Multi-grained estimation of nighttime light dynamics during the COVID-19 surge in Shanghai with SDGSAT-1 GIU imagery and point of interest data

Nighttime light (NTL) imagery remotely sensed from outer space has been suggested to be a suitable proxy to investigate socioeconomic dynamics. Since the outbreak of COVID-19, many studies have used NTL imagery to estimate the impacts of the pandemic. However, finer-grained analytics are rarely achieved limited by the spatial resolution of major NTL data sources. In November, 2021, the Sustainable Development Science Satellite-1 (SDGSAT-1) was launched and one of its payloads, Glimmer Imager for Urbanization (GIU) can provide 10m/40 m panchromatic and multispectral NTL images for public use. In this study, we estimate the fine-grained NTL dynamics before and after the COVID-19 surge in the city of Shanghai during the second quarter of 2022 using SDGSAT-1 GIU nighttime imagery. To distinguish the different behaviors among urban functional entities, categorized Point of Interest (POI) data are adopted. The estimation is conducted in three progressive levels: city-level, POI-class-level, and POI-object-level. To characterize each urban objects from multiple angles, two additional NTL indices, NTL and luminous area ratio composite index (NTL-CI) and NTL background relative activeness index (NTL-AI) are introduced and estimated. On the basis of raw NTL, NTL-CI further considers the change of luminous area and NTL-AI further considers the relative change to the average standard. Moreover, detailed visual observations at typical POI objects are conducted, for instance, the Shanghai Disney Resort, the Shanghai Tesla Gigafactory, and multiple cabin hospitals temporarily converted from large stadiums and exhibition centers. This study aims to present a comprehensive investigation of the socioeconomic influence of COVID-19 in Shanghai from the perspective of NTL changes in multiple granularities, and the utility of SDGSAT-1 GIU nighttime imagery in supporting SDG 3 (The Sustainable Development Goals 3: Good Health and Well-Being) is also demonstrated with this set of quantitative analytics.