## 5 Conclusions  
The present study demonstrates a promising approach for mapping a summer maize cultivated area over a large scale, which considered the phenological differences of maize among the different regions. In practicality, the relationship between summer maize calendars and environmental factors was constructed and environmental factor data were employed to simulate the summer maize phenology in the whole study area. Simulated phenology of summer maize acted as a parameter to obtain a standard EVI time series curve of summer maize. A MAD map was calculated through comparison between time series images of standard summer maize EVI and the actual MODIS-EVI. Appropriate thresholds were set in five different provinces to map spatial distributions of summer maize in the study area. This research has demonstrated that, in terms of precision analysis, the accuracy of the extraction result in each province was above 85%. Statistical data from the Statistical Yearbook were gathered for accuracy evaluation, and the results showed that the R\({}^{2}\) reached 0.81 with an RMSE of 143.8 ha at the city level and the R\({}^{2}\) reached 0.69 with an RMSE of 18.7 ha at the county level. Those indicated a robust potential for identifying areas where the summer maize was cultivated over a large scale. Of course, there are still many challenges in the estimation of the crop planting area while considering the complex agricultural structure, including crop types, management practices, and crop interplanting operation. Further studies can also attempt to use more high-spatial-resolution images to map the crop cultivated area and improve the accuracy of extraction results on a large scale.  
Conceptualization, J.Z. and F.D.; methodology, J.Z., X.W. and F.D.; validation, S.Z. and L.F.; writing--original draft preparation, X.W.; writing--review and editing, J.Z.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Province**} & **Statistical** & **Extracted Area/10\({}^{3}\)** & **High (+) or Low** & \multirow{2}{*}{**Accuracy/\%**} \\  & **Area/10\({}^{3}\) ha** & **ha** & **(-)/\%** & \\ \hline Beijing & 114.5 & 105.1 & \(-\)8.21 & 91.79 \\ Tianjin & 191.7 & 178.2 & \(-\)7.05 & 92.95 \\ Hebei & 2693.6 & 2630.4 & \(-\)2.33 & 97.67 \\ Henan & 3203.3 & 3104.3 & \(-\)3.09 & 96.91 \\ Shandong & 3060.7 & 2707.0 & \(-\)11.56 & 88.44 \\ \hline \hline \end{tabular} Note: High (+) or low (\(-\))\(\%\) = (statistical area-extracted area)/statistical area \(\times\) 100, and accuracy (%) is 100-Abs (high or low).  
\end{table}
Table 7: Comparison of extracted area and statistical data of summer maize for each province in 2013.  
Figure 13: Correlation between the extracted area based on remote sensing and statistical area of summer maize in Huanghuaihai Plain in 2013; **(a)** City level, **(b)**. County level.  
**Funding:** This work was jointly supported by the National Key Research and Development Program of China (No. 2016 YFD0300101, 2016 YFD0300110), the Shandong Key Research and Development Project (No. 2018 GNC110025), \"Taishan\" Scholarship Project of Shandong Province (No. TSXD201712); and the Natural Science Foundation of China (No. 31671585, 41871253).  
**Acknowledgments:** We are very grateful to the editor and four anonymous reviewers for their valuable comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.