All countries around the world and many international bodies, including the United Nations Development Program (UNDP), United
Nations Food and Agricultural Organization (FAO), the International Fund for Agricultural Development (IFAD) and the International
Labor Organization (ILO), have to eliminate rural poverty. Estimation of regional poverty level is a key issue for making strategies
to eradicate poverty. Most of previous studies on regional poverty evaluations are based on statistics collected typically in administrative
units. This paper has discussed the deficiencies of traditional studies, and attempted to research regional poverty evaluation issues using
3-year DMSP/OLS night-time light satellite imagery. In this study, we adopted 17 socio-economic indexes to establish an integrated poverty
index (IPI) using principal component analysis (PCA), which was proven to provide a good descriptor of poverty levels in 31 regions
at a provincial scale in China. We also explored the relationship between DMSP/OLS night-time average light index and the poverty
index using regression analysis in SPSS and a good positive linear correlation was modelled, with R2 equal to 0.854. We then looked
at provincial poverty problems in China based on this correlation. The research results indicated that the DMSP/OLS night-time light
data can assist analysing provincial poverty evaluation issues.