Temporal expansion of the nighttime light images of SDGSAT-1 satellite in illuminating ground object extraction by joint observation of NPP-VIIRS and sentinel-2A images

Abstract

Poverty is the leading cause of social instability around the world. Reducing poverty has become a crucial objective for global sustainable development. Timely and consistently monitoring poverty status plays a vital role in evaluating the efficiency of poverty reduction policies. Nighttime light images from satellites allow for frequent monitoring of poverty status by recording lighting intensity. The coarse spatial resolution of the recent, widely used, publicly available nighttime light images hinders research on smaller unit scales, such as county-level. The SDGSAT-1 satellite, with a glimmer sensor and a spatial resolution of 10-m, was launched in 2021 to meet the sustainable development goals. This study proposes a model to sharpen the existing nighttime light images with a coarse spatial resolution to expand the available period of nighttime light images with a 10-m spatial resolution in discriminating illuminating ground objects. The expanded nighttime light images with discriminated illuminating ground objects are then utilized to calculate the time-series county-level poverty index for three typical Chinese urban agglomerations with a statistically significant correlation coefficient of 0.675 (p < 0.01). Compared to the poverty statistics derived from the Chinese county statistical yearbook, the estimated poverty index can explore county-level poverty status change more effectively by completing the missing census. The time-series county-level poverty analysis demonstrates the effectiveness of Chinese endeavor in poverty reduction. >90% of the counties experienced a reduction in poverty in Beijing–Tianjin–Hebei region due to time-series economic development. Statistical analysis of the poverty index of impoverished counties demonstrates a remarkable reduction of poverty in the study areas and the significance of the persistent poverty reduction policy implementation. This study's proposed pipeline compensates for the lack of nighttime light images with a higher spatial resolution prior to 2021 and provides a reliable poverty analysis foundation for sustainable development and policymaking.