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Land degradation and multidimensional poverty represent global challenges whose interconnected dynamics are poorly understood and hinder effective interventions. The aim of this study was to address these issues by integrating the RULSE model, the Vegetation Condition Index (VCI), and the Multidimensional Poverty Index (MPI). Using high-resolution geospatial data from Sentinel-2 via the Google Earth Engine, land-use changes were identified as indicators of degradation, with VCI critical for assessing vegetation health. A holistic poverty assessment using MPI data from the Nigerian National Bureau found strong evidence of land degradation in these regions, with approximately 60–75% experiencing multidimensional poverty. Spatial overlap highlights the close connection between severe land degradation and high multidimensional poverty rates. The correlation analysis provided insights into the …