The Advances, Challenges and Perspectives in the Correction Field of Free Night Light Satellite Image

Remote sensing provides a powerful data source for the mapping of urban areas and the monitoring of urban dynamics on a range of scales. Among the variues types of  remote sensing data, images captured at night offer an effective means ofmonitoring human activities on a global scale. The distinctive features and capabilities of these images permit the separation of urban areas and other human activities, the main feature of which is the use of light at night by accurately measuring the location, from the background without light. Via providing uninterrupted and continuous monitoring from the night world perspective, these images provide a valuable source of information about human activities over time from the past to the present. The time series analysis of this data is highly valuable for discovering, estimating and monitoring social and economic dynamics in countries, especially sub-regions where there are no official statistics. With recent developments in night-time data satellite sensors and new research conducted in this field, this study aims to review the advances in night-time sensors, introduce the existing data and products, review and express the advantages and disadvantages of each one, and review the methods and solutions presented in previous research for solving the existing problems and limitations in order to improve these images.

The main objective of this research is to introduce and review the general charactristics of night-time light data, discussing their advantages, challenges, and methods for addressing these challenges. The majority of studies on DMSP night light images focus on two spatial and temporal dimensions. In the spatial dimension, inherent deficiencies of this dataset are observed, such as saturated numerical values in central urban areas and flourishing effects in suburban and rural areas. In the temporal dimension, the lack of calibration in the processor, necessitates the implementation of additional processes on annual products of stable DMSP night light data in order to examine urban dynamics. The existing methods for correcting spatial problems are divided into tow categories: spectral and non-spectral. Similarly, methods for addressing temporal issues are divided into two categories: annual calibration of night light data and adjustment of temporal patterns. NPP-VIIRS monthly images encompass various features including fixed light values such as city lights and transportation routes, as well as noise values such as gas flames, biomass burning, and background noise. Therefore, preprocessing is necessary before utilizing this data. Furthermore, the positioning accuracy of Loujia_01 data is lower than its spatial resolution, resulting in image displacement of up to 650 meters in some areas. Geometric correction is applied to rectify this issue, and various correction methods have been investigated.

A general comparison of the data sets reveals that, despite the existing problems and limitations, the DMSP stable night light data outperforms other night light datasets due to its longer time series, which spans from 1992 to 2013. This extended temporal coverage makes it a valuable resource for research on urban dynamics and estimating the overall growth trend of cities. On the other hand, NPP-VIIRS offers advantages and is sensitive to faint light sources. However, its passage time at 1:30 in the morning, when many lights are turned off, limits its utility for urban studies. Consequently, it may not be the optimal choice for exclusively investigating urban areas exclusively. Nevertheless, the NPP-VIIRS data is more useful in research related to economic activities. Furthermore, the sensor's lack of sensitivity to blue light emitted by LEDs impacts its ability to accurately quantify artificial light emissions from the ground.

The objective of this study was to introduce types of remote sensing night light data and their analysis. In short, current research in the field of correcting spatial saturation and blooming problems is divided into two categories: spectral and non-spectral. Non-spectral methods typically rely solely on night light data, although they may also incorporate non-remote sensing data. Spectral methods often employ spectral indices that are related to vegetation and ground surface temperature. Currently, correcting DMSP images from the temporal dimension can be achieved through inter-data calibration, specifically via the fixed reference regions or reference pixels method. One of the most reliable methods in this field is the reference area method. Following the conclusion of the DMSP-OLS mission, the VIIRS was introduced. In contrast to the annual data of this satellite, the monthly data requires correction due to the presence of background noise, and stray lights. A reviews of existing studies indicates that the majority of methods aim to remove noise using specific frameworks although with differing assumptions. Finally, considering the current challenges and limitations of night light satellites, several recommendations for future progress and development in this field are put forth. Further investigation could be conducted into the integration of DMSP-OLS data with NPP-VIIRS data or higher resolution Loujia-01 data, with the objective of developing a longer time series for future research on urban dynamics.