, which was conducive to the growth of maize.