The　coronavirus　disease　2019　(COVID-19)　has　caused　significant　changes　in　urban　networks　due　to　epidemic　prevention　policies　(e.g.,　social　distancing　strategies)　and　personal　concerns.　Previous　measurements　of　urban　networks　were　mainly　based　on　flow　data　or　were　simulated　from　statistical　data　using　models　(e.g.,　Gravity　model).　However,　these　measurements　are　not　directly　applicable　to　the　mapping　of　directional　urban　networks　during　unexpected　events,　such　as　COVID-19.　Since　nighttime　light　(NTL)　data　offer　a　unique　opportunity　to　track　near　real-time　human　activities,　the　radiation　model,　traditionally　used　for　routine　situations　only,　was　modified　to　measure　directional　urban　networks　using　NTL　data　under　three　scenarios:　the　routine　scenario　(before　the　Shanghai　lockdown),　the　COVID-19　scenario　(during　the　Shanghai　lockdown),　and　the　extreme　scenario　(without　Shanghai＇s　participation).　When　compared　with　the　Baidu　migration　index,　the　modified　radiation　model　achieved　an　acceptable　accuracy　of　0.74　under　the　routine　scenario　and　0.44　under　the　COVID-19　scenario.　Our　mapping　of　each　scenario＇s　urban　networks　in　the　Yangtze　River　Delta　Region　(YRDR)　shows　that　the　Shanghai　lockdown　reduced　the　urban　interaction　index　between　Shanghai　and　its　surrounding　cities.　However,　it　led　to　an　increase　in　the　urban　interaction　index　centered　on　the　periphery　cities　of　YRDR.　Our　findings　suggest　that　urban　interactions　within　YRDR　are　resilient,　even　under　extreme　scenarios.　Considering　the　long　time　series　and　global　coverage　of　NTL　data,　the　proposed　NTL-based　urban　network　model　can　be　readily　updated　and　applied　to　other　regions.　©　2023　Elsevier　Ltd