# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023  
[PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (WL. ); [EMAIL_ADDRESS] (Z.N. )1  
[PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (WL. ); [EMAIL_ADDRESS] (Z.N. )1  
[PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (WL. ); [EMAIL_ADDRESS] (Z.N. )1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/1000](https://www.mdpi.com/journals/pdf/1000)]([https://www.mdpi.com/journals/pdf/1000](https://www.mdpi.com/journals/pdf/1000))  
Therefore, it is difficult for this statistical method to objectively and comprehensively reflect the changes in social and economic activities [2]. NTL is a spatial data closely related to human socio-economic activities. It can realize the observation of global NTL intensity without distinction, and provide new ideas for socio-economic assessment at multiple spatiotemporal scales [3; 4; 5]. NTL comes from a wide range of sources, such as DMSP-OLS, NPP-VIIRS, Luojia-1, SDGSAT-1, and other satellites/sensors. The temporal and spatial resolutions and the stage of data collection are different. At present, NTL has been widely used in many fields such as urban monitoring [6; 7; 8; 9], socio-economic estimation [10; 11; 12; 13], and disaster assessment [14; 15; 16; 17; 18]. In recent years, there have been many research results on GDP estimation using NTL. For example, [PERSON] et al. spatialized the GDP of Henan Province, China, and analyzed the economic center distribution and development direction of economic connection elements at the provincial and municipal levels [19]; [PERSON] et al. combined multiple data, used multivariate linear regression and random forest algorithms to estimate the GDP of Ningbo City, China, and, following a comparison, concluded that the township estimates were more accurate than county-level estimates [20]; [PERSON] et al. combined the NPP/VIIRS datasets and Sentinel-2 images to spatialize and analyze the GDP of the secondary and tertiary industries in Zibo City, China, and obtained the GDP change pattern of the city [21]; [PERSON] et al. used the United States as the research area and found that the NPP/VIIRS data predicted the GDP of metropolitan statistical areas better than the GDP prediction results of each state, indicating that NTL is more closely related to cities than to rural areas [22]; [PERSON] et al. proposed a learning framework that relies on a convolutional neural network algorithm to use NTL to estimate the county-level GDP in mainland China [23]; [PERSON] et al. calculated the GDP using DMSP/OLS data and found that, between 2000 and 2012, Panama's official GDP was 19% higher than the GDP produced by nighttime lights [24]; and [PERSON] et al. combined NPP/VIIRS data and population data from the Global Human Settlements to propose a new enhanced light intensity model to capture the spatial heterogeneity in economic activities [25]. Compared with traditional GDP statistics, NTL data have obvious advantages in assessing social and economic indicators: 1 they can objectively obtain global NTL data without being affected by administrative boundaries; 2 the spatial details within the data are rich and can reflect the internal differences of a certain region [26]; and 3 the periodicity of obtaining data is short and can provide timely support for government decision-making [27]. The quality of NTL remote sensing data is an important factor affecting GDP estimation. The NPP/VIIRS second edition annual NTL data are generated by the average monthly cloud-free radiation, and the data threshold range is based on the multi-year maximum median and multi-year percentage cloud cover grid.