Since the release of the digital archives of Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) nighttime light data in 1992, a variety of datasets based on this database have been produced and applied to monitor and analyze human activities and natural phenomena. However, differences among these datasets and how they have been applied may potentially confuse researchers working with these data. In this paper, we review the ways in which data from DMSP/OLS nighttime light images have been applied over the past two decades, focusing on differences in data processing, research trends, and the methods used among the different application areas. Five main datasets extracted from this database have led to many studies in various research areas over the last 20 years, and each dataset has its own strengths and limitations. The number of publications based on this database and the diversity of authors and institutions involved have shown promising growth. In addition, researchers have accumulated vast experience retrieving data on the spatial and temporal dynamics of settlement, demographics, and socioeconomic parameters, which are “hotspot” applications in this field. Researchers continue to develop novel ways to extract more information from the DMSP/OLS database and apply the data to interdisciplinary research topics. We believe that DMSP/OLS nighttime light data will play an important role in monitoring and analyzing human activities and natural phenomena from space in the future, particularly over the long term. A transparent platform that encourages data sharing, communication, and discussion of extraction methods and synthesis activities will benefit researchers as well as public and political stakeholders.

The authenticity and reliability of urbanization levels measured by different indicators in China have not reached a consensus, which may impede our understanding of the process of urbanization and its impacts on the environment. The objective of this study was to describe a reliable method of estimating urbanization level based on the Operational Line-scan System (OLS) on the Defense Meteorological Satellite Program (DMSP) nighttime light data and to analyze the dynamics of urbanization levels in China from 1992 to 2012. We calculated the comprehensive urbanization level at the national, provincial, and county scales using a compounded night light index (CNLI) and compared the change rate of CNLI with those of the other two conventional urbanization level indicators, proportion of the nonagricultural population and proportion of built-up area. Our results showed that CNLI derived from the DMSP/OLS data set provided a relatively reliable and accurate measure of the comprehensive urbanization level in China. During the last two decades, China has experienced continued and rapid urbanization with large regional variations. The CNLI increased 3.12 times, from 1.72 × 10⁻³ to 7.09 × 10⁻³. The annual increases of CNLI in eastern provinces were much faster than those in western provinces. In addition, we found that the rates of change in these three indicators were consistent for most provinces with the exception of the four municipalities (Beijing, Tianjin, Shanghai, and Chongqing) and a few eastern coastal provinces (Jiangsu, Zhejiang, Fujian, and Guangdong). Because the imbalance among population growth, urban expansion and socioeconomic development may affect cities’ sustainable development, we should pay more attention to these regions with large disparities between different indicators.

The urban population (UP) measure is one of the most direct indicators that reflect the urbanization process and the impacts of human activities. The dynamics of UP is of great importance to studying urban economic, social development, and resource utilization. Currently, China lacks long time series UP data with consistent standards and comparability over time. The nighttime light images from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) allow the acquisition of continuous and highly comparable long time series UP information. However, existing studies mainly focus on simulating the total population or population density level based on the nighttime light data. Few studies have focused on simulating the UP in China. Based on three regression models (i.e., linear, power function, and exponential), the present study discusses the relationship between DMSP/OLS nighttime light data and the UP and establishes optimal regression models for simulating the UPs of 339 major cities in China from 1990 to 2010. In addition, the present study evaluated the accuracy of UP and non-agricultural population (NAP) simulations conducted using the same method. The simulation results show that, at the national level, the power function model is the optimal regression model between DMSP/OLS nighttime light data and UP data for 1990–2010. At the provincial scale, the optimal regression model varies among different provinces. The linear regression model is the optimal regression model for more than 60% of the provinces. In addition, the comparison results show that at the national, provincial, and city levels, the fitting results of the UP based on DMSP/OLS nighttime light data are better than those of the NAP. Therefore, DMSP/OLS nighttime light data can be used to effectively retrieve the UP of a large-scale region. In the context of frequent population flows between urban and rural areas in China and difficulty in obtaining accurate UP data, this study provides a timely and effective method for solving this problem.