Combining Three‐Dimensional Building Indicators With <scp>SDGSAT</scp> ‐1 Nighttime Light Data for Poverty Identification—A Case Study in Jiangxi, China

ABSTRACT Poverty is a persistent global challenge. However, relying solely on socioeconomic indicators or low‐resolution nighttime light data hinders the precision of poverty identification. Moreover, the spatial characteristics of buildings, as key indicators of socioeconomic development, remain under‐investigated for their potential in identifying impoverished areas. To bridge these gaps, this study combined high‐resolution (10‐m) SDGSAT‐1 nighttime light with three‐dimensional (3D) building indicators to enable precise poverty identification. The XGBoost algorithm was employed to assess the Multidimensional Poverty Index (MPI) for counties within Jiangxi Province, China. A comparison was also made with conventional NPP‐VIIRS nighttime light (500‐m resolution). The findings indicate that the incorporation of 3D building indicators into SDGSAT‐1 data substantially improves model performance, providing a more robust poverty estimation than single‐source datasets. Specifically, the combined model achieved R 2 values of 0.8986 and 0.8540 for SDGSAT‐1 and NPP‐VIIRS, respectively. This represents an information gain of 0.1160 for the high‐resolution SDGSAT‐1 data compared to its baseline value of 0.7826. Additionally, SHAP analysis elucidates that “mean building height”, “sky view factor”, and “standard deviation of pixel light values” are the predominant drivers of the model. In conclusion, combining 3D building indicators with fine‐grained nighttime light data enables a higher precision in identifying poverty across county‐level units. This approach presents a novel methodology for the timely monitoring of poverty across large regions. These findings provide a robust foundation for optimizing resource allocation and informing sustainable poverty alleviation strategies.