## 1 Introduction  
Urban areas account for less than 3% of the global land surface, but the intensive human activities happening in urban areas make them the main source of carbon dioxide (CO\({}_{2}\)) emissions in the biosphere [1]. In urban areas, vegetation plays a major role in sequestering CO\({}_{2}\) and reduces local net carbon emissions. On one hand, rapid urban expansion replaces natural vegetation or agricultural crops with artificial surfaces made of concrete, asphalt, or turf grasses, decreasing the photosynthesis of the area. On the other hand, the integration of parks, green wedges, and green belts in the urban area changes the CO\({}_{2}\) sequestrations of cities [2]. As urbanization is expected to increase significantly in the coming decades [3], it is important to understand the impact of rapid land-use/cover change (LUCC) on vegetation primary production in the urban area. The effects of LUCC on the vegetation primary production in the urban area have been evaluated with remote sensing techniques and/or process-based models in several studies. At a regional scale, [PERSON] et al. assessed the impact of urban land development on net primary production (NPP) in the southeastern United States, through a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) data, Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data, and Landsat Enhanced Thematic Mapper Plus (ETM+) images [4]. [PERSON] et al. examined the consequences of urban land transformation in the United States using data from two satellites and a terrestrial model [5]. They found that urbanization had a negative impact on NPP [PERSON] et al. evaluated the differences in NPP between pre-urban and post-urban land development in China using the Carnegie-Ames-Stanford approach (CASA) model [6]. They found that urbanization reduced NPP at an accelerating rate of 0.31 \(\times\) 10\({}^{-3}\) PgC year\({}^{-1}\). At a city/county scale, a study of the NPP in the Phoenix metropolitan region of the United States indicated that urbanization, in an arid environment, may boost productivity by introducing highly productive plant communities and weakening the dependence of plants on natural cycles of water and nutrients [7]. In China, the effects of urban expansion on NPP in Jiangyin County [8], Shenzhen [9], and Guangzhou [10] were evaluated based on satellite data, or the combination of satellite data and an ecosystem model. Although these studies successfully analyzed the impacts of LUCC on the dynamics of vegetation primary production in urban areas using different models and at different scales, three key issues still need to be addressed. First, vegetation primary production in urban areas needs to be estimated at fine spatial and temporal resolutions, considering that land covers in urban areas are highly fragmented and dynamically shaped by environmental processes and socioeconomic drivers. Vegetation primary production was usually estimated at coarse spatial resolution (e.g., 500-2000 m) either by remote sensing-based models or by process-based models [11; 12; 13; 14; 15; 16]. Limited data availabilities and the unaffordable expenses of high-resolution data make it infeasible to estimate primary production with high spatial and temporal resolutions. Data fusion techniques bridge the gap between the spatial and temporal availabilities of current satellite data, and facilitate estimations of vegetation primary production at finer resolutions [17; 18]. For example, the spatial and temporal adaptive reflectance fusion model (STARFM) combines Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data and MODIS Terra data to create a dataset with a 30-m spatial resolution and a daily revisit period [19; 20; 21; 22]. The STARFM-generated data has been applied in various studies, such as phenology analysis [23; 24], forest disturbance mapping [25; 21], and the estimation of gross primary production (GPP) [26].