Application of the Nighttime Light Remote Sensing Data for Urban Mapping in China During 2010-2022: A Comprehensive Review

Using visible-band of night light emissions, it is now possible to observe human activity directly from space. This has made it possible to map urban areas, calculate GDP and population, and track conflicts and natural disasters, among many other things. More recently, night light satellite data from remotely detected night lights have been used to analyse the patterns, dynamics, and environmental effects of urbanization at various spatio- temporal scales. Yet, there is a lack of review papers in the topic on application of nighttime light (NTL) remote sensing for urban mapping and associated topics based on China. Thus, this study focused on two objectives; (i) evaluating the role of NTL data to urban mapping and monitoring, and (ii) exploring the efficiency of NTL remote sensing information for urban area monitoring. The present study shows that NTL data has a positive impact on urban mapping and urban area monitoring, especially in sub urbanization, and urban sprawl in China. The popularity of NTL data can be attributed to the facts that they reflect the socioeconomic status rather than impervious surface information that can be gleaned from optical remote sensing data, as well as the high temporal resolution. Additionally, after integrating NTL data with other optical remote sensing data, accuracy levels during micro level studies in urban areas can be increased. The free accessibility also affected to the usage of NTL data as China is still a developing country in the global south. The research also demonstrates that image calibration and other pre-processing techniques achieve high accuracy (>80%) when used with nighttime images. Different techniques are utilized to map urban areas, including threshold, segmentation, and regression analysis. But in recent times, machine learning techniques like random forest and cellular- automata are also are becoming popular. However, NTL still provide reliable and promising data sources for many applications and have the potential to make a greater contribution to the monitoring of urban areas in China.