## 5 Conclusions  
The GEE platform was utilized in this study to build features using Sentinel-2 image data, compare the classification accuracy of random forests and support vector machines, and extract the area of maize planted in Huinong District from 2017 to 2021 using the subsequent forest classification method: The Huinong District's planted area for maize was then covered in masks for 2021. The NDVI difference of the covered area was then calculated using the MODIS image, high-frequency growth monitoring of the crop, and monitoring and analysis of the crop's growth in Huinong District in conjunction with meteorological factors. From these analyses, the following conclusions were drawn:  
(1) Using GEE and having access to Sentinel-2 and MODIS image data ensures that there are reliable data and computing resources for finding maize fields and keeping an eye on how they grow quickly. This study employed support vector machine and random forest classification methods for the purpose of classifying and extracting maize planting areas. The findings indicated that support vector machine, when computed usingthe confusion matrix, exhibited an average overall accuracy of 96.6%. Additionally, the average Kappa coefficient was 0.734, the average User Accuracy was 50.2%, and the average Producer Accuracy was 55.9%. The random forest algorithm yielded an average overall accuracy of 98.9%, as determined by the confusion matrix. Additionally, the average Kappa coefficient was found to be 0.966. The average user accuracy and average producer accuracy were observed to be 97.7% and 99.2%, respectively. This suggests that the random forest classification algorithm is good at identifying maize, which makes it a useful tool for distinguishing grown maize and performing statistical analyses in the research area. In general, the year 2020 exhibited the highest level of accuracy in maize identification. The random forest and support vector machine classification algorithms demonstrated high Kappa coefficients. Also, the overall accuracy was higher than 90%, which shows that the identification results and the validation sample plots were very similar. (2) This study combined the advantages of using two remote sensing datasets, Sentinel-2 and MODIS, to effectively track the growth patterns of maize in Huinong District during the year 2021. This ensured a high level of temporal resolution. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance during the seedling stage, nodulation stage, and early stage of staminate pulling and spitting in comparison to previous years. However, as a result of the weather and other factors, its growth rate gradually slowed down in August. The proportion of regions experiencing diminished growth compared to prior years showed a progressive increase; nonetheless, the overall growth of maize in Huinong District in 2021 surpassed that of previous years. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/su152316343/s1](https://www.mdpi.com/article/10.3390/su152316343/s1)]([https://www.mdpi.com/article/10.3390/su152316343/s1](https://www.mdpi.com/article/10.3390/su152316343/s1)). Conceptualization, W.L. ([PERSON]) and X.S. ; Data curation, W.L. ([PERSON]) and Y.F. ; Formal analysis, Q.L. ; Funding acquisition, X.S. and W.L. ([PERSON]); Investigation, W.L. ([PERSON]), Y.F., Q.L. and T.W. ; Methodology, Y.F. ; Project administration, X.S. and W.L. ([PERSON]); Resources, X.S. and W.L. ([PERSON]); Software, Y.F. ; Supervision, X.S. and W.L. ([PERSON]); Validation, W.L. ([PERSON]), T.W. and D.M. ; Writing--original draft, W.L. ([PERSON]); Writing--review and editing, X.S. and W.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (52169010), the National Natural Science Foundation of China (51869023), and National Key Research and Development Program of China (2021 YFD1900600). Not applicable.