# Spatiotemporal Dynamics of Net Primary Productivity in China's Urban Lands during 1982-2015
## 1 Introduction  
More than 50% of the world's total population lives in urban areas. According to the World Population Prospects revised by the United Nations in 2017, the world's urban population will grow to approximately 8.5 billion by 2030 [1]. Population growth and rapid economic development are important factors contributing to the urbanization process, which has become a common phenomenon around the world [2, 3, 4]. Previous studies have demonstrated that Asia and North America have the most newly urbanized areas. Europe has already been highly urbanized, and the increase in urban area there is the smallest [5].  
Urban expansion in China has been most obvious from 2000 to 2010, with the expansion area accounting for approximately 65% of total urban land growth in Asia and 28% of global urban expansion [5].  
The rapid urbanization process has led to a series of problems, such as the increased frequency of heatwave events caused by the urban heat island effect [6], a decline in water quality, and an increase in greenhouse gas emissions [2; 7; 8]. Urban expansion affects the global carbon cycle through the conversion of high-productivity forests or croplands into urban lands, which reduces the carbon sequestration capacity of soils and vegetation and poses a threat to natural resources and food security [3; 9; 10; 11]. Therefore, vegetation plays an irreplaceable role in the human living environment within urban areas [4]. Urban vegetation improves air quality, increases terrestrial carbon sinks, and provides huge psychological and socioeconomic benefits for urban residents [12; 13; 14]. As an important parameter for monitoring vegetation growth and ecosystem health, net primary productivity (NPP) expresses the amount of carbon fixed by plant communities through photosynthesis per unit of time and space and is of considerable significance to the global terrestrial biosphere carbon cycle [15; 16]. Based on long-term NPP data, we can effectively monitor long-term dynamic variations in vegetation in urban areas and assess the impact of urban expansion on the environment and food security [5; 9; 11].  
To assess the growth of vegetation within an urban area, some studies have focused on the impact of urban expansion on NPP in China at different spatiotemporal scales. [PERSON] and [PERSON] [17] studied the reduction of NPP in China from 1989 to 2000 due to urban expansion. [PERSON], et al. [9] analyzed the pressure of urban expansion on food security based on the impact of urban expansion on cropland NPP in China from 1992 to 2015. Additionally, there have been previous studies on typical urban agglomerations in China. For example, [PERSON], et al. [11] evaluated the impact of urban expansion on NPP in the Pearl River Delta from 2001 to 2009. However, most studies do not examine long-term data sequences from the 1980s to the present. Since the reform and opening of China in 1978, the economy and urban areas have grown rapidly. Monitoring the complete time series of urban NPP in China or Chinese regions can reflect the impact of urban development on vegetation since China's reform and opening. In addition, most previous studies have used China or a certain region as a research area. However, the NPP differences among different urban agglomerations and whether certain meteorological variables promote urban NPP are still worth discussing. These are of great significance for prediction of future urban differences and for policy formulation.  
To assess and monitor long time series of vegetative growth in ecological environments in urban areas, we (1) analyzed the NPP variations within China's urban areas from 1982 to 2015 based on the urban lands in the 1980s and 2015 and, further (2) separately analyzed the NPP variations in different typical urban agglomerations and their surrounding areas. Finally, (3) we evaluated the importance and contribution of different variables to NPP in these urban agglomerations and surrounding areas.