## 2 Materials and Methods
### Data Sources and Processing  
Remote sensing data, climate data and land cover data were used in this research to estimate GPP in Suzhou and analyze the importance of urban forests for the regional carbon budget. Three kinds of resolution remote sensing images are used to estimate GPP. Of these, 30 m-Landsat 8 and 10 m-Sentinel 2 surface reflectance datasets were downloaded from the United States Geological Survey ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 10 April 2022)) and the Copernicus Open Access Hub ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)) (accessed on 15 April 2022)) in 2021 to calculate NIRv. In order to avoid the impact of cloud on data quality, the maximum value composite (MVC) method was set to generate the monthly NIRv.  
MODIS 500 m surface reflectance data (MOD09A1) and NDVI data (MOD13A1) downloaded from NASA's Distributed Active Archive Center (DAAC) have eliminated the  
Figure 1: The geographic location (**a**) and land cover (**b**) derived from Sentinel-2 by ESRI of Suzhou in Jiangsu province in China.  
impact of cloud. In order to maintain the consistency of the production of NIRv, the MVC method was also used to generate the MODIS monthly NIRv.  
Climate data, including monthly temperature and precipitation, were used to investigate GPP changes in response to the local climate; climate data were obtained from the National Meteorological Science Data Center ([[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/)) (accessed on 2 May 2022)).  
MODIS, Landsat and Sentinel-2 land cover datasets were downloaded to summarize the GPP of different land cover types according to the corresponding estimation of GPP with the same spatial resolution. A MODIS land cover data (MCD12Q1) product was downloaded from NASA DAAC. The MCD12Q1 product supplies maps of land cover at annual steps, and the land cover in Suzhou was classified according to the International Geosphere Biosphere Programme (IGBP) land cover classification scheme as urban and built-up lands, croplands, grasslands, water bodies, evergreen broadleaf forests (EBF), deciduous broadleaf forest (DBF), mixed forest (MF) and so on.  
Global land cover data producing 30 m resolution were downloaded from [[http://data.ess.tsinghua.edu.cn/](http://data.ess.tsinghua.edu.cn/)]([http://data.ess.tsinghua.edu.cn/](http://data.ess.tsinghua.edu.cn/)) (accessed on 17 April 2022). This product used the amount of training samples across the world to optimize many kinds of classifiers, eg. maximum likelihood, decision tree, random forest, etc. A unique land-cover classification system was used in this product. In Suzhou, the typical land cover types were crop, forest, grass, shrub, water, and impervious. Additionally, impervious was considered as urban built-up areas in this study.  
ESRI generated a global map of land use and land cover ([[https://livingatlas.arcgis.com/landcover/](https://livingatlas.arcgis.com/landcover/)]([https://livingatlas.arcgis.com/landcover/](https://livingatlas.arcgis.com/landcover/)) (accessed on 17 April 2022)) derived from Sentinel-2 imagery at 10 m resolution by using a deep learning AI land classification model trained by billions of human-labeled image pixels [48]. This product has 9 classes, of which water, tree, crop, shrub land, built area and grass are the dominant ones in Suzhou.  
In order to better calculate the changes in carbon assimilation of urban forest, the urban area of Suzhou was divided into built area (black areas in Figure 1b) and non-built area (remaining areas besides built area).