This study aimed to evaluate the potential use of normalized difference vegetation index (NDVI) from satellite-­ derived remote sensing data for monitoring rodent abundance in semi-arid areas of Tanzania. We hypothesized that NDVI could potentially complement rainfall in predicting rodent abundance spatially and tem- porally. NDVI were determined across habitats with differ- ent vegetation types in Isimani landscape, Iringa Region, in the southern highlands of Tanzania. Normalized differ- ences in reflectance between the red (R) (0.636–0.673 mm) and near-infrared (NIR) (0.851–0.879 mm) channels of the electromagnetic spectrum from the Landsat 8 [Opera- tional Land Imager (OLI)] sensor were obtained. Rodents were trapped in a total of 144 randomly selected grids each measuring 100 × 100 m 2 , for which the corresponding values of NDVI were recorded during the corresponding rodent trapping period. Raster analysis was performed by transformation to establish NDVI in study grids over the entire study area. The relationship between NDVI, rodent distribution and abundance both spatially and tempo- rally during the start, mid and end of the dry and wet sea- sons was established. Linear regression model was used to evaluate the relationships between NDVI and rodent abundance across seasons. The Pearson correlation coefficient (r) at p ≤ 0.05 was carried out to describe thedegree of association between actual and NDVI-predicted rodent abundances. The results demonstrated a strong linear relationship between NDVI and actual rodent abundance within grids (R 2 = 0.71). NDVI-predicted rodent abundance showed a strong positive correlation (r = 0.99) with estimated rodent abundance. These results support the hypothesis that NDVI has the potential for predicting rodent population abundance under smallholder farming agro-ecosystems. Hence, NDVI could be used to forecast rodent abundance within a reasonable short period of time when compared with sparse and not widely available rainfall data.h