###### Abstract  
Urban forests have the potential to sink atmospheric CO\({}_{2}\). With the improvement of coverage of vegetation in urban environments, more attention has been paid to the carbon sequestration potential of the urban forest. However, the high fragmentation of urban forests makes it difficult to evaluate their carbon budget on a regional scale. In this study, the GPP-NIRv relationship model was employed to estimate GPP in Suzhou by MODIS, Landsat-8 and Sentinel-2 remote sensing data, and to further explore what kind of remote images can figure out the spatial-temporal pattern of GPP in urban forests. We found that the total GPP of the terrestrial ecosystem in Suzhou reached 8.43, 8.48, and 9.30 Tg C yr-1 for MODIS, Landsat-8, and Sentinel-2, respectively. Monthly changes of GPP were able to be derived by MODIS and Sentinel-2, with two peaks in April and July. According to Sentinel-2, urban forests accounted for the majority of total GPP, with an average of about 44.63%, which was larger than the results from GPP products with coarser resolutions. Additionally, it is clear from the high-resolution images that the decline of GPP in May was due to human activities such as the rotation of wheat and rice crops and the pruning of urban forests. Our results improve the understanding of the contribution of the urban forest to the carbon budget and highlight the importance of high-resolution remote sensing images for estimating urban carbon assimilation. g g  
Additionally, the coarse spatiotemporal resolution of satellite SIF is generally a limiting factor in the regional estimation of GPP [22; 23]. In recent years, Near-infrared Reflectance of Vegetation (NIRv), which integrates the advantages of NDVI and near-infrared reflectance, has provided a new avenue for GPP estimation [24; 25; 26; 27]. NDVI can intuitively reflect vegetation coverage, which is tightly correlated to vegetation productivity, while near-infrared reflectance can embody the information on vegetative canopy structure and leaf area [24; 28]. Therefore, NIRv takes into consideration both the pigment and structure of vegetation in remote sensing images to avoid the saturation effects of NDVI [29; 30; 31]. Meanwhile, a strong and stable correlation was found between NIRv and GPP in the same ecosystem [21; 23; 32]. In the study of regional and global carbon budgets of terrestrial ecosystems, NIRv has been suggested as the effective substitution for satellite SIF, based on theoretical derivations and radiative transfer simulations, and has gained a great deal of attention in recent years [24; 33]. Urban forest is considered as a large green infrastructure in the urban area, which consists of trees, shrubs and grasses in streets, parks, gardens, alongside rivers and so on [34; 35; 36]. Additionally, urban forest in this study is also defined as the above description. Urban forest usually plays an indispensable role for people living in urban environments, such as reducing air pollution and heat islands effect [37], and increasing biodiversity [38]. As global CO\({}_{2}\) concentrations continue to increase, there is also increasing interest in the carbon storage and sequestration of the urban forest [39; 40; 41]. Vegetation in urban areas is offered for environments with higher temperatures and CO\({}_{2}\) concentrations, highly intensive use of water and pesticides [42; 43], and its photosynthetic activity would play an important role in the carbon cycle of terrestrial ecosystems. However, in some studies, the vegetation productivity in urban areas was set as zero [44]; the role of urban vegetation might be underestimated in the global carbon budget. With the improvement of urban green coverage (e.g., the green coverage in the urban built areas in Jiangsu province increased from 19.5% in 1990 to 42.1% in 2020), more attention should be paid to the effect of the urban forest in the global and regional carbon assimilation [45; 46; 47].