## 2 Literature Reveiew  
The remote sensing technology of night-time light which is an indirect manifestation of human activities is widely used for the study of population dynamic distribution [6; 7; 8; 9], urban scale evolution [10; 11; 12; 13; 14; 15; 16; 17; 18; 19], socioeconomic research [20; 21], energy consumption estimation [22; 23; 24], and various other fields. Today, there are two main types of night light data used worldwide, namely, the DMSP/OLS Night-time light data and the NPP-VIIRS Night-time light data. Night-time light data has been widely used in studies reflecting human activities and can be used as an ideal data source for obtaining spatio-temporal information in conflict areas. The level of violent conflict and quality of life in Baghdad, Iraq, was studied by using DMSP/OLS data [25]. The DMSP/OLS data has been used to estimate Sri Lanka's GDP and electricity consumption, as affected by the civil war [26]. The impact of war in Russia and the Caucasus, e.g., in countries such as Georgia, has also been studied and monitored with DMSP/OLS data, and it has been verified that the use of DMSP/OLS data can reflect long-term burning of fires and large-scale population movements [27]. The application value of night-time light data, such as DMSP/OLS images in the research of humanitarian crises, for instance, in Syria, has been verified, and the causes of large losses of urban lights can be speculated upon [28]. Compared with DMSP/OLS data, the NPP/VIIRS data has more advantages in terms of spatial resolution, and studies have shown that the observation accuracy of human activities based on NPP/VIIRS data is higher than the former [29; 30]. The feasibility of using NPP-VIIRS data in three areas of human activity that have a large impact on society: light pollution, gas flaring, and armed conflict is verified by analyzing the VIIRS data for a long time [31]. Estimates of power shortages and their affected populations with VNP46A2 data during the Ukrainian-Russian conflict have been made [32]. Like other satellites data, NPP-VIIRS has the problem of cloud overcover [33]. The application performance of NPP-VIIRS can be improved by combining it with other multi-source remote sensing data. VIIRS was combined with multi-source data to estimate the total area of fires caused by war in the first month after the outbreak of the conflict between Russia and Ukraine [34]. Differences in spatial and radiative properties between DMSP-OLS and NPP-VIIRS make it difficult to perform time-consistent analyses using both datasets. A cross sensor calibration model of DMSP-OLS and NPP VIIRS nottilment images has been established [35]. The NPP-VIIRS data was used to simulate DMSP-OLS data to study the urbanization process in Southeast Asia [36]. Simulated DMSP/OLS images with NPP/VIIRS images were also used to assess the dynamics of urban lights in Syria during the civil war [37]. Furthermore, by correcting NPP-VIIRS night-time light using DMSP-OLS, it has been shown that war has darkened Yemen, providing support for international humanitarian aid organizations [38]. In this study, stable night-time light data from the built-up areas of Ukrainian cities are extracted through statistical methods, and data from each city is counted as a sub-area in order to obtain the sum of the night-time light intensity of each city. The night-time light ratio index is obtained by comparing the sum of the night-time light intensity during the war with the sum of the stable night-time lights before the war, so as to reflect the spatial and temporal changes in the relative night-time light intensity affected by the conflict [39]. The NLRI has different meanings at different scales. When the scale is large, NLRI can reflect the trend of human activities in an entire country. As the scale shrinks, the changes to the NLRI are more sensitive, and regional events show continuous directional changes. Such directional changes can be used to reflect the trends of a conflict.