Poverty　and　inequality　remain　outstanding　challenges　in　many　global　regions.　Understanding　the　underlying　social　and　economic　conditions　is　important　in　formulating　poverty　eradication　strategies.　Using　Visible　Infrared　Imaging　Radiometer　Suite　(VIIRS)　Night-Time　Light　(NTL)　images　and　multidimensional　socioeconomic　data　between　2012　and　2018,　this　study　measured　regional　poverty　and　inequality　in　the　Xiamen-Zhangzhou-Quanzhou　city　cluster　in　the　People＇s　Republic　of　China.　Principal　Component　Analysis　(PCA)　and　the　Theil　index　decomposition　method　were　used　to　establish　an　Integrated　Poverty　Index　(IPI)　and　a　regional　inequality　index,　respectively.　The　results　indicated　that:　(1)　The　poverty　index　is　affected　by　the　geographical　location,　policies,　and　resources　of　a　district/county.　A　significant　logarithmic　correlation　model　between　VIIRS　Average　Light　Index　(ALI)　and　IPI　was　established.　(2)　The　Theil　index　derived　from　Gross　Domestic　Product　(GDP)　indicators　showed　that　overall　inequality　and　between-prefecture　inequality　declined,　while　within-prefecture　inequality　remained　unchanged.　In　terms　of　the　contributions　to　regional　inequality,　the　contribution　of　within-prefecture　inequality　is　the　largest.　The　results　indicated　that　Suomi　National　Polar　Partnership/Visible　Infrared　Imaging　Radiometer　Suite　(NPP/VIIRS)　night-time　data　can　help　to　perform　district/county-level　poverty　assessments　at　small　and　medium　spatial　scales,　although　the　evaluation　effect　on　regional　inequality　is　slightly　lower.