## 5 Conclusions  
In this study, MODIS, Landsat-8 and Sentinel-2 images were used to generate regional GPP in one of China's most economically developed cities, Suzhou, to identify the applicability of different kinds of remote sensing data on urban scale studies and the characteristics of urban carbon budgets. The results of GPP estimated by MODIS, Landsat-8, and Sentinel-2 images were 8.43, 8.48, and 9.30 Tg C yr\({}^{-1}\), respectively. The monthly dynamic of GPP exhibited two peaks in April and September. In May, the harvest of wheat and the pruning process conducted on urban forests made a pronounced decline in total GPP by about 25.93% according to Sentinel-2. Accordingly, climate factors as well as anthropogenic factors contribute to the change of urban GPP. As the spatial resolution rose, the contribution of the urban forest to regional total GPP increased, reaching about 44.63% according to the 10 m sentinel-2 images. Since the distribution of urban forests is highly fragmental,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Veg. Type** & **Non-Built Area (g C m\({}^{-2}\) yr\({}^{-1}\))** & **Urban Forest (g C m\({}^{-2}\) yr\({}^{-1}\))** \\ \hline GRA & 2275.9 \(\pm\) 949.8 & 841.5 \(\pm\) 599.2 \\ DBF & 2500.9 \(\pm\) 749.0 & 1589.7 \(\pm\) 722.0 \\ MF & 2306.1 \(\pm\) 605.6 & 1478.4 \(\pm\) 624.4 \\ EBF & 3206.6 \(\pm\) 507.4 & 2640.6 \(\pm\) 478.8 \\ \hline \hline \end{tabular} Note: GRA, DBF, MF, and EBF are grassland, deciduous broadleaf forest, mixed forest, and evergreen broadleaf forest, respectively. Urban forest is the GRA, DBF, MF, and EBF in the built area.  
\end{table}
Table 3: Differences in yearly GPP between vegetation in non-built areas and built area.  
high-resolution remote sensing images can better figure out the dynamic changes in the GPP of the urban forest. The results of our study demonstrate the importance of using high-resolution remote sensing images for estimating the GPP of the urban forest and for improving our understanding of the urban carbon budget.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15010071/s1](https://www.mdpi.com/article/10.3390/rs15010071/s1)]([https://www.mdpi.com/article/10.3390/rs15010071/s1](https://www.mdpi.com/article/10.3390/rs15010071/s1)), Table S1. The relationship between GPP and NIRv.  
Conceptualization, X.L., Q.H. and Q.Z., methodology, X.L. and Q.H., software, F.C.; validation, Q.H. and X.L.; formal analysis, X.L., Q.H., H.Z. and Q.Z.; resources, X.L. and Q.H.; data curation, F.C. and X.L.; writing--original draft preparation, Q.H. and X.L.; writing--review and editing, Q.H., X.L., Q.Z. and H.Z.; visualization, X.L., funding acquisition, Q.H. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (Grant no. 41871334, Grant number. 42071392), the Natural Science Foundation of Nanjing Xiaozhuang University (Grant number. 2020 NXY15), and the Suzhou Agricultural Science and Technology Innovation project (Grant no. SNG2020072).  
The research does not involve this issue.  
We really appreciate the editors and anonymous reviewers for their meaningful comments for improving our manuscript.  
The authors declare no conflict of interest.